From 3d5552da3403b9720f6fe3334fbe87081cc5397e Mon Sep 17 00:00:00 2001 From: ibrahim-shehzad Date: Tue, 25 Nov 2025 15:06:01 -0500 Subject: [PATCH 01/28] correct typo in formula for gamma_circ --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 57b3d2c83f4..b5816025549 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -1027,7 +1027,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "6e1aa95c-fda5-4dff-a17a-fb872ed6e5a1", "metadata": {}, "outputs": [ @@ -1052,7 +1052,7 @@ " eplg = 1 - lf ** (1 / (n - 1)) # error per layered gate (EPLG)\n", " fid_cx = 1 - eplg\n", " gamma_cx = 1 / fid_cx**2\n", - " gamma_circ = gamma_cx * num_2q_ops\n", + " gamma_circ = gamma_cx ** num_2q_ops\n", "\n", "cvar_aggregation = 1 / np.sqrt(gamma_circ)\n", "print(\"\")\n", From 083753731fcf7ed1f92ea1dc5bae5e58627f4c63 Mon Sep 17 00:00:00 2001 From: Ibrahim Shehzad Date: Tue, 25 Nov 2025 15:38:34 -0500 Subject: [PATCH 02/28] add typo-correct adv-qaoa tutorial, run on Heron --- .../advanced-techniques-for-qaoa.ipynb | 2664 +++++++++-------- 1 file changed, 1342 insertions(+), 1322 deletions(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index b5816025549..9fa6a2d8d54 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -1,1349 +1,1369 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "f5ebeac7-51ad-438b-8c0a-260810947fcb", - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "source": [ - "{/* cspell:ignore elist, rarr, qidx, cidx, rbrack, cvar, setminus */}" - ] - }, - { - "cell_type": "markdown", - "id": "21b8bd8e-d52d-40c8-b3cd-72935ad47a09", - "metadata": {}, - "source": [ - "# Advanced techniques for QAOA\n", - "*Usage estimate: 5 minutes on an Eagle r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*" - ] - }, - { - "cell_type": "markdown", - "id": "0c7f710b-fb7f-494e-ad85-790b02d4556c", - "metadata": {}, - "source": [ - "## Background\n", - "\n", - "This notebook introduces advanced techniques to improve the performance of the **Quantum Approximate Optimization Algorithm (QAOA)** with a large number of qubits.\n", - "See [Solve utility-scale quantum optimization problems](/docs/tutorials/quantum-approximate-optimization-algorithm) for an introduction to QAOA.\n", - "\n", - "The advanced techniques in this notebook include:\n", - "\n", - "- **SWAP strategy with SAT initial mapping**: This is a specifically designed transpiler pass for QAOA that uses a SWAP strategy and a SAT solver together to improve the selection of which physical qubits on the QPU to use. The SWAP strategy exploits the commutativity of the QAOA operators to reorder gates so that layers of SWAP gates can be simultaneously executed, thus reducing the depth of the circuit [\\[1\\]](#references). The SAT solver is used to find an initial mapping that minimizes the number of SWAP operations needed to map the qubits in the circuit to the physical qubits on the device [\\[2\\]](#references) .\n", - "- **CVaR cost function**: Typically the expected value of the cost Hamiltonian is used as the cost function for QAOA, but as was shown in [\\[3\\]](#references) , focusing on the tail of the distribution, rather than the expected value, can improve the performance of QAOA for combinatorial optimization problems. The CVaR accomplishes this. For a given set of shots with corresponding objective values of the considered optimization problem, the Conditional Value at Risk (CVaR) with confidence level $\\alpha \\in [0, 1]$ is defined as the average of the $\\alpha$ best shots [\\[3\\]](#references).\n", - "Thus, $\\alpha = 1$ corresponds to the standard expected value, while $\\alpha=0$ corresponds to the minimum of the given shots, and $\\alpha \\in (0, 1)$ is a tradeoff between focusing on better shots, while still applying some averaging to smooth out the optimization landscape. Additionally, the CVaR can be used as an error mitigation technique to improve the quality of the objective value estimation [\\[4\\]](#references)." - ] - }, - { - "cell_type": "markdown", - "id": "1f7cd99b-a5a9-4b6b-95e3-9d950d93ecd7", - "metadata": {}, - "source": [ - "## Requirements\n", - "\n", - "Before starting this tutorial, be sure you have the following installed:\n", - "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", - "- Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\n", - "- Python SAT (`pip install python-sat`)" - ] - }, - { - "cell_type": "markdown", - "id": "d4b2713f-3324-4fcd-b9ec-2e5a2496eda4", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "6f597f16-939d-4195-a3d3-deb4b61bc012", - "metadata": {}, - "outputs": [], - "source": [ - "from __future__ import annotations\n", - "\n", - "import numpy as np\n", - "import rustworkx as rx\n", - "import matplotlib.pyplot as plt\n", - "from dataclasses import dataclass\n", - "from itertools import combinations\n", - "from threading import Timer\n", - "from collections.abc import Callable, Iterable\n", - "from typing import Sequence\n", - "from pysat.formula import CNF, IDPool\n", - "from pysat.solvers import Solver\n", - "from scipy.optimize import minimize\n", - "from rustworkx.visualization import mpl_draw as draw_graph\n", - "\n", - "from qiskit.quantum_info import SparsePauliOp\n", - "from qiskit.circuit.library import QAOAAnsatz\n", - "from qiskit.circuit import QuantumCircuit, ParameterVector\n", - "from qiskit.transpiler import CouplingMap, PassManager\n", - "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", - "from qiskit.transpiler.passes.routing.commuting_2q_gate_routing import (\n", - " SwapStrategy,\n", - " FindCommutingPauliEvolutions,\n", - " Commuting2qGateRouter,\n", - ")\n", - "\n", - "from qiskit_ibm_runtime import QiskitRuntimeService, Session\n", - "from qiskit_ibm_runtime import SamplerV2 as Sampler" - ] - }, - { - "cell_type": "markdown", - "id": "4133971b-d6ae-447a-aced-ee5ddaa1ae5a", - "metadata": {}, - "source": [ - "## Step 1: Map classical inputs to a quantum problem\n", - "\n", - "### Max-Cut Problem\n", - "Let's consider solving the **Max-Cut** problem on a graph with 100 nodes using QAOA.\n", - "The Max-Cut problem is a combinatorial optimization problem that is defined on a graph $G = (V, E)$, where $V$ is the set of vertices and $E$ is the set of edges. The goal is to partition the vertices into two sets, $S$ and $V \\setminus S$, such that the number of edges between the two sets is maximized.\n", - "In this example, we use a graph with 100 nodes that is based on a hardware coupling map.\n", - "\n", - "\n", - "### Graph → Hamiltonian\n", - "\n", - "First, convert the graph into a Hamiltonian that is suited for the QAOA. Details on this process can be found in the [introductory QAOA tutorial.](/docs/tutorials/quantum-approximate-optimization-algorithm)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "72e384cb-b090-419f-b181-e83f3fc191de", - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "service = QiskitRuntimeService()\n", - "backend = service.least_busy(operational=True, simulator=False)\n", - "print(backend)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "39d768e2-adeb-4678-a691-05480df67b47", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "1a869c2d-ae51-47d3-8a41-bfcf5e505f59", + "metadata": {}, + "source": [ + "# Advanced techniques for QAOA\n", + "\n", + "*Usage estimate: 25 minutes on IBM Sherbrooke (NOTE: This is an estimate only. Your runtime may vary.)*\n", + "\n" + ] + }, { - "data": { - "text/plain": [ - "\"Output" + "cell_type": "markdown", + "id": "ea97567d-810f-4cca-8edf-a47d70ea870a", + "metadata": {}, + "source": [ + "## Background\n", + "\n", + "This notebook introduces advanced techniques to improve the performance of the **Quantum Approximate Optimization Algorithm (QAOA)** with a large number of qubits.\n", + "Please see [Solve utility-scale quantum optimization problems](/docs/tutorials/quantum-approximate-optimization-algorithm) for an introduction to QAOA.\n", + "\n", + "The advanced techniques in this notebook include:\n", + "\n", + "* **SWAP strategy with SAT initial mapping**: This is a specifically designed transpiler pass for QAOA that uses a SWAP strategy and a SAT solver together to improve the selection of which physical qubits on the QPU to use. The SWAP strategy exploits the commutativity of the QAOA operators to reorder gates so that layers of SWAP gates can be simultaneously executed, thus reducing the depth of the circuit [\\[1\\]](#references). The SAT solver is used to find an initial mapping that minimizes the number of SWAP operations needed to map the qubits in the circuit to the physical qubits on the device [\\[2\\]](#references) .\n", + "* **CVaR cost function**: Typically the expected value of the cost Hamiltonian is used as the cost function for QAOA, but as was shown in [\\[3\\]](#references) , focusing on the tail of the distribution, rather than the expected value, can improve the performance of QAOA for combinatorial optimization problems. The CVaR accomplishes this. For a given set of shots with corresponding objective values of the considered optimization problem, the Conditional Value at Risk (CVaR) with confidence level $\\alpha \\in [0, 1]$ is defined as the average of the $\\alpha$ best shots [\\[3\\]](#references).\n", + " Thus, $\\alpha = 1$ corresponds to the standard expected value, while $\\alpha=0$ corresponds to the minimum of the given shots, and $\\alpha \\in (0, 1)$ is a tradeoff between focusing on better shots, while still applying some averaging to smooth out the optimization landscape. Additionally, the CVaR can be used as an error mitigation technique to improve the quality of the objective value estimation [\\[4\\]](#references).\n", + "\n" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n = 100 # Number of nodes in graph\n", - "graph_100 = rx.PyGraph()\n", - "graph_100.add_nodes_from(np.arange(0, n, 1))\n", - "elist = []\n", - "for edge in backend.coupling_map:\n", - " if edge[0] < n and edge[1] < n:\n", - " elist.append((edge[0], edge[1], 1.0))\n", - "graph_100.add_edges_from(elist)\n", - "draw_graph(graph_100, node_size=200, with_labels=True, width=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "385a3b5a-1860-4b7e-abaf-933a35f59ac6", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cost Function Hamiltonian: SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIZ', 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'IZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IZIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII'],\n", - " coeffs=[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j])\n" - ] - } - ], - "source": [ - "def build_max_cut_paulis(graph: rx.PyGraph) -> list[tuple[str, float]]:\n", - " \"\"\"Convert the graph to Pauli list.\n", - "\n", - " This function does the inverse of `build_max_cut_graph`\n", - " \"\"\"\n", - " pauli_list = []\n", - " for edge in list(graph.edge_list()):\n", - " paulis = [\"I\"] * len(graph)\n", - " paulis[edge[0]], paulis[edge[1]] = \"Z\", \"Z\"\n", - "\n", - " weight = graph.get_edge_data(edge[0], edge[1])\n", - "\n", - " pauli_list.append((\"\".join(paulis)[::-1], weight))\n", - "\n", - " return pauli_list\n", - "\n", - "\n", - "max_cut_paulis = build_max_cut_paulis(graph_100)\n", - "\n", - "cost_hamiltonian = SparsePauliOp.from_list(max_cut_paulis)\n", - "print(\"Cost Function Hamiltonian:\", cost_hamiltonian)" - ] - }, - { - "cell_type": "markdown", - "id": "ca4b56d1-d1e0-4f57-b156-6cf7cfa7a061", - "metadata": {}, - "source": [ - "### Hamiltonian → quantum circuit" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8398b537-8863-414b-b8cb-e9f92436ddc1", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "40fb546e-85e0-450b-a5ea-5d08950d129f", + "metadata": {}, + "source": [ + "## Requirements\n", + "\n", + "Before starting this tutorial, be sure you have the following installed:\n", + "\n", + "* Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", + "* Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\n", + "* Rustworkx graph library (`pip install rustworkx`)\n", + "* Python SAT (`pip install python-sat`)\n", + "\n" + ] + }, { - "data": { - "text/plain": [ - "\"Output" + "cell_type": "markdown", + "id": "50285e5f-1a7b-471c-a223-1ae0af19d9ed", + "metadata": {}, + "source": [ + "## Setup\n", + "\n" ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "circuit_100 = QAOAAnsatz(cost_operator=cost_hamiltonian, reps=1)\n", - "circuit_100.measure_all()\n", - "\n", - "circuit_100.draw(\"mpl\", fold=False, scale=0.2, idle_wires=False)" - ] - }, - { - "cell_type": "markdown", - "id": "9c0749c0-93c4-4993-8bc4-9b7b7446d5f8", - "metadata": {}, - "source": [ - "## Step 2: Optimize problem for quantum hardware execution\n", - "### SWAP strategy with the SAT initial mapping\n", - "We will demonstrate how to build and optimize QAOA circuits using the **SWAP strategy with SAT initial mapping**, a specifically designed transpiler pass for QAOA applied to quadratic problems.\n", - "\n", - "In this example, we choose a SWAP insertion strategy for blocks of commuting two-qubit gates, which applies layers of SWAP gates that are simultaneously executable on the coupling map. This strategy is presented in [\\[1\\]](#references) and is exposed as a standardized Qiskit transpiler pass (see [`Commuting2qGateRouter`](/docs/api/qiskit/qiskit.transpiler.passes.Commuting2qGateRouter)), where we can choose different qubit configurations. We use a line swap strategy in this example." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "0ed7840d-28f3-4101-862e-9f3b8a5fa1bb", - "metadata": {}, - "outputs": [], - "source": [ - "nodes = rx.longest_simple_path(graph_100)\n", - "# even edges and odd edges\n", - "even_edges = [\n", - " (nodes[i], nodes[i + 1])\n", - " if nodes[i] < nodes[i + 1]\n", - " else (nodes[i + 1], nodes[i])\n", - " for i in range(0, len(nodes) - 1, 2)\n", - "]\n", - "odd_edges = [\n", - " (nodes[i], nodes[i + 1])\n", - " if nodes[i] < nodes[i + 1]\n", - " else (nodes[i + 1], nodes[i])\n", - " for i in range(1, len(nodes) - 1, 2)\n", - "]\n", - "edge_list = [\n", - " (edge[0], edge[1]) if edge[0] < edge[1] else (edge[1], edge[0])\n", - " for edge in graph_100.edge_list()\n", - "]\n", - "\n", - "swap_strategy = SwapStrategy(CouplingMap(edge_list), (even_edges, odd_edges))" - ] - }, - { - "cell_type": "markdown", - "id": "0a3f2210-8242-4a75-b04d-dee5ac09a284", - "metadata": {}, - "source": [ - "#### Remap the graph using a SAT mapper\n", - "\n", - "Even when a circuit consists of commuting gates (this is the case for the QAOA circuit, but also for Trotterized simulations of Ising Hamiltonians), finding a good initial mapping is a challenging task. The SAT-based approach presented in [\\[2\\]](#references) enables the discovery of effective initial mappings for circuits with commuting gates, resulting in a significant reduction in the number of required SWAP layers. This approach has been demonstrated to scale to up to *500 qubits*, as illustrated in the paper.\n", - "\n", - "The following code demonstrates how to use the `SATMapper` from Matsuo et al. to remap the graph. This process allows the problem to be mapped to a more optimal initial state for a specified SWAP strategy, resulting in a significant reduction in the number of SWAP layers required to execute the circuit.\n", - "\n", - "In `SATMapper`, the problem of finding a good initial mapping is formulated as a SAT problem. A SAT solver is used to find such an initial mapping for the QAOA circuit. `python-sat` (`pysat` for short) is a Python library for a SAT solver, and we will use it to solve the SAT problem in this example." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "0fb9345b-3cfd-4aa6-9a37-e502b7383283", - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"A class to solve the SWAP gate insertion initial mapping problem\n", - "using the SAT approach from https://arxiv.org/abs/2212.05666.\n", - "\"\"\"\n", - "\n", - "\n", - "@dataclass\n", - "class SATResult:\n", - " \"\"\"A data class to hold the result of a SAT solver.\"\"\"\n", - "\n", - " satisfiable: bool # Satisfiable is True if the SAT model could be solved\n", - " # in a given time.\n", - " solution: dict # The solution to the SAT problem if it is satisfiable.\n", - " mapping: list # The mapping of nodes in the pattern graph to nodes in the\n", - " # target graph.\n", - " elapsed_time: float # The time it took to solve the SAT model.\n", - "\n", - "\n", - "class SATMapper:\n", - " r\"\"\"A class to introduce a SAT-approach to solve\n", - " the initial mapping problem in SWAP gate insertion for commuting gates.\n", - "\n", - " When this pass is run on a DAG it will look for the first instance of\n", - " :class:`.Commuting2qBlock` and use the program graph :math:`P` of this block\n", - " of gates to find a layout for a given swap strategy. This layout is found\n", - " with a binary search over the layers :math:`l` of the swap strategy. At each\n", - " considered layer a subgraph isomorphism problem formulated as a SAT is solved\n", - " by a SAT solver. Each instance is whether it is possible to embed the program\n", - " graph :math:`P` into the effective connectivity graph :math:`C_l` that is\n", - " achieved by applying :math:`l` layers of the swap strategy to the coupling map\n", - " :math:`C_0` of the backend. Since solving SAT problems can be hard, a\n", - " ``time_out`` fixes the maximum time allotted to the SAT solver for each\n", - " instance. If this time is exceeded the considered problem is deemed\n", - " unsatisfiable and the binary search proceeds to the next number of swap\n", - " layers :math:``l``.\n", - " \"\"\"\n", - "\n", - " def __init__(self, timeout: int = 60):\n", - " \"\"\"Initialize the SATMapping.\n", - "\n", - " Args:\n", - " timeout: The allowed time in seconds for each iteration of the SAT\n", - " solver. This variable defaults to 60 seconds.\n", - " \"\"\"\n", - " self.timeout = timeout\n", - "\n", - " def find_initial_mappings(\n", - " self,\n", - " program_graph: rx.Graph,\n", - " swap_strategy: SwapStrategy,\n", - " min_layers: int | None = None,\n", - " max_layers: int | None = None,\n", - " ) -> dict[int, SATResult]:\n", - " r\"\"\"Find an initial mapping for a given swap strategy. Perform a\n", - " binary search over the number of swap layers, and for each number\n", - " of swap layers solve a subgraph isomorphism problem formulated as\n", - " a SAT problem.\n", - "\n", - " Args:\n", - " program_graph (rx.Graph): The program graph with commuting gates, where\n", - " each edge represents a two-qubit gate.\n", - " swap_strategy (SwapStrategy): The swap strategy to use to find the\n", - " initial mapping.\n", - " min_layers (int): The minimum number of swap layers to consider.\n", - " Defaults to the maximum degree of the\n", - " program graph - 2.\n", - " max_layers (int): The maximum number of swap layers to consider.\n", - " Defaults to the number of qubits in the\n", - " swap strategy - 2.\n", - "\n", - " Returns:\n", - " dict[int, SATResult]: A dictionary containing the results of the SAT\n", - " solver for each number of swap layers.\n", - " \"\"\"\n", - " num_nodes_g1 = len(program_graph.nodes())\n", - " num_nodes_g2 = swap_strategy.distance_matrix.shape[0]\n", - " if num_nodes_g1 > num_nodes_g2:\n", - " return SATResult(False, [], [], 0)\n", - " if min_layers is None:\n", - " # use the maximum degree of the program graph - 2\n", - " # as the lower bound.\n", - " min_layers = max((d for _, d in program_graph.degree)) - 2\n", - " if max_layers is None:\n", - " max_layers = num_nodes_g2 - 1\n", - "\n", - " variable_pool = IDPool(start_from=1)\n", - " variables = np.array(\n", - " [\n", - " [variable_pool.id(f\"v_{i}_{j}\") for j in range(num_nodes_g2)]\n", - " for i in range(num_nodes_g1)\n", - " ],\n", - " dtype=int,\n", - " )\n", - " vid2mapping = {v: idx for idx, v in np.ndenumerate(variables)}\n", - " binary_search_results = {}\n", - "\n", - " def interrupt(solver):\n", - " # This function is called to interrupt the solver when the\n", - " # timeout is reached.\n", - " solver.interrupt()\n", - "\n", - " # Make a cnf (conjunctive normal form) for the one-to-one\n", - " # mapping constraint\n", - " cnf1 = []\n", - " for i in range(num_nodes_g1):\n", - " clause = variables[i, :].tolist()\n", - " cnf1.append(clause)\n", - " for k, m in combinations(clause, 2):\n", - " cnf1.append([-1 * k, -1 * m])\n", - " for j in range(num_nodes_g2):\n", - " clause = variables[:, j].tolist()\n", - " for k, m in combinations(clause, 2):\n", - " cnf1.append([-1 * k, -1 * m])\n", - "\n", - " # Perform a binary search over the number of swap layers to find the\n", - " # minimum number of swap layers that satisfies the subgraph isomorphism\n", - " # problem.\n", - " while min_layers < max_layers:\n", - " num_layers = (min_layers + max_layers) // 2\n", - "\n", - " # Create the connectivity matrix. Note that if the swap strategy\n", - " # cannot reach full connectivity then its distance matrix will have\n", - " # entries with -1. These entries must be treated as False.\n", - " d_matrix = swap_strategy.distance_matrix\n", - " connectivity_matrix = (\n", - " (-1 < d_matrix) & (d_matrix <= num_layers)\n", - " ).astype(int)\n", - " # Make a cnf for the adjacency constraint\n", - " cnf2 = []\n", - " for e_0, e_1 in list(program_graph.edge_list()):\n", - " clause_matrix = np.multiply(\n", - " connectivity_matrix, variables[e_1, :]\n", - " )\n", - " clause = np.concatenate(\n", - " (\n", - " [[-variables[e_0, i]] for i in range(num_nodes_g2)],\n", - " clause_matrix,\n", - " ),\n", - " axis=1,\n", - " )\n", - " # Remove 0s from each clause\n", - " cnf2.extend([c[c != 0].tolist() for c in clause])\n", - "\n", - " cnf = CNF(from_clauses=cnf1 + cnf2)\n", - "\n", - " with Solver(bootstrap_with=cnf, use_timer=True) as solver:\n", - " # Solve the SAT problem with a timeout.\n", - " # Timer is used to interrupt the solver when the\n", - " # timeout is reached.\n", - " timer = Timer(self.timeout, interrupt, [solver])\n", - " timer.start()\n", - " status = solver.solve_limited(expect_interrupt=True)\n", - " timer.cancel()\n", - " # Get the solution and the elapsed time.\n", - " sol = solver.get_model()\n", - " e_time = solver.time()\n", - "\n", - " print(\n", - " f\"Layers: {num_layers}, Status: {status}, Time: {e_time}\"\n", - " )\n", - " if status:\n", - " # If the SAT problem is satisfiable, convert the solution\n", - " # to a mapping.\n", - " mapping = [vid2mapping[idx] for idx in sol if idx > 0]\n", - " binary_search_results[num_layers] = SATResult(\n", - " status, sol, mapping, e_time\n", - " )\n", - " max_layers = num_layers\n", - " else:\n", - " # If the SAT problem is unsatisfiable, return the last\n", - " # satisfiable solution.\n", - " binary_search_results[num_layers] = SATResult(\n", - " status, sol, [], e_time\n", - " )\n", - " min_layers = num_layers + 1\n", - "\n", - " return binary_search_results\n", - "\n", - " def remap_graph_with_sat(\n", - " self, graph: rx.Graph, swap_strategy, max_layers\n", - " ):\n", - " \"\"\"Applies the SAT mapping.\n", - "\n", - " Args:\n", - " graph (nx.Graph): The graph to remap.\n", - " swap_strategy (SwapStrategy): The swap strategy to use\n", - " to find the initial mapping.\n", - "\n", - " Returns:\n", - " tuple: A tuple containing the remapped graph, the edge map, and the\n", - " number of layers of the swap strategy that was used to find the\n", - " initial mapping. If no solution is found then the tuple contains\n", - " None for each element. Note the returned edge map `{k: v}` means that\n", - " node `k` in the original graph gets mapped to node `v` in the\n", - " Pauli strings.\n", - " \"\"\"\n", - " num_nodes = len(graph.nodes())\n", - " results = self.find_initial_mappings(\n", - " graph, swap_strategy, 0, max_layers\n", - " )\n", - " solutions = [k for k, v in results.items() if v.satisfiable]\n", - "\n", - " if len(solutions):\n", - " min_k = min(solutions)\n", - " edge_map = dict(results[min_k].mapping)\n", - " # Create the remapped graph\n", - " remapped_graph = rx.PyGraph()\n", - " remapped_graph.add_nodes_from(range(num_nodes))\n", - " mapping = dict(results[min_k].mapping)\n", - " for i, graph_edge in enumerate(list(graph.edge_list())):\n", - " remapped_edge = tuple(mapping[node] for node in graph_edge)\n", - " remapped_graph.add_edge(*remapped_edge, graph.edges()[i])\n", - " return remapped_graph, edge_map, min_k\n", - " else:\n", - " return None, None, None" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4350d321-ee74-4de8-9599-b4ac6d507ff7", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Layers: 0, Status: True, Time: 0.07903199999999977\n", - "Map from old to new nodes: {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20, 21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26, 27: 27, 28: 28, 29: 29, 30: 30, 31: 31, 32: 32, 33: 33, 34: 34, 35: 35, 36: 36, 37: 37, 38: 38, 39: 39, 40: 40, 41: 41, 42: 42, 43: 43, 44: 44, 45: 45, 46: 46, 47: 47, 48: 48, 49: 49, 50: 50, 51: 51, 52: 52, 53: 53, 54: 54, 55: 55, 56: 56, 57: 57, 58: 58, 59: 59, 60: 60, 61: 61, 62: 62, 63: 63, 64: 64, 65: 65, 66: 66, 67: 67, 68: 68, 69: 69, 70: 70, 71: 71, 72: 72, 73: 73, 74: 74, 75: 75, 76: 76, 77: 77, 78: 78, 79: 79, 80: 80, 81: 81, 82: 82, 83: 83, 84: 84, 85: 85, 86: 86, 87: 87, 88: 88, 89: 89, 90: 90, 91: 91, 92: 92, 93: 93, 94: 94, 95: 95, 96: 96, 97: 97, 98: 98, 99: 99}\n", - "Min SWAP layers: 0\n" - ] + "cell_type": "code", + "execution_count": 13, + "id": "d019ea68-61e0-4341-84c7-e612ca10dde7", + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "import numpy as np\n", + "import rustworkx as rx\n", + "from dataclasses import dataclass\n", + "from itertools import combinations\n", + "from threading import Timer\n", + "from IPython.display import Image\n", + "from collections.abc import Callable, Iterable\n", + "from pysat.formula import CNF, IDPool\n", + "from pysat.solvers import Solver\n", + "from scipy.optimize import minimize\n", + "from rustworkx.visualization import mpl_draw as draw_graph\n", + "\n", + "from qiskit.quantum_info import SparsePauliOp\n", + "from qiskit.circuit.library import QAOAAnsatz\n", + "from qiskit.circuit import QuantumCircuit, ParameterVector\n", + "from qiskit.transpiler import CouplingMap, PassManager\n", + "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", + "from qiskit.transpiler.passes.routing.commuting_2q_gate_routing import (\n", + " SwapStrategy,\n", + " FindCommutingPauliEvolutions,\n", + " Commuting2qGateRouter,\n", + ")\n", + "\n", + "from qiskit_ibm_runtime import QiskitRuntimeService, Session\n", + "from qiskit_ibm_runtime import SamplerV2 as Sampler" + ] }, { - "data": { - "text/plain": [ - "\"Output" + "cell_type": "markdown", + "id": "8b77b0c9-f5a6-476e-86b8-069ba14f9ab3", + "metadata": {}, + "source": [ + "\n", + "\n", + "### Max-Cut Problem\n", + "\n", + "Let's consider solving the **Max-Cut** problem on a graph with 100 nodes using QAOA.\n", + "The Max-Cut problem is a combinatorial optimization problem that is defined on a graph $G = (V, E)$, where $V$ is the set of vertices and $E$ is the set of edges. The goal is to partition the vertices into two sets, $S$ and $V \\setminus S$, such that the number of edges between the two sets is maximized.\n", + "In this example, we use a graph with 100 nodes that is based on a hardware coupling map.\n", + "\n", + "\n", + "\n", + "\n" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sm = SATMapper(timeout=10)\n", - "remapped_graph, edge_map, min_swap_layers = sm.remap_graph_with_sat(\n", - " graph=graph_100, swap_strategy=swap_strategy, max_layers=1\n", - ")\n", - "print(\"Map from old to new nodes: \", edge_map)\n", - "print(\"Min SWAP layers:\", min_swap_layers)\n", - "draw_graph(remapped_graph, node_size=200, with_labels=True, width=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "128ee96e-3d28-441e-97b5-ae49d5b5ad1a", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - 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'IIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIZIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIZIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIZIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IZIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII'],\n", - " coeffs=[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j])\n" - ] - } - ], - "source": [ - "remapped_max_cut_paulis = build_max_cut_paulis(remapped_graph)\n", - "# define a qiskit SparsePauliOp from the list of paulis\n", - "remapped_cost_operator = SparsePauliOp.from_list(remapped_max_cut_paulis)\n", - "print(remapped_cost_operator)" - ] - }, - { - "cell_type": "markdown", - "id": "6ba2011e-f1c6-4056-9701-9574179b25ad", - "metadata": {}, - "source": [ - "#### Build a QAOA circuit with the SWAP strategy and the SAT mapping\n", - "We only want to apply the SWAP strategies to the cost operator layer, so we start by creating the isolated block that we will later transform and append to the final QAOA circuit.\n", - "\n", - "For this, we can use the [`QAOAAnsatz`](/docs/api/qiskit/qiskit.circuit.library.QAOAAnsatz) class from Qiskit. We input an empty circuit to the `initial_state` and `mixer_operator` fields to make sure we are building an isolated cost operator layer.\n", - "We also define the edge_coloring map so that RZZ gates are positioned next to SWAP gates. This strategic placement allows us to exploit CX cancellations, optimizing the circuit for better performance.\n", - "This process is executed within the `create_qaoa_swap_circuit` function." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "64ffd379-af81-4d03-954d-4afac67e643a", - "metadata": {}, - "outputs": [], - "source": [ - "def make_meas_map(circuit: QuantumCircuit) -> dict:\n", - " \"\"\"Return a mapping from qubit index (the key) to classical bit (the value).\n", - "\n", - " This allows us to account for the swapping order introduced by the SWAP strategy.\n", - " \"\"\"\n", - " creg = circuit.cregs[0]\n", - " qreg = circuit.qregs[0]\n", - "\n", - " meas_map = {}\n", - " for inst in circuit.data:\n", - " if inst.operation.name == \"measure\":\n", - " meas_map[qreg.index(inst.qubits[0])] = creg.index(inst.clbits[0])\n", - "\n", - " return meas_map\n", - "\n", - "\n", - "def apply_swap_strategy(\n", - " circuit: QuantumCircuit,\n", - " swap_strategy: SwapStrategy,\n", - " edge_coloring: dict[tuple[int, int], int] | None = None,\n", - ") -> QuantumCircuit:\n", - " \"\"\"Transpile with a SWAP strategy.\n", - "\n", - " Returns:\n", - " A quantum circuit transpiled with the given swap strategy.\n", - " \"\"\"\n", - "\n", - " pm_pre = PassManager(\n", - " [\n", - " FindCommutingPauliEvolutions(),\n", - " Commuting2qGateRouter(\n", - " swap_strategy,\n", - " edge_coloring,\n", - " ),\n", - " ]\n", - " )\n", - " return pm_pre.run(circuit)\n", - "\n", - "\n", - "def apply_qaoa_layers(\n", - " cost_layer: QuantumCircuit,\n", - " meas_map: dict,\n", - " num_layers: int,\n", - " gamma: list[float] | ParameterVector = None,\n", - " beta: list[float] | ParameterVector = None,\n", - " initial_state: QuantumCircuit = None,\n", - " mixer: QuantumCircuit = None,\n", - "):\n", - " \"\"\"Applies QAOA layers to construct circuit.\n", - "\n", - " First, the initial state is applied. If `initial_state` is None, we begin in the\n", - " initial superposition state. Next, we alternate between layers of the cost operator\n", - " and the mixer. The cost operator is alternatively applied in order and in reverse\n", - " instruction order. This allows us to apply the swap strategy on odd `p` layers\n", - " and undo the swap strategy on even `p` layers.\n", - " \"\"\"\n", - "\n", - " num_qubits = cost_layer.num_qubits\n", - " new_circuit = QuantumCircuit(num_qubits, num_qubits)\n", - "\n", - " if initial_state is not None:\n", - " new_circuit.append(initial_state, range(num_qubits))\n", - " else:\n", - " # all h state by default\n", - " new_circuit.h(range(num_qubits))\n", - "\n", - " if gamma is None or beta is None:\n", - " gamma = ParameterVector(\"γ'\", num_layers)\n", - " if mixer is None or mixer.num_parameters == 0:\n", - " beta = ParameterVector(\"β'\", num_layers)\n", - " else:\n", - " beta = ParameterVector(\"β'\", num_layers * mixer.num_parameters)\n", - "\n", - " if mixer is not None:\n", - " mixer_layer = mixer\n", - " else:\n", - " mixer_layer = QuantumCircuit(num_qubits)\n", - " mixer_layer.rx(-2 * beta[0], range(num_qubits))\n", - "\n", - " for layer in range(num_layers):\n", - " bind_dict = {cost_layer.parameters[0]: gamma[layer]}\n", - " cost_layer_ = cost_layer.assign_parameters(bind_dict)\n", - " bind_dict = {\n", - " mixer_layer.parameters[i]: beta[layer + i]\n", - " for i in range(mixer_layer.num_parameters)\n", - " }\n", - " layer_mixer = mixer_layer.assign_parameters(bind_dict)\n", - "\n", - " if layer % 2 == 0:\n", - " new_circuit.append(cost_layer_, range(num_qubits))\n", - " else:\n", - " new_circuit.append(cost_layer_.reverse_ops(), range(num_qubits))\n", - "\n", - " new_circuit.append(layer_mixer, range(num_qubits))\n", - "\n", - " for qidx, cidx in meas_map.items():\n", - " new_circuit.measure(qidx, cidx)\n", - "\n", - " return new_circuit\n", - "\n", - "\n", - "def create_qaoa_swap_circuit(\n", - " cost_operator: SparsePauliOp,\n", - " swap_strategy: SwapStrategy,\n", - " edge_coloring: dict = None,\n", - " theta: list[float] = None,\n", - " qaoa_layers: int = 1,\n", - " initial_state: QuantumCircuit = None,\n", - " mixer: QuantumCircuit = None,\n", - "):\n", - " \"\"\"Create the circuit for QAOA.\n", - "\n", - " Notes: This circuit construction for QAOA works for quadratic terms in `Z` and will be\n", - " extended to first-order terms in `Z`. Higher-orders are not supported.\n", - "\n", - " Args:\n", - " cost_operator: the cost operator.\n", - " swap_strategy: selected swap strategy\n", - " edge_coloring: A coloring of edges that should correspond to the coupling\n", - " map of the hardware. It defines the order in which we apply the Rzz\n", - " gates. This allows us to choose an ordering such that `Rzz` gates will\n", - " immediately precede SWAP gates to leverage CNOT cancellation.\n", - " theta: The QAOA angles.\n", - " qaoa_layers: The number of layers of the cost operator and the mixer operator.\n", - " initial_state: The initial state on which we apply layers of cost operator\n", - " and mixer.\n", - " mixer: The QAOA mixer. It will be applied as is onto the QAOA circuit. Therefore,\n", - " its output must have the same ordering of qubits as its input.\n", - " \"\"\"\n", - "\n", - " num_qubits = cost_operator.num_qubits\n", - "\n", - " if theta is not None:\n", - " gamma = theta[: len(theta) // 2]\n", - " beta = theta[len(theta) // 2 :]\n", - " qaoa_layers = len(theta) // 2\n", - " else:\n", - " gamma = beta = None\n", - "\n", - " # First, create the ansatz of one layer of QAOA without mixer\n", - " cost_layer = QAOAAnsatz(\n", - " cost_operator,\n", - " reps=1,\n", - " initial_state=QuantumCircuit(num_qubits),\n", - " mixer_operator=QuantumCircuit(num_qubits),\n", - " ).decompose()\n", - "\n", - " # This will allow us to recover the permutation of the measurements that the swap introduce.\n", - " cost_layer.measure_all()\n", - "\n", - " # Now, apply the swap strategy for commuting Pauli evolution gates\n", - " cost_layer = apply_swap_strategy(cost_layer, swap_strategy, edge_coloring)\n", - "\n", - " # Compute the measurement map (qubit to classical bit).\n", - " # We will apply this for qaoa_layers % 2 == 1.\n", - " if qaoa_layers % 2 == 1:\n", - " meas_map = make_meas_map(cost_layer)\n", - " else:\n", - " meas_map = {idx: idx for idx in range(num_qubits)}\n", - "\n", - " cost_layer.remove_final_measurements()\n", - "\n", - " # Finally, introduce the mixer circuit and add measurements following measurement map\n", - " circuit = apply_qaoa_layers(\n", - " cost_layer, meas_map, qaoa_layers, gamma, beta, initial_state, mixer\n", - " )\n", - "\n", - " return circuit" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "690d3b82-fef2-4bd3-85e2-12c53404d613", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Map classical inputs to a quantum problem\n", + "\n", + "### Graph → Hamiltonian\n", + "\n", + "First, map the problem onto a quantum circuit that is suited for the QAOA. Details on this process can be found in the [introductory QAOA tutorial.](/docs/tutorials/quantum-approximate-optimization-algorithm)" + ] + }, { - "data": { - "text/plain": [ - "\"Output" + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Instantiate runtime to access backend\n", + "service = QiskitRuntimeService()\n", + "backend=service.least_busy(min_num_qubits=100, operational=True, simulator=False)\n", + "print(backend)" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "draw_graph(remapped_graph, node_size=200, with_labels=True, width=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "bcb0d53d-728d-4204-b95c-335bec719835", - "metadata": {}, - "outputs": [], - "source": [ - "# We can define the edge_coloring map so that RZZ gates are positioned next to SWAP gates to exploit CX cancellations\n", - "# We use greedy edge coloring to color the edges of the graph in the rustworkx. This coloring is used to order the RZZ gates in the circuit.\n", - "\n", - "edge_coloring_idx = rx.graph_greedy_edge_color(graph_100)\n", - "edge_coloring = {\n", - " edge: edge_coloring_idx[idx]\n", - " for idx, edge in enumerate(list(graph_100.edge_list()))\n", - "}\n", - "edge_coloring = {tuple(sorted(k)): v for k, v in edge_coloring.items()}" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "514fd708-03cd-421f-95c9-b5d6f539ea73", - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "\"Output" + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "backend.coupling_map.is_symmetric" ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qaoa_circ = create_qaoa_swap_circuit(\n", - " remapped_cost_operator,\n", - " swap_strategy,\n", - " edge_coloring=edge_coloring,\n", - " qaoa_layers=1,\n", - ")\n", - "qaoa_circ.draw(output=\"mpl\", fold=False)" - ] - }, - { - "cell_type": "markdown", - "id": "37b8f5ce-edfd-49e4-8aa2-afe3833e5ba5", - "metadata": {}, - "source": [ - "## Step 3: Execute using Qiskit primitives\n", - "### Define a CVaR cost function\n", - "This example shows how to use the Conditional Value at Risk (CVaR) cost function introduced in [\\[3\\]](#references) within the variational quantum optimization algorithms.\n", - "\n", - "The CVaR of a random variable $X$ for a confidence level $α ∈ (0, 1]$ is defined as\n", - "$$ CVaR_{\\alpha}(X) = \\mathbb{E} \\lbrack X | X \\leq F_X^{-1}(\\alpha) \\rbrack $$\n", - "where $F_X^{-1}(p)$ is the inverse cumulative distribution function of $X$. In other words, CVaR is the expected value of the lower $\\alpha$-tail of the distribution of $X$." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b7bbbebb-ea00-4efa-aa32-173e80fd0d92", - "metadata": {}, - "outputs": [], - "source": [ - "pass_manager = generate_preset_pass_manager(\n", - " backend=backend,\n", - " optimization_level=3,\n", - ")\n", - "\n", - "transpiled_qaoa_circ = pass_manager.run(qaoa_circ)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "29cc54fb-ccf2-411a-8271-5dc23f0ce8ac", - "metadata": {}, - "outputs": [], - "source": [ - "# Utility functions for the evaluation of the expectation value of a measured state\n", - "# In this code, for optimization, the measured state is converted into a bit string,\n", - "# and the sign of the value is determined by taking the exclusive OR of the bits\n", - "# corresponding to Pauli Z.\n", - "\n", - "_PARITY = np.array(\n", - " [-1 if bin(i).count(\"1\") % 2 else 1 for i in range(256)],\n", - " dtype=np.complex128,\n", - ")\n", - "\n", - "\n", - "def evaluate_sparse_pauli(state: int, observable: SparsePauliOp) -> complex:\n", - " \"\"\"Utility for the evaluation of the expectation value of a measured state.\n", - "\n", - " Args:\n", - " state (int): The measured state.\n", - " observable (SparsePauliOp): The observable to evaluate the expectation value for.\n", - "\n", - " Returns:\n", - " complex: The expectation value of the measured state.\n", - " \"\"\"\n", - " packed_uint8 = np.packbits(observable.paulis.z, axis=1, bitorder=\"little\")\n", - " state_bytes = np.frombuffer(\n", - " state.to_bytes(packed_uint8.shape[1], \"little\"), dtype=np.uint8\n", - " )\n", - " reduced = np.bitwise_xor.reduce(packed_uint8 & state_bytes, axis=1)\n", - " return np.sum(observable.coeffs * _PARITY[reduced])" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "4104c91b-f1e0-44ea-92da-750070009b17", - "metadata": {}, - "outputs": [], - "source": [ - "def qaoa_sampler_cost_fun(\n", - " params, ansatz, hamiltonian, sampler, aggregation=None\n", - "):\n", - " \"\"\"Standard sampler-based QAOA cost function to be plugged into optimizer routines.\n", - "\n", - " Args:\n", - " params (np.ndarray): Parameters for the ansatz.\n", - " ansatz (QuantumCircuit): Ansatz circuit.\n", - " hamiltonian (SparsePauliOp): Hamiltonian to be minimized.\n", - " sampler (QAOASampler): Sampler to be used.\n", - " aggregation (Callable | float | None): Aggregation function to be applied to\n", - " the sampled results. If None, the sum of the expectation values is returned.\n", - " If float, the CVaR with the given alpha is used.\n", - " \"\"\"\n", - " # Run the circuit\n", - " job = sampler.run([(ansatz, params)])\n", - " sampler_result = job.result()\n", - " sampled_int_counts = sampler_result[0].data.c.get_int_counts()\n", - " shots = sum(sampled_int_counts.values())\n", - " int_count_distribution = {\n", - " key: val / shots for key, val in sampled_int_counts.items()\n", - " }\n", - "\n", - " # a dictionary containing: {state: (measurement probability, value)}\n", - " evaluated = {\n", - " state: (\n", - " probability,\n", - " np.real(evaluate_sparse_pauli(state, hamiltonian)),\n", - " )\n", - " for state, probability in int_count_distribution.items()\n", - " }\n", - "\n", - " # If aggregation is None, return the sum of the expectation values.\n", - " # If aggregation is a float, return the CVaR with the given alpha.\n", - " # Otherwise, use the aggregation function.\n", - " if aggregation is None:\n", - " result = sum(\n", - " probability * value for probability, value in evaluated.values()\n", - " )\n", - " elif isinstance(aggregation, float):\n", - " cvar_aggregation = _get_cvar_aggregation(aggregation)\n", - " result = cvar_aggregation(evaluated.values())\n", - " else:\n", - " result = aggregation(evaluated.values())\n", - "\n", - " global iter_counts, result_dict\n", - " iter_counts += 1\n", - " temp_dict = {}\n", - " temp_dict[\"params\"] = params.tolist()\n", - " temp_dict[\"cvar_fval\"] = result\n", - " temp_dict[\"fval\"] = sum(\n", - " probability * value for probability, value in evaluated.values()\n", - " )\n", - " temp_dict[\"distribution\"] = sampled_int_counts\n", - " temp_dict[\"evaluated\"] = evaluated\n", - " result_dict[iter_counts] = temp_dict\n", - " print(f\"Iteration {iter_counts}: {result}\")\n", - "\n", - " return result\n", - "\n", - "\n", - "def _get_cvar_aggregation(alpha: float | None) -> Callable:\n", - " \"\"\"Return the CVaR aggregation function with the given alpha.\n", - "\n", - " Args:\n", - " alpha (float | None): Alpha value for the CVaR aggregation. If None, 1 is used\n", - " by default.\n", - " Raises:\n", - " ValueError: If alpha is not in [0, 1].\n", - " \"\"\"\n", - " if alpha is None:\n", - " alpha = 1\n", - " elif not 0 <= alpha <= 1:\n", - " raise ValueError(f\"alpha must be in [0, 1], but {alpha} was given.\")\n", - "\n", - " def cvar_aggregation(\n", - " objective_dict: Iterable[tuple[float, float]],\n", - " ) -> float:\n", - " \"\"\"Return the CVaR of the given measurements.\n", - " Args:\n", - " objective_dict (Iterable[tuple[float, float]]): An iterable of tuples containing\n", - " the measured bit string and the objective value based on the bit string.\n", - "\n", - " \"\"\"\n", - " sorted_measurements = sorted(objective_dict, key=lambda x: x[1])\n", - " # accumulate the probabilities until alpha is reached\n", - " accumulated_percent = 0.0\n", - " cvar = 0.0\n", - " for probability, value in sorted_measurements:\n", - " cvar += value * min(probability, alpha - accumulated_percent)\n", - " accumulated_percent += probability\n", - " if accumulated_percent >= alpha:\n", - " break\n", - " return cvar / alpha\n", - "\n", - " return cvar_aggregation" - ] - }, - { - "cell_type": "markdown", - "id": "5dba6ce4-8943-4492-9363-f1aebdd7175d", - "metadata": {}, - "source": [ - "The CVaR can be used as an error mitigation technique as previously discussed [\\[4\\]](#references). In this example, we determine $\\alpha$ and the number of shots according to the circuit's error rate." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6e1aa95c-fda5-4dff-a17a-fb872ed6e5a1", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "layer fidelity 0.00497140225686636\n", - "\n", - "The corresponding CVaR aggregation value is: 0.06332979773816615\n", - "To mitigate the twirled noise, increase shots by 15.790355183739091\n" - ] - } - ], - "source": [ - "num_2q_ops = transpiled_qaoa_circ.count_ops()[\"ecr\"]\n", - "\n", - "for el in backend.properties().general:\n", - " if el.name[:2] == \"lf\" and el.name[3:] == str(n):\n", - " lf = el.value # layer fidelity\n", - " print(\"layer fidelity\", lf)\n", - " eplg = 1 - lf ** (1 / (n - 1)) # error per layered gate (EPLG)\n", - " fid_cx = 1 - eplg\n", - " gamma_cx = 1 / fid_cx**2\n", - " gamma_circ = gamma_cx ** num_2q_ops\n", - "\n", - "cvar_aggregation = 1 / np.sqrt(gamma_circ)\n", - "print(\"\")\n", - "print(\"The corresponding CVaR aggregation value is: \", cvar_aggregation)\n", - "print(\n", - " \"To mitigate the twirled noise, increase shots by \", np.sqrt(gamma_circ)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "707d7174-6bc4-49d1-9b09-5df8b08fcc85", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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yrDvQ/tzJ8qk0IaYAjYK28Lb9kDP/bqij4vlvaxb/8iv6t0mAN18P1a9LTEGQM9aCChV71yL/51cQ2fVMnlJ3BAVV5488w2c9lBRQVlVV2QSa27JK8Z9VJR5lX50hPS7cK4sSwTvIqy0IgiD4BFNZe08ul0BtMkiJkWzPSI46lLF0Bwoe3M3ymRPeqjsPiZBtn72A0vxxfOF17oy1IFF1dDvyvn8WYa17IXny3SYfdHv42lZQq9UiOjqaL+aEpBYDq1Z75X2pD/P3xJNFieAdJKAUBEEQvC5o3dDgDV1DwRR5fZM944hOyW71uFEmSnVqf65k+exB5W/rKW9CdepxfOV1Tq+PM9aCdcW5yP36cX5GUd2Go3z36aCNCE1tx/qaTW0raP6ewAvviz189Z4I7iMBpSAIguBVqz1FGohoSB5IuZ3sB8lVhUTQaRrcWYrychCrqUWxLsSpLB8Ll6tgcmcxbbP7T+iryhCW3rnRMn2K1zkR3qYX0mc9x9aCZZsWm6wF40fOZmtBoq4oG4ZTAW/Br2/a7C9u+GWmgJJeV+odbArczb66gq+zr4LryLshCIIgeIXXlu1zW7ycAiC6kO8ziaCTbqXDbXU6/PLLL3jnnXfw448/ImnsTTyc4kyWj8qrOQseQmT3EQhJbAWVSoXq7P0o37EMmrg0xAyeYvFYvsz0UZmfMrOK13lYi65Iu/hRh9uHt+2Dtvf96LStYFMOoriafSVqcg+hYp9xCry28AQHz0V/LuC/KVCO7DykybOvgmPEy1sQBEHwSmaSgkFvMXd6bxu9wGPHjuH999/He++9h8zMTPTr1w833HADhoydigve3cTbVGft4Sxfbc5BU5YvutcYzvKRTqOuohhFKz9BVeZ26EryYNDXQRubesrL+2ILL+/G8MT2pdd5U0rkmPuUO/O+KJPr+Ytfsru/qF5jkDzpdr/xKRdskYBSEARB8EpQVJabiaJV81F9bCf0lWXQxKbwBG/skAtsSswElZiPv3MD9BXFSJ52H6K6nWUTFLWIC8PPP//M2ciffvqJJXcuu+wyDiQHDhzIGUZi1rx1pkyft1AyfaSbGejBeFMQyO+J4DreHVsTBEEQmh00gFNVlIvsj+5A9fE9iBkwCQnnXoewlt1QvPpT5H33nN37Fa36FIbaaru31en0uOR/i9CuXTtMnjyZs5NvvPEGsrKy8O6772LQoEGmYJKggR5yQvEmtD/ar6+hntG7xlraJgaDrWAgvyeC60hAKQiCIHhU2qRp7pKtf/CENFkAxp15EWL6jUfy+bchqtc5qNy/DrqqMov71Zw8zNZ7ysCJNToDkGWIw1nnX4gNGzZg48aNnJWMjbWvV0jlXRro8SZk09dYZWPqGX1mem/OzFIWzhVoe7ofZSbnjO4Ef8EX78nANgmoqnV9KlzwPRJQCoIgCG5DOpMU0OhrjBO95HxiDmk70iSvSm05A1q49B1EdjkT4a0cBxy0364Tr7XJRgZrpo+eP5X5qaRLNBRYKrfT9nQ/f8lM+uo9IdYdLuDeTCqnU6uF4D9IQCkIgiC4DYmWU49ceBtjGTJ/8Ss8SV1XchLlu1ZyFjJm4GSoQ0/3UJJ+YvXx3Ug4+6p69037JVu95pTpU7zOf7ttJGYNaYu2SZGsuWgO/U3X0+00nELb+7NHtSfvSUMyU9R/KvgHMpQjCIIguEVZdR16P3LaPpAkXkr++hKGutN9kbHDLkHCyFmmv/W11ch690ZE9TwbCaOuRNWRrcj5/AGboRwFCj+2PzLOZc1BZ8TVFZTb3RVX9zXBYivoynviCpQBrU9mSmgcAu8TKQiCIPgFR/LLLewUtXFpCGvdk32aWWvwwAaUrFnIZfDYgZN5m5K1X7EzStyZFzv1GLR/CqZctdVTMn0m+8e9uTiab8f+MSmSNQ1JBNxfZWiCxVbQ/D15+IcdWHMg3yv7Je3TlOgwvyz5NyckoBQEQRDcgjJm5q4nBb+8hhbXv23ybKbAEgYDipZ/yPJBhupKlKz7Boljb4Q6NMKtx3FHPPyRKT3xCHoGTaYv0AkP0WDjkULk/fgiyrf/7nC7lnM+hDbG+Fky6GpRsu5blG3/A3XFOVCHRSEsoxMSx93Cn7f/fr8Dwzom+112uTkh3yRBEATBLSgoUyjdtBihaR1MwaRCZKczUL5tKfdVlm/7HZqYRIS16Y26ohy+XVdeyD/1FSV8nSYuBSqV2uHjeEKwZPoCHSp7kzVnTP/xCG/Xz+pWAwqWvM7Z7tPBZB1yv3wU1cd3IbrvOISShWZVGapP7D3lvZ7M+6P9ij5l0yEBpSAIguAWlOGjsjGVkXUVRVCHR9tsY9CfknjR63hQp67wBLLeutZmu4Jf3+CfrW9bAJXZflSnHkcILpkpIqxld76YU5W5g7VJo3qcbbquZMMiVB3djvSZc9me0h7Uj0n73Z9b6retC8GOBJSCIAiC2xm/NomROFJQgZCEFqg8vBm1BccRktjSohROskEhKe0QP3IW9JUlFvuoOXkExavms98zCaGrrBx1qMdRytLBJzPlaCCHPy9QcYsEYTDoUfr39ywxRcEkLVCo/G3PeYn2O3/tUW5xEBof+ZYKgiAIbjO6ayo+WXeEA8LKgxuRPf9exAw83ziUs389qg5uRHTfsdDGJPHFGuqFK6aydkZnDhqsAwQamBGCT2bKHlTarti9GmGturPXN1GbdxS6sgKEprZD/s+voox6LnV1vEBJPPd6hLftYyMzRf2yQuMjOpSCIAiC21wxpM0pHcpeSJ/1HELTO6Js02IULH0XdUXZiB85G4nj5ri1b9ovTV8LwSMzdbQeMfLKQ5s4g21e7q4ryOKfJRu+Q9XRbUgadwuSJt4GQ10Nchb+FzW5hyz2QZP8NHwlND6SoRQEQWhmeGvaubKyEvNefBJVh6MR2b4flyTTLn7UpX1QhqntfT/aXE/ZSXKAkX644JWZslvuVmsR2f20Hqm+tsr4s6YCGVe9DG1siulzc/zt61Gy7mskT77LY5kpwXMkoBQEQWgGmPQY9+RylshGjzExksvXlHEkqZ2GWL16Na655hocPnwYt//nSXxbrUG1B/I+1mjVKhYZF4KH+uSf9DWVqNy3FhHt+3O7hIJKG8o/w1r2MAWThDYuFWGteqD62C6XHkfwHVLyFgRBCGLInYR8j8n/mHodaYDGOktEf9P1dHtDPsmlpaW45ZZbMGLECCQlJWHLli145qG78KiXByEem9JTNAWDjPrknyr2rjVOd/c8Xe4mNNFJdj3ijdfFsXyQK48j+A551QVBEIIU8jkmv2PyPSYasrpryCd5yZIl6NWrFz744AO89NJLWLVqFbp3N8q+XDq4DVvgeYO7x3YV15MglpmyR/nO5VCFRiCis6WOZGhKWy6D60ptXXV0pQVQR1qWtkVmqumQgFIQBCEIeW3ZPtz3zTYuQ7vqmUzb0/3o/rSfgoIC/Otf/8L48ePRpUsXbN++Hf/+97+h0Wgs7kd+ys9M740wrZp7IF2Btqf7zZ3eG3NGd3LpvkJgyUxZo6soRtXhLYjsPNRGDkgdFomIjoNY1Lw2P9N0fW1eJl8X3r6/xfYiM9V0SEApCIIQZFBmkfyNvQHtp/fU67Fo0SLMmzcPv/76K9q3b+9we8pULr19FA/UEA0FlsrttD3dTzKTwQ316Vp/Jsp3rWThe+tyt0L8qNlQhYQh5/MHUfzXl3zJWfAg1BExiDvzItN2IjPVtKgMBoNrS1dBEATBb6HeRypXU4aRRMaLVs1H9bGd0FeWQRObwoLRsUMuMGWCsj+9D9WZ2232E95+ANIueYw7LNV6HRb+qw8GdXccSNY7CLQ3l+VcbAaBkiI5ACBpIJnmbh7QZ4L6dM058fGdbLvZ6paPoFJbZr0VqrP3syd89fHdgEqF8LZ9kTD6KgsRfWLp7SPls9RESEApCIIQRNBADfVAVhfl4sS8W6AKi0JM/wmczaGTMflqR3QagtQL/2MKKFkvctSVFvvRRCciol1fCwkfT3ySvSVVJATPZ9TVVoz68MZnVPAM+TYLgiAEoU9y+fZl0FeXI2Pms8bBBgAx/caTuTbKt/8BXVUZNKc8s6lPLbrXaIf79YZPMgWPog0oECQHRVl0bwaUIjPV9EgPpSAIQpD5JCtC0PbkVijzSN7aKrVlPoE8kkkL0BGKT7IgeArJQYnMVPAhAaUgCEIQ+iSHtzFma/IXv4KanIOoKznJww+lmxcjZuBkqENPT9PWFmTh6PMzkPnCRch8dSaKVn7CvsrmKD7JguANRGYq+JCStyAIQhPhzb5Ca5/kiA4DETdiJkr++hIn9q8zXR877BIkjJxl+lubkME2dqEp7djmrmL3nyhe8wUHmSnT7rXrkyy9j4I3GNcjHWv25+Ovg/n1WjI6yphTmZsykxJM+gdyVBAEQQhgC8T6fJK1cWkIa90TkV2HsZ1dxYENKFmzkMvgsQMn8zbJE/9tcZ/oXucg/+dXUfbPElQfn4qwlt1Mt4lPsuAtJYIHvt3GfbkUGLoSTNL2lC2nARzqmfR1mVuGyZxHXhVBEIQmOInaG0gwt0D88K/DGNEp2emTprV/cfnOFSj45TW0uP5taGOT+ToKLGEwsPwKyQeZeyabE3vGBRxQVh7eYhFQ2nscQXBVI/Xh73eg7tTn39XBnCHtEzkr6UtpIF8t+oId6aEUBEEIMAtEe1j7F5duWozQtA6mYFIhstMZ7JlMfZWO0Jy6j76qtMHHEYTGcG9SWHMgH7/syEYg+N43N+TIIAiCECAWiK74JOsqimAw2GYTaZqbUX7agXQpCY34JAt+6t70hROLLH9b9AU7UvIWBEEIkJNoSnSY3QGEEydO4LPPPoO2Kh214UaZoJCEFqg8vJndcszdRKgUTrJBITSEU10BlSYEKm2I6XbyuqChHMUtxxzxSRbcgTJ4VOY2d70pXv0ZOzgZ6mqhjU9DdL/xiB00hW+vPLQJ5btWoSZrD2rzj0ETk4xWN79vsc//fr8Dwzome6WHkhZr7n5PKbCky33fbENeWTX72TdX5MggCILgw5No3o8vonz77w63aznnQ0Cnw/G3rnG4TXTfsUia8H8WJ9GKigr21/7444/x22+/QavVot81TyIvIh46AxA7ZAYqD25E9vx7ETPwfONQzv71qDq4kfenjUlC1ZGtyPv+OUT2GMkBKJXCK/at5RM9neDD0juZnoP4JAvuQr3DSs8kBYu5Xz2G0LSOiBt2KVSh4ZwR15UaBfmJ8h0rULF7FW/Duql2oP3Rfj11xmmsRV9zQKwXBUEQfGgvV5G5E7WF1j1fBhQseZ2nsFtc+wb0NVWo2PuXzT6qDm1E+Y7lSJ52H6K6ncVBXc8kLZK3L8BXX32F0tJSDB8+HLNnz8ZFF12EvBqthU9yddYeFK3+DLU5B6GrLDVmgnqNQezQGeyZXFuUbfRHPrEP+vJC9kgOSWqN6L7jOKBUqcyL6OKTLHjm3U0Z8ePvXI+wlt2RcsH9UKnsd93VleZzu4VKo0Xul4+i5uQRmwylNz6T5r73zmROiapju1C0/APUZB+AKiwCUd1GIH7UbKhDI/j2MK0aS28f1SxF1iVDKQiC4EMLRDp50sWcqswdnA2M6nE2/00i4/asD8l3WxUWyYM0BJXWtp6shXrTbtx5552YOXMmOnbsaNo+AeDJcMUnOaxFV6Rd/KjD5xkSn46Uafc57ZMswaTgrnsTfR7Ldy6HvrwICSNnczBJCylVSKhNYEnZc2dQ3JsecdN1x9XMKQ2y5S54kBddCWOu4cC3ZN03qC3MMn3PvJU5DUQkoBQEQfDhSdQe3McIFUv3OKKurABVR7chqtc5UGlDTdfTfmc+9g4entLL7v3EJ1nwV/emqsNbeIFUV5aP3G+eQF3BcahCwhHVazQSx1xn8Tl3BsW96RH09GjRR5nTvB9fQETHwfVmTgtXfAR1eDTSLn8a6jBjBlIbl4qCn1/lgDSi/QCv+N4HKjLlLQiC4MOTqDVkaVixezXCWnXnkpojKnaupJFsRPU0ZjEVaL/L9550eD/xSRb8BWv3JnJfInWBk18/zsFXygUPILrPeSjb/DPyfnrJrcdQ3Js88b23lzm1VkigoJMCYvo+KsGkYgSgCo1Axa7VaO6+95KhFARB8OFJ1BrKZOgrS0zlbkfQSY4GEsgW0ZqGLBDJJ5kmTr0xbCA+yYK7WLs3GWqruNUjuv8EJJ53g0ls36CrRdmWX1A74goLRQJnoP2/8v7n6JwSgZiYGLuX0NBQjzOnNScPczAcmm45xU0qCaGp7VGTc8ArmdNARgJKQRAEL2LPAtGm3K3WIrL7WQ63Iamfmuz9iBk81W75zRkLRJIvSY4OM7mSuFICp8RNiEYtPsmCR1i7Kikl7ajulq0etLiigLL6+G6XA0rikcefQM0Jx4snCijNA8yo+CScGHYnD6FZZ06j+4xF+Kgrud2kdOMP0FeVI2XqPdCVFfC2mmjqVLaEFn7VmadlkZqr733z+U8FQRAagfqsCfU1lajctxYR7fs7tD0kaLKbiOo52q3HMc9UDu+YjDsWbsGGI4UNbm96ngagT8s4ligSBHexdlXSRCehNu8oe8lbXB9lXBjpq8rcepwN6/5Cmxg1qx7QpaSkxPS7vUtWpRonzBQMnMmcGupqTBlJa+g6/anbm7PvvQSUgiAIXqQ+a8KKvWuN091WfZH2yt3axFYWOpCuPI45fx7Iw9bjxdCowPqUzrIps4iHe6gfkwJTQXAVxb1J+diFpndE1eHNPB0dktTKtF1daYFdZyZnoP13TI3lTGB8vGWg6ojNRwtxwZtrXMqcqkLC+DoKMq2h69R2Boqam++9DOUIgiB4EWsLROtAkRr4Izo7lhQh7ci6whOI6ul4AtxZC0QL20cXh75dsX0UBHtQkNfGbJiLNBuJsq2/WmzHf6s1CGvjupKAO+5N9jKn/LOezKkisK4rs830UzlcY0eAvbn53kuGUhAEwQcn0SNWgzm6imLjlGj3kVCHhDdc7q5naMeZk6g4gAj+wOiuqfhk3RFeoFCGMqrPeSjf+htO6vUIb9OLexVJ9SD2zItM+pM1uYdQsW8d/15beAKG6nIU/bmA/6YBmMhTCzJ33ZvcyZyGJrfloLcmex+iuhsDYyU7Sc83sptlT3Rz9L2XgFIQBMGHJ1GF8l0rufG/vnK3Qa9DOVnOteiKkIQMu9s4cxJVbB9puKdo1Xx2/tBXlkETm8Lal7FDLrAIaumkWLLuW5Rt/wN1xTlQh0UhLKMTEsfdAm1sste9k4XmwxVD2uDDvw6b/k4aNwfa2BSUbV3K7lDauBQkjLkOsYOnmrYhF5riVfMt9qP8HdVrjCmgpO/XzKFtPF70Uea0ZO1XnCmNaNfXbuZUHR6F8Hb9eMFHwueKdFDZ9mUw1FSyk1Vz971vXv+tIAhCE5xECToRqSPj+aTkCMpgkh5e1JmXONzGmZMoOXVUFeUi+6M7oAqLQsyASVBHxHAvWPHqT3mCPPXC/5h0Mcnervr4LrZcDE1pxyW+6hN7oa8uB2AMKJuzA4jgPp3TYizcm8hOMf6sy/niiOg+5/LFl+5N7mRO40fOQvYndyPns/sR3W8cZzRL13+L8Pb9EdFhIJq77714eQuCIPjQy9ubjjXKSbS+oE7xTi5esxBFKz9GxjWvIzSlrel2cgQp3/4HWt22AJrwaBSv/QpFK+cjfeZctmpsCPHzFhRIFocmmWn4hPoFqcRrLytn7ZntDTz1zDb3GFcWVsV/LeTMKfVEUuaUFmLmmVPFNrVo+YesO0n90JSZjB91pYXYeXP9nkiGUhAEwQc0lQWi4gCir6mwP2hAwwMqNVRqLbuBlP79PSK7nMnBJJXceWLVQY+np97JQuBDgRh9xkgYnAT8DVZ9g1RKpuwfZekpO0lQ0Dcs7DiW1dlv42gK9yZ3MqdEeOueSJ/1nMPbNc3Y9755jSAJgiA0Ek1lgag4gISfmpjNX/wKanIOoq7kJPdxlm5ejJiBk6EODWdNQMrGhKa2Q/7Pr+Lo8zOQ+fyFyJp3C6qObLXZt+IAIjQ/KMtIWXfK6lGpmPoPrZdK9DddT7fTdrQ93e+VV17Bh/+5Dv3U3rEj9JZ7Ey3OaJHmTbTN2PdeSt6CIAg+LAk+88tufLL2iFdOonNGO9alVGwfez+yxHSip8nYkr++hKGu2rRN7LBLkDByFv9esWcNTn77FNQRsVCHRyPuzIv5eir91ZXkIuPKF3mq1hw6/W5/ZFyzGzhozpBigDuOS5StU0OP7B9fxY1j++DZZ5/FF39nur0vCta87d5E/xtJY3mLudN7N1s1BDkiCIIgNFJJ0FVcPYla2z5q49IQ1ronu36QM0/FgQ0oWbOQy+CxAydDX1vF21F5POOql3n6liD/8ONvX4+SdV8jefJdaO4OIM0Z0iB1V36KAkYdVEia+H9oc14XqFQqk3sTDXit2p/Hn/H6AkvldiojU+bP2yoD4nvvPSSgFARB8AJU2nP2JNkQ7p5EzZ05yDO84JfX0OL6t03SPxRYwmDgoQKSD1IcQsJa9jAFk4Q2LhVhrXqg+tiuBh9HCF68qWX6/G97kRpj1DKlzzMNlpkWX3tz2fvaph8zKZKnpUnVwJc9iZ743vsqcxqISEApCILgxZIg4W4w6elJ1NyZo3TTYoSmdTAFkwqRnc5A+bal3FfpyCHEeF0canMONPg4QnCiaJkS1dn7Ubz6M9YzNdTVQhufhuh+4xE7aApnuctJU3LfOtSePMx/a+MzENNvPEvrqNQa0z6ttUxpMIYGvB5BT6cnxn2Fv2VOAxEJKAVBEJqoJGjO7KFtcO/47h6dRM0dQHQVRdwXaQ1NcjN6nVFOSK2FrjTfZjtdaQHUdryVm6MDSHOEAitaIFUe2oTcrx5DaFpHFvRWhYajrigbutI83o5+L/jtbYS364uYM6ZBHRrJ9yn49Q1UZ+1G8qQ7TPusT8uUPvdN3Ubhb5nTQEMCSkEQBD8oCX689iifUD0pm5k7gIQktEDl4c3slhOS2NKiFE6yQSEp7Vg7L6LjIFTuX4/a/EyEJLXmbWrzMo1C5/0n2DxGc3QAaW5QQEVZOn11BeuWRnQcjJQL7odKZZuZ1kQlIOOa1yy0TmP6T0DeTy9xJjxu+KX8WSQoo0f73Z9b6teBmD9lTgMJeVUEQRA8LAk2VBYksj+9D9WZ2232E95+ANIuecxr9oaKA0jskBmoPLgR2fPvRczA841DOfvXo+rgRkT3HXva/WPUbFQd+Qc5nz/IckJE6cYf2Fkn7syLLPbtCwcQOWH7H4qWaenO5ezclDByNgeT+poqqEJCLQJL8rmmizWkbUoBJS1OlIAyELVM/SFzGijIt1YQBMGDkiDRUFlQQROTzK4aNkLjp/CGvaFi+0j2cSTAXLT6M5RtWgxdZSkHufEjZyN26AzT9qHJbZB2+dM8qFO85gtApUJ4275IGH0VtDGW/Zfueid7QxxbaDwULVOyAlWFRaKuLB+53zyBuoLjUIWEI6rXaCSOuc401GUPXXkh/9RExtrVMqXsnxBcSEApCILgZkmQcKYsqEAl5uheox3e7o2SoLkDCLnfpF38aIP3CUvvhLRLn6h3G284gDgzCW8ujk2BMf0vMvTQeJCWKQX5RG1BFvfanvz6cUT3GYvwUVeyxzVlsPVV5UiZeo/dfZDbEjkwkWxVaEYXm9upL5Ey05KJDi5kVE8QhEaFTiQ7soqx+Wgh/6S/A7UkSJTbKQuSpaEjaChGX1Pp8HalJBhsDiDUb0pWlBToOjMJr9xO29P96P6C7zHXMjXUVsFQW42oXucg8bwbWHaKflIrR8Wuldyfa4+CX99iF6bEsTdaTHlba5kKwYUsDwRB8DnBVuJUSoKEK2VByviQvSF0dVBHxSOm7zjEDb+MfYS9WRKkbN4DE7vh4e93wh+8kz0Wx9Yb2M2EBKhJM1DwHeYao8pnN6r7KIttonqcjbItv6D6+G6LgS+ieN3XKPtnCeJGzOSsvTOPIwQHElAKguAzgrHEaV4SdKUsqE3IYAea0JR2rNVXsftP7lmk+6dMu9crJUHrwN1beOIA4s1JeNpPSrRRHFvwDeYao6RTSplGa51S0igl9FVlFteXbV2KomUfsjpA/PBLnX4cITiQgFIQBL8Q+7YucT46pSeLDfsb1vaGSlmQTqJUDiSoNEh9ZJTFqR1xBWdxkif+22I/0b3OQf7Pr3I2p/r4VIS17Oa2vaE3XXq86QDi6iS8ORSsHH/nBugripE87T5EdTvLa5PwgnNapqHpHVF1eDPqSvMRktTKtE1daQH/NJ/urti7Fvk/v4LIrmcicexN9T6GaJkGJxJQCoLgdYK5xFltVapzpyyoEHvGBRxQVh7eYhFQElOmTUd6SBXS0tKQnp7OP5WL+d8/7Mz3ikuPLxxA3JmEVyha9SkH6tZ4YxJecAxlxVvGheFYcTWiuo1AydqvULb1V0S062vahv6GWoOwNsae2qqj25H3/bMIa90LyZPvrncwjRAt0+BE3lFBELxKsJQ4DQYDsrKysGPHDuzcuZN/0mVPTjliLn7K7bKgxTanbBH1VaU2t51z9khUZx9ATk4O1qxZwz9zc3Oh158OaGPPvBgJo2azPzfJ/XiCtx1A3J2EJ2pOHkbp5sUsil286lOL2wJFHDvQoM/76tWr8fzzz2NXeTriBk3iDGVUn/NQvvU3nNTrWYqK2jkqdq9G7JkXsZZpXXEucr9+nD9BUd2Go3z3aov9hqa2Q2hqe59qmQr+gQSUgiB4DXdKnFQaLln3Lcq2/4G64hyow6IQltEJieNuYR9qX5c4zQNH8+CRfhYXF/M24eHh6N69O3r27IkJk3tjXsnp+7tSFrSGMnT2tqHg7qXHH7TJ4uh0OuTn53Nw+eWmLHy8+9SEvIfBJDG9bR1m9AlDWlo40qPUXpuEpwCw3AmBbHMKl77DwtjhrXrC+A5YEmji2P5MXV0dvv76aw4kN2zYwJ/zu26ZhrczjZ+ppHFzoI1N4f7Iir1/QRuXgoQx1yF28FTj/YuyYag2TmwX/Pqmzf5p6Mw8oPSWlqngf0hAKQiC13C1xGnQ1SH3y0eNNn99xxkHVqrKUH1iL/R8kkr2WomTAsfjx49bZBvpd0eB45QpU9CjRw/+vV27dtBoTsufLH1uGQ8SEc6UBSlDp9KEQKUNsXg+LCR+yi3HmZIgPYfU1FRUa6PxxcJjfB1JtxStms9Bu76yDJrYFET1GIXYIRdAHUKveQ6Ov3WNw9eFXHMSx9+KL/fr8Mp90zmo5/8rKspuid3e39HR0V6bhKcMF7UJtLj2TdNzsUbEsT2npKQE8+bNw8svv4wjR47gnHPOwU8//YTx48dDrVZj57x13M+sgxbxZ13OF3vQoFnb+3506jG9oWUq+C8SUAqC4BXcKXGWbFjE/VfpM+eyCLc9XC1xKoGjdamafqeTqCuBY0P2hvTcnCkLVh3Zirzvn0Nkj5FsQ0e9gRX71nIQSBlbEhZ3pSSoBO51JSeR/dEdUIVFIWbAJLZLpGCsePWnqMnej9QL/wN1ZBySJt1ps4+qQxtRvmM5B7MqlQra0DCc/+inuLUXOANKl+zsbNPv69at47+p7E6ZUnPMg0+6JKe3xJH4CS5Pwutrq1H4xzzEDJ7K2WxHASUh4tjukZmZiVdeeQXvvPMOKioqcOmll2LRokXo16+fxXbUP0vDcd4Y8PKWlqng38g3URAEr+BqiZPEv8lNg0qbFEyS4DeVvymr5kyJ0zxwtC5VK4FjREQEB44UME6dOtXlwLEhe0OFhsqCmrhUhLXuyZOwerKkU6kQktQaiePmcEBpTkMlQfPAvXz7Ms7kZsx8FqEpbfm6GNqfQY/y7X9AV1UGTXi0XXce8lmmrGFkpzOMj2sAtmRXoe1lIzFmjOPAnXo4CwoKbAJO87/X7TwIDHN9Ep4yvRR4xp15cYPvgauT8P5GY3uYb9q0icvaCxcu5IzyTTfdhFtvvRUtW9ofGKMWE1JaoOE4f9AyFfwfCSgFQfAKrpY4aZBFV1bATfssn7P9dxb8Dklph8Rzr+dSmgLtd/GWw4jdv8SpwJGCRvrd08DRGXtDem4kTF5fWTAkPh0p0+7zSknQPHDX1xjL7jYDQeQPrlJDpbZ/iK8rK+AMITmgmJebnelNpHJocnIyX+h1tge5IF3w5hqXJuHpuZas+4bdVdShEXCGQBPHbmyBfwr+Fy9ezIHk8uXL0b59e/796quvttumYA3JdpHSgjeG7DzRMhUCAwkoBUFoErHvOtqGy97fQR0ejaRxt/DfxX8tRM7C/yLjyhctmvlzKgz4z/NPoFun9o0WONZHU5UEzQP38Da9OauXv/gVxI+44lTJexdPSMcMnAx1qG22l6jYuZKzmFE9z/ZJb6K1aLUzk/DUB6qJSeSeU+r75OdD2Vy6vaKEr9PEpVhkuQNFHLuxBf4rKyvx8ccf48UXX8SePXswdOhQfPnll7jgggtc/p6QbFdydJhJmsqVz7s3tEyFwEECSkEQmkTsm9xiCMqyZVz1MpeMCcpMHn/7epSs+xrJk+8y7ZP6/NbuOIDerRLgDzRFSdA6cI/oMJAt7kr++hIn9q8zXR877BIkjJzlcD/UkkBZTPMssDd6E5UybkllrcX1zkzCUz9oXeEJZL11rc1+C359g3+2vm0BVOHRppaHS88fg4F9e2HAgAF8oT5AZzJvwSrwT/2tr7/+Ot544w1WA6AA8v3338ewYWb9B25Ajz+8Y7LT4vne1DIVAgcJKAVB8Bjr0qNTJc6QML4urGUPUzBJaKnfsFUPVB/bZfM4/lbhbOySoHXgTmjj0rg/kwJ2TUQsKg5sQMmahZwNjB042WYfNBVOAzs0+GJvWMrV3sT6yrgKzkzC03S6vrLESo/yCIpXzUfskBks/E5tEwqJIToMHToYGzduxIIFC1BdXc2Ljq5du5oCzIEDB3KQGR9vmRkNNoH/Xbt24YUXXsAnn3zCGUgqad92223o2LEjvAUFhaS0YHq/9+by4sPgQy1TIbCQgFIQBI9xp8QZmtDi1HW2J3varjbnQIOP4w80ZknQOnAv37kCBb+8hhbXv82anQQFliR0XrT8Q5YPoiDT4j47lvPPqJ6jnX4cT+0enZmEp4s1pElKgk6hGZ15eEuBHm/q4E54ZIpR97C2tpaDKgouafiELjS5TFPMBAVWSoCpBJtJSbaPF0gC/5ShXbZsGfdEUp9kRkYGHn74Ydxwww1ITEyEr6DeTuqxpbaIxh4sEvwbeecFQfCq/6+zJU6eSlZroSvNt9mfrrSA5W4Cxf+3sUqC1gF16abFCE3rYAomFWhym6a4a3IOIqJdP5tytzaxlYVUUUOP42kZ15lJeFewnoQPCQlBnz59+HLVVVcZt9HpuH9QCTAp2HzyySdRWmp0JWrTpo1FgEkX0tdsCoH/4jULUbF/HZf89TWV/H6S5FbcsEtMoveKwH9atBZffPEFZyS3bNmC3r1748MPP2T5n7AwY9a/saDgMVCn7AXvIwGlIAheObGkx2hxorTO6RKnOiwSER0HoXL/etTmZ7KMDlGbl2kUOu9/WscwEPx/G6MkaB246yqKeKDJGpJgYpSfp6jO2sNBS9yIKxw+RkOBu7tl3IYm4Z0VzXZWHJtKvzSwRZeZM2eapp4PHDhgkcmkwKyw0Dj806JFC4tyOf0kWR0qpftS4J9aEGgALar7SKhCI1CXn4lS8ng/sAEZV73Kw1V1Oj0ue/F7HP7gLpbLGjduHH799Vece+65Lj8/QfAF/nt0FgTB7zl06BD3r9ElM3Uo4gZOgkGldqrEScSPmo2qI/8g5/MHeSqZoElwmlaOO/OigPT/9WVJkO5L0jKKSw+JpFce3sx9kaTjaF4KJ9kgkmCyW+7uYTndbU7rxAiHz9GbZdymEMcmyaPOnTvzhTJ6Sun48OHDFplMGmrJyzMGfCkpKTblclIVcBTEuSPwnzL9AZvrQlt0Q96ip1G5fx23LpBO6LG6GAyfMAP/+fd16NWrl1uvgSD4CgkoBSEA8KdeJRKvJnHkzz//HGvXrmUNSHKbuXnqRDy9Ve1SiTM0uQ3SLn+a+/3YhlClQnjbvkgYfRW0MckB7//ri5KguUsPDatUHtyI7Pn3Imbg+cahnP3rUXVwI1sqmvclUtayfPcqhLboipCEDLv7pm32r/wO/z72Ha699loup9or41IJt2jFx5xJJsJadOP3jMrvCs5YPiZN+L8mF8emwJD0GekyY8YMC9F880wmlZWfeuopvj0hIcEmk0l9mhSweuJhbg6VxQmjBakRjQroPvl69OollpOC/6Ey0DdHEAQ0dxHk+qCS4DfffMNBJA0CUDmRSm6XXXYZB5OKVMssxf/Xi9qMSonTUy/vYPpcnPfSSosydtHqz1CbcxC6ylJjf16vMYgdOgMq9WnNQQo8cxc+jIRzb0DsINvpb4UJ+g345sM3WYLmjDPO4MCSsnk3L9zJ721F1j7kzL8HmphkduUxwMC9nPqqUmTMfsHUM0vBEy0kHFk+Jk+7D1HdznJ5En7OaMe9n42xmDLPZNLPo0eP8m0xMTHo378/ikbchmKdUeXg5LdPofLwFqRc8AAKfn2zXg9zOhXzlLtej9rC4yha/hG/ty2ufd3UDkK0TYrEirscD1QJQlMhAaUg+BmuTM8qt3siguyI8vJy/PDDDxxE/vzzz6irq8Po0aM5iJw+fbrdSVJ67qSdV+1FfZ8wrRpLbx8lWnZm+Dpwp6npH3/8Ee+99x5++eUXRLfohISZL/B2uV8+wrJPLW54xzRBTs47We/cgIh2/e2Wb82h9obq7H1ofet8i4AqUMWxqTS+efNmDjA3bN6Kje0v50w7kTXvFtQVneDfWeC/TW+TwH9k95EmD3NCV1aIY6+d1g6lgD3hnGsQ1X2ExePRnrc/Ms6v+4mF5ol8IgWhmYog26OmpoYDCAoiv//+e5ZdGTJkCJ577jlcfPHFLE1SH+L/GxwuPTQ1TaLYdMnMzMTN7y3DjlqAHq4qcwcLqpvLEWlJJL11L1QcWM9Tyo6sEx1ZPgayODZZUJ533nl82ZFVjPNfXe2yhzmhjohG6qVPwFBXg5qcA6jY8xcMNZVB52EuBC8SUAqCn9BYIsg299Xp2OeXgsivv/4aRUVF3PD/4IMPcqmzQ4fTfXHOIP6/vqcxA/fWrVujJLo19KcGgSgYshcMslC9rg61J4+wCLkrlo8IEnFsdwT+lYBSpQkxSTyR7FN4237ImX831FHx/Hd9jyMI/oAElILgB/haBNka6nRZt24dB5E0YEO9YRQ4zpkzh0va5JHtCeL/63ssAnfqXPJAOqa+wN3a7jEksRX39tEAj9KjSUEmXUeQ9miYG5aPn107BHERIQEtju2OwL8jwlt159eK+k2tA0p/FPgXhMD81gpCEOGKCLKzk7OKCLJ5xomCyG3btnEQSTI/JJVCJWzKQlIQOXjwYK/q2Yn/r+8ZFJHPTjkp428mTRyfBO7Wdo8xAyaiYMkbyF/8Cg/+UMaRJvSpB5Cgkq09GrJ8pGAy0Mu47gj81we9luZT3v4u8C80bySgFIQmxhURZHKPSZp0p8PJ2fD2A/hv2h/tlwYsSMiZgki67Ny5kyVPLrzwQg4iR44cyRPbvkL8f30nE1VcXGzMJqen49PbR+LhH3b7JHC3Lq/G9J+IupI8lKz7BuXbf+frQtM7c3BZsuYLFuG2+z81YPkYDGVca51QZwT+aRqevgRqM59yonz3n5zBDEvvHFAC/0LzRT6VgtCEuCqCTCfr6F62J2Sy2VOFRZpKYxQw0H4HnD0Rm1f8jKioKEydOhVz587F2LFjERra8HStNxH/X+/KRHVKjcZNN92EgoIC/PHHH2ifGuezwN1eeTVh1GzEDpnO/ZLktx2a2g6FKz7i27RmAuuuWD4GSxnXXCfUGYF/ssfMWfAQIruP4HYCqhJQlaJ8xzJo4tIQM9hozxhoAv9C80OO4oLQhHhDBNnh5KxBD3XXUVhw05WYNGkSB5X+gPj/ui4TRddQ1osClQ//OowOkTVYtfgPfPzWWyzI7cvA3bqMq6AJj4am9ele26rDW1jqxry066zlYzCVcSngp/fIWYF/TUwST36TY1T5tt9h0NdBG5uKmAGTEDfsYotp+kAV+BeaBxJQCkITQtkoJYCgEzJlGevK8pH7zRP1iiA7NTmrUiOq8xm45BIRQQ42magDZRq0vvEdqDoZp4J9Gbhbl3HtUb5rJWpO7EPC6KvtLoAasnwMpjIuBfWkC6vohDbkYU59lEnjb2lwv856mAtCUxEc32BBCECsp2drC7IAvQ4nv37cKII86kqTCLK+qtxCBNnZyVkqe1KmKlhO1sGEJzJRNF1NHYfuyER5WsatOrodxX9+jvD2/aGOiEVN1m7OvoV3GMgDN9Y0ZPkYjGVcX+uECoI/EhxNK4IQgFhPzyoiyFS6JhFkKoPRT5rwrti1kqdkHU3OUv+VvcyQIoIsBLdM1BcbjPZ/vizjKsERlWhpopyGcshOsOrYTsSPnIXUGQ9ZWD0qUOadWjkcZSeDsYyr6IR6ExH4F/wdSVsIQgCKIDs7OWvvcQT/kImixUDRqvksD6WvLIMmNgVRPUYhdsgFpolfg0HP733p5p+5B5FaIGjQgxQASKdQwZ5MlK/KuEjIQNoljzt9X3LVaXvfj82ujCsC/0JzQzKUguBHIsj80wUR5IYmZ+09jtC00ABOVVEusj+6A9XH9/DwRcK517G7TPHqT5H33XOmbQv/eJ81H0NT2iFhzLWIPWMa99bmfHafSUjcXCbKl1C5lcqu3iTYy7jUivDM9N7sR0/BsyvQ9nS/udN7Y85ox99vQfAX5EwjCE2EMj2rQJkngkSQzXEkgqxMzkb1tMxoBuv0bDDJRJVs/YMFq1MvehhxZ16EmH7jkXz+bdzuULl/HXRVZdx7WLb5Z0R2HY7kyXfyNnFDL2S/Z+q1VbLT5jJR+3NLffbcpYzrfqZy6e2jOBNLNBRYKrfT9nQ/yUwKgYIElILQRCjTs6a/u404LXpshrkIsiuTs8E2PRtMMlH6mgr72ejoRJ7OV6m1MOjqYKirtt0mMt64DXlnm1+vVmH+2qM+D47uGtvFK/tqTmVcReD/t9tGYtaQtmibFGmxmCTob7qebl96+0jePtiDbSG4kDONIDQBZIO4du1a1B7ZAlV0JxhUaqdEkJ2dnA3W6dlgkYkKb9ObHVTIvjB+xBVQR8Sg+vgulG5ejJiBk01uM/T+lm37HaEtuyG8VU/Oahb/uQDq8Gge1jKH9kuC5qRB6UvEp9197OmEnj95KiaMOw//e/heWfwJAY18egWhEamoqGALxNdffx2bN29G+37DYBj/gNMiyDaTs2de4vCxgnF6NlhkomhQJW7ETJT89SVO7F9n2iZ22CVIGDnL9DeVuvMWzUX+D8+brtPGpyN95rMIiU9vMpko8Wn3HEUnNLa2ANrSbAkmhYBHPsGC0Ajs378fb775Jj744AMUFRVh4sSJePLJJzFu3Dhc+cEG/HkgD3Q+bkgE2ZnJ2WCfng0WmShtXBrCWvdkeShyQ6k4sAElaxZyiTt24GTeRh0agZDkNjywE962L3TlRShe+yUL36dfMdemr1aRiWoMJyLxafcOcXFxKCkpaeqnIQgeIwGl0GxobA9pnU6Hn3/+mbORv/zyCxITE3HttdfixhtvRIcOHUzbndkxyeTn7S2CfXo2EDGXbyrfuQIFv7yGFte/DW1sMl9HgSUMBhQt/5Dlg8gjmzyew1v3RuLYG033DW/XF1nvzWEdyITRV9X7OI2B+LR7RmxsLIqLi5v6aQiCx8g3XQhqTNmTPblcbrTJniRGsgsICTfTidEb5OXlYd68eXjrrbdw+PBhDBo0iDOTl1xyCSIiIrzmltLcp2cDDXP5ptJNixGa1sEUTCpEdjoD5duWoibnIH9Ca08eQcI511psQ1qk5JdNPZcNPU5jIz7t7mUos7Ozm/ppCILHSEApBK14dEP9XXQN+ROTpdyHfx1m4WZP+rvWr1/P2cgvvviC/6YAkn4/44wzfO6W0lynZwNRJoo+d7qKIh6ssYaGrRi97rTuqMFOxlGvO72tGSITFZgZyj17TmuKCkKgIgGlEHRQoKZMoBINTaEqt5MLCPnvktYeDR04Q2VlJQeNFEj+/fffaNu2LR599FFcc801SE62zD7Zc0tRqM7ej+LVn7FriqGuFtr4NJ7ijR00xSXHlHvHdcVNZ4sIsj/LRNEiJiShBSoPb2a3HHP3IyqFkyRQSEo76MoLT123kntmzT8rdL/ofuNsHkNkogIP6aEUggU58ghBhSclZAos6XLfN9vYMo3kURxx6NAhHrKh0nZBQQEP13z//fc8bKPR2PoZW0PZUyXgrTy0CblfPYbQNGOAqAoNR11RNnSleRaOKaUbFrHFYsyA8zl7RQEmOabQxG9Yi67QqIA1B/IloPRjqL2CMuKxQ2ag8uBGZM+/FzEDzzcO5exfj6qDGxHddyxLRNElvF1/lG//nXUrI9r3h66sEKUbf2CbzthBlpP/IhMVmETExKNEE4vNRwul71QIaFQGEsQThCDJTFIw6C3I8sy8dKzX67FkyRLORi5evJgzC1dddRVuuukmdO7sOPi019d53ksrjfusrsDxd65HWMvuSLngfqhUtv1vVNrMfOFiRHQcxNso1BZlI+uta1m3MPG8G0zXkyiyTNP6J+bvPTkdFa3+DLU5B6GrLDVmpXuNQezQGVCpjYsSfW01StZ/g4pdq1BXlANotAhv1QPxI2dxD6Y18t4HXm/3kYLyU80Kvu3tFgRfI8sgIShQSshVR7Yi5/PTuo7mpM/6H8uv0In5+FvXONwXZYiSJvwf/vv9DgzrmIwoVPFQDWUkDxw4gP79++Pdd9/FZZddhsjISLfdUigbSl7cpCeZMHI2B5P6miqoQkItAktXHFMUtxSauhX8DwoOqFeX2isoq5x28aP1bq8OCUP88Mv4Uh8iExXIvd0qn/Z2C0JjIQGlEBSYl5AJytqFZlhaxGlPOcqoI+OQNOlOm31UHdrIdobh7Qfw33U6PaY+sQC73pzD2cmLLroIn3zyCYYOHQqVqn4/XmfcUvgxD2+BKiwSdWX5rC1YV3Cc+yOjeo1G4pjruLRJQYWzjimN5ZYiuA8FB9Sr64rDTEOITJT/05i93YLQFEhAKQRF+chax5EEo6O6nWV3e7K1i+412uZ6kmuh4I6kWwidASgIS8MtDzyBu2+YhdTUVK+6pRC1BVk8sXvy68cR3WcswkddyXaL1CenrypHytR7XHZMaSy3FME9KNNEwYE32zNEJsq/aazebkFoSppOsEwQvIRSQraG+hPtSavYo66sgAO5yC7DOCuoQPuN6jveK8GkPbcUQ20VDLXViOp1DvdBkrg1/aSsY8WulTzNa+6YQgM5KRc8gMSxN/P/RllNXUWxXbcUwX+JPbkdxavme2VfIhPl33hTHoz288WGo17ZlyB4G0lhCAGPeQlZIX/xyzDUVHKPIWUrE0ZfjbAMxyv7ip0rWe8vqufZFtd7u4Rs7WKiBK9R3UdZXB/V42ye4q4+vpszka46pjS2W0pzxxWHmE2bNuHiiy/GOeecg0un9cSjP+7iMqgrJXBa6FCZmzKTEkwGR2+3gkFXi5J136Js+x+oK85hx6SwjE5IHHcLC+Ervd2SkRb8DQkohYDGuoQMTQhn+SI6DOJeydq8oyhZ/y1yPr0X6TOfY+1Ge9BwjCY6EeFt+9jc5s0SsrWLiSY6iZ+jzcBNlNFthOSBqo5ud9kxpSndUlwhkK363HFhOnr0KCZNmoTu3bvj888/R1RUFEZ0Tm1QhF9BuZ0GcGRQI7h6u5UBvNwvH+XvdHTfcQhNacfHgOoTe7lvGkjm/dF+yUddEPyJwDhyC4KTJWQS+TYX+kbnIYjsNhwn5t2KwhUfIe2Sx2z2QWXlmuz9iBk81b5sz6kSsjcs5czdUggKcKsOb0ZdaT4Hhwp1pQX8UxMZB31FkUuOKf7ultIUdpj+4MJ037ntcMn5ExEWFoYff/yRg0mCgkIKDkyvy95cXsTYvC5JkawzOXNoG5nmDsLebqJkwyJeQKbPnMsqAPagzxvtd39uqXwOBL9CAkohoHGmtEuuJBGdh6Bi7xoOvhSNPwWa7CZINNyTx3HVLYX/7jYCJWu/QtnWXxHRrq9pO/obag3C2vR22THFX91SmsIO058mdSe/mYOymM5Y8eVTSEtLs9mWgmeSe6L2ikDO3Aq28mDWvd0k9WV9HCI3rNK/v0dklzM5mKRjFZW/1SHhNvsWeTDBH5EjlBDQOFvapd4jkJ5jbTVPcluXu7WJrRCW7thhxpslZMUthU40lKGM6nMeyrf+hpN6PcLb9OLhoIrdqxF75kUuO6b4q1tKMEimeDqpC5UGkWdfi9+ztehulkS3BwWP3siIC4HT202tL7qyAoSmtkP+z6+ibPvvfMwiG87Ec6+3aMcReTDBH5GAUghorEvIjiArQwq+yNbQHHIrIW/suBFXOLyvt0vIVMql7JtC0rg50MamoGzrUlTs/QvauBQkjLkOsYNPB4opMx4yOaYUHtxk4ZhiXiqnEw2VRP2JYJBM8fakbkp0mAzTBDHu9HbXkYQYl72/Y33ZpHG38N/Ffy1EzsL/IuPKFxGa2t60S5EHE/wN+SQKAY11CZkkdKjv0JyanIOo2Leey8XWPZKmcncPy+luX5aQzd1SKFhSabSIP+tyvnjimELKSX1bxSMjLgL+QjAEYsqkLkEtBkWr5qP62E7oK8ugiU1BVI9RiB1ygak0SYMVFASUb/udBeu10UmchY478yJTmVMmdYMbd3q79bVVfBNVITKuepkXmXzftn1w/O3rUbLuayRPvssnvd2C4A0koBQCHvMS8slFc6EOCWVvbGMmIBNl//zCPUsJZ//L4n7Uo1S+exW70ISYTVqa46sSsi/cUmhXmzOL0OuRJX4x2KIEYnk/vsjleke0nPMhtDHJ3ENGUkmlm3/mrDE5BlHmJm7YpaaTcVMEYsqkbl3JSWR/dAdUYVGIGTAJ6ogYlnUqXv0pD3WlXvgf3j7vh+e5ZSG6z3kIzeiE6uN7WHNSV3ISSRNu5W1kUje4cau3+5SEWFjLHqZgktDGpSKsVQ9UH7NVcxB5MMGfkIBSCHjMS8iRXYZy1rFk/SJe6VO2ksTK4866jA/g5pDtIfloR515icN9+6qE7Au3FH8bbFECsZj+4xHerp/NsyxY8jq0cWkcTBKFf7yP0g2LeDiKBNxJLoUCzJzP7mNHIBpUaOxAzHxSt3z7MpZuyZj5LEJT2vJ1MWR7adCjfPsf0FWVoa4wCxW7V3EQHD9ypnGb/hOhiYzlz2TMwElctpRJ3eDGnd5ukhAjrCXEjNfFoTbngNuPIwiNgQSUQsBjXkKOHTSFL85AJfC29/3o8HbKTpLen69O+DRkQn2B3ioJ+9Ngi3kgRtliuphTlbnD6BB0qtWAMjRlm39GZNfhbDOpENntLGS9dS0vEiigbOxAzHxSlxYohI1maHQiD1mo1FpUZxpL45E9RlpsE9l9JPfMle9aZeqDk0nd4MWd3m5epKi10JXm22ynKy3giksgyYMJzQ9Z3ghBAWXgyDnEm9D+aL++hIZMnpneG2FatV37SG9AwVB1nZ6zoTQg05R2mArlO1fwKZH6D5W+Q0NdtW2wFhlvDNZCwk5fdyoQa+xJ3fA2xs9C/uJXuC+XSuDlu1aidPNiFqwmj3iSeSHM7Tv571PPn0rj1pO6QvD2ditY26Oa93aTggP1dqvDIhHRcRCLmtfmZ5q2o7Ydui68ff+AkAcTmi/yaRSCAl+UkMnWrjHKxJQ1HN4x2Wm3lEAYbLEnmaJAwSP1GIa16g5tfJpp6Ih6Wcu2/Y7Qlt0Q3qonl5eL/1zAE6/kbd7YkinWk7qU0Y4bMRMlf32JE/vXma6PHXYJEkbO4t9DEo0T99TvFhKfbtpGyVzqyiyzTzKpG7y409sdP2o2qo78g5zPH+RFCkHyYNSvS0Nd/i4PJjRv5CgmBDyKCHTXtBjMGtoWn6w94vE+7x7btVGniZ1xSyEh8+LVn/GEsaGuloMxCrSUEr8z08WNMdhiI5liReWhTdBXlthM1lOpO2/RXOT/8LzpOvIxp/5J8+CMOJJfgQVffQuNoQ51dcZLbW2t6Xfzi6PrG7q9VBsHQ+eLLR6Xej5JP5AkYDQRsag4sAElaxZyZjV24GTOMGliU1G4bJ4xSE7vxNJURSs/YaF6Q22Nxf5kUjd4cae3OzS5DdIufxpFyz9E8ZovAJUK4W37ImH0VaZeY3+VBxMECSiFgKQ++z6C+5fIqtCOlaIjaNVPZW7KTDaVRqC1W8rMeevwz7EilB/chNyvHkNomnHqmXquqP9KV3ra2s2Z6WLC14Mt1pIpdsvdai0iu1ta0KlDIxCS3AZhlKFs2xe68iIUr/0Sud88gfQr5trIQc2ecydqcw/Z7D8kJARardbiQtdpNBqb2xxtSz81EWE2z7vgl9fQ4vq3jcMUFCh0HUYfNA4AqHxPQWbqRQ8j77u5OPntU8Y7akI4IChes9BGB5WQSd3gxN3ebjJYSLv0iSbr7RYEd5GAUggonLHvI/haJ4NJZT90kPYnm7+sokqWASKrtrwfX0BEx8FIueB+u37j1Sf2OjVdTHhzsKWkpASZmZk4evQo/6TLjuxyIHmM3e31NZWo3LeW3X4o+FKgoZycBQ8hvHVvJI690XR9eLu+yHpvDkrWfcNBmTm//b4M/dskWASDarX32sJ3ZBXj/FdXm/4u3bQYoWkdTMGkQmSnM1C+bSn3xEW068fDFRnXvM7i1TSpTkEy9VQW/v4eOyFZI5O6wYsv5MEao7dbENxBAkohYHDVvk/B0bSl6lRjO/UiUfnI31b8ymBL6c7lLG+UMHI2B5P6miqoQkItAktXpoudnTCurq7GsWPHLIJF698poFSgYK5FixZI7zYIsIy5TFTsXWuc7u5pWe6uOrodtSePIOGcay2uD0lsyU5ANJRgTXJiPGJjTwelvp7U1VUUcT+nNRQMM8pP+mypVCZpIaLywAaWF7KWT5JJ3eAmkHu7BcFVJKAUAgJP7PvMg8nZQ9vggv6tOCtEJ3J/HoZQBltIL5P8x6kvksq/dQXHWfQ7qtdoJI65jrNfrkwXE7TfpTuzMD611CJQNA8Yc3JyLO6TlJSENm3aoHXr1jj77LP5J12U6yiYpCwhlepJXN1eEE++6arQCBZ0NkdfUWT8hdoUrNHrTgdtjRiIWbswUa9b5eHN7JZDga5FCV+lZs9le+hrq1G0cj7LC0V1twz4ZVI3+PGmPFhj93YLgivIkUzwe7xp3/fx2qM8AOHvB2XzwZZa8vjV63Dy68cR3Wcswkddiaqj23j6U19VjpSp97g8XUxkFlVj2MgpMNRWISoqyhQY9u3bF5MnT7YIGFu1aoXIyEi3AjFz6RQKjimoUmwKFbSnArTynSt5mtr0/LP3cwAX3W9ckwRi5pO6sUNmoPLgRmTPvxcxA883DuXsX4+qgxsR3XcstDFGYeqTi57h4JFK3dSuUL71N9QWZXNvJUnDKMikbvOB5MGSo8Pw4Df/QA8VDLwkCpzebkFwBgkoBb/G3EfZHJqApMlZOmm3uPYNvq6uKAfH37rG4b7opJ804f8CwkfZfLCFAj4qE0f3n4DE824wDYNQVpKcZGpHXOHydLFSlv3615U4u28nxMfH89++CMQUSLORAmPrcrcyiEB6fGTRSFOw1GOpKyvkoJmyrrGDpjZJIGY+qUv9j+mznkPR6s9QtmkxdJWlPGkfP3I2YofOMN2HXvuybUv5vaHnHtaqJ5Kn3M39l+bIpG7zYkBcFY6/eyMG3/QCMuuiG5QH89febkFwhASUgl+j2PeZU1eSx/I4VPY1h/TdkiaddllRqDq0kSU7wtsPCBgfZfPJX6WMHdXdKAKuQLI7FLSQnzSVYF2dLibadeiEhIQEnwZiCvQeqCPj7dgwGkmZ8RBK1n+Dil2rUHhwE6DRIrxVD8SPnMV9lE0RiJlP6tLjkltP2sWP1nufuKEX8qU+ZFK3+XHPPfcgPToEvz04FZnFtQ7lwfy9t1sQHCEBZRDqMVIwEgg9gq7Y95lDWTg6sRv0etYzVCCnkuheo222pwlc6kGkaVwiEHyUzSd/yeOXJoZtXGSijDI6NEnM93Fxutj6cXwZiBEZs0/rS9qDMqvxwy/jiz8FYl6f1DUYoFWrZVK3GbFs2TJ89913WLBgAcLDw9E5PNxCHiyYjttC80U+tUGsx8gr3cRILj9SxohO8oGEuY+y+TRwxe4/kXHVKyj47a0G91FXVsD9hlG9zrEYWPF3H2XzCePQ9I6oOrwZdaX5Fpm6utIC/mmuz+jsdHFjDLYEi2SK1yd1VSokHlqKxDDLAR0hONHpdLjjjjswdOhQXHyxpVA+QcGjCNsLwYAIoAVwb+Gseetw3ksruVeNBiCsT9v0N11Pt9N2tD3dL1Cwtu+jSV8KIqkXMjTV/kStNRU7V3JAZd235+8+yuZewFHdRvDPsq2/WmzDf6s1CDvlMe3KdHFjDLYogZg3aSrJFJrUvWtsF6/sa1oHDbZ/9w7OOecc5OXZZuCF4OLjjz/Gli1b8OKLL3q1T1kQ/A3JUDYDPUbldio/UsaITvJ0gvRn7Nn3lW3+GXUlJ5F22ZNO74dkaiigCm/bx+Y2f/dRVgZbKENJFoo0LXxSr+fyNWVdyRUn9syLXJ4ubszBlmCSTFEmdZXvniuZV+tJ3VkDlmPixIk466yz8Msvv6BdO+cWSFIeDSzKysrw4IMP4tJLL+UMpSAEM3IkakZ6jHQCpAuV7ugkTydIf8Xavk9XWYKiVZ8iftglNhZ8jiC5GdJfjBk81a67jD/7KJeXl6N0y8/Q6bvz30nj5kAbm4KyrUtRsfcvaONSkDDmOsQOnurydHFjD7Z4MxBraihAHt4x2Sm3pvomdQcOHIg1a9Zg7NixGDZsGAeVffrYLnqCva0l2Hn22WdRUFCAZ555pqmfiiD4HJXBYPBeg5Pg88ykNx0X5k7v7RcnaUKv1+P48eM4ePAgDh06hG0nyvB1yWlnl/wlr7OGIUkEqTQhfF32p/fxUI4iG2QNBaDFf36O9H+9xLI09vj2pmFs3+dP/VZUIqOsBp2Izrz/UxytifBqH6Iy2NLYU+7O2mYqz5Fup8Eef5VMMQV6Hkzqkng8ZSr379/PQxskGB+sr1dzg8wBunbtittuuw1PPXVKeUEQghgJKAMEOrlQubraTE7GkR4jYTDoOVNVSmXiwhMssUOlU/J6Dm9lzHqFadVYevuoRjv5FBcXc7BIQaNyUf4+fPgwampOayW26TMMqokPmDKNWe/exBk5ZVKbOPn9szzNnHbxYzzFrYmwPGkff/s6QKVBy+sdD+/8dOtZfpOh/P3333HnnXfin3/+4RLZ008/zdqS9t53T2js990XgZi/4UkpurS0FNOnT8fKlSvx2WefYcaMGRZtLe5kdAOhrSXQaeg9nz17NpYsWYJ9+/b51CJUEPwFCSgDBBqoMZdgUfQYs94loWsVtHGpFgFlwe/voXTDIkT1HI2w1j058KIAk3oQ02c+y7I73s5U1dbW8qrcPFA0v1DGTYGcWTp06GBzad++PfeT6dUhJvu+qiNbkfO5Mbh0RMygKUg893rT3yTqnf3xnYgbcYVDGRr66Ccvewojhw3BiBEjuJ8tNbXxnUt2796Nu+++Gz/++CPOPPNMvPDCCxb9VsGcmZaeQCO0mLryyivxxRdf4LqXvsaSE5YWmu5AQ0T+3NYSiDjbfjAgthxTRw/BO++8g+uuu64Jn7EgNB7N78jdDPQYaRqaBlgiuw5H8uTTQt+R3c5C1lvXssA03c9VPUYKwCgotA4UleCRfKCpZEuo1Wq27aMgkaz8pk2bZhE4JicnNzjxqNj3haS0Rcr0B21up8ysvqaSA0ltfIbFbfQ/KuLfjkgM0aFn105YtGgRXnrpJb6OSlQUXCoXCm7pefoi8KEJ30ceeQRvvfUW2xtSMHHRRRfZvC7BNNhijUimGAkNDcWnn34KbdeRXgkmCfq8pESH+dX7Hag4035grqrxod6Afre9h/Om2coECUKwIgFlEOoxGnR1MNRV2wphR8YDKjVUIWEO9Rirq6u5/Owoy0jlOQVyWKGMIgWIFAiZZxkpQKKTpDemnBEZh8guZ9rcXrLhO/5pfRsF1OW7VyG0RVeEJFgGmub/99TBnfDI4x/z38eOHcOqVau47Eg/33vvPYQktUb6iIsQ0XEwqkOiT+UgPB+GoNf41VdfxRNPPMFBOjXs33rrrQgLO/2+eHOwhf9flQr/HtMJc0bb7yUVmp7jRVVYr2uH6uy9KFrxMaqP7+Lrw1p0Y8cje8NVVcd2oWj5B6jJPgBVWARLTMWPmg11aATfHgg2o8GqqlESmYFxr6yW9gOh2SAl7wBg1HPLeOVrHjCd+ODfCGvZDUnjb7E7nHLi4ztRe/IIEsfPQXirntBXl6P4zwUsN5N+5QsIiU83bRteV4q09W9ywEiDMcpHQqvVcobOPFA0/90Xln3WmVnSz3SEo6GcyoMbkbvwYSScewNiB012eP+lt4+0m5mlbMRdCzdh3ZFiqAx6GOxMiLszDEGv61dffYV7772Xs7k33ngjHn74YaSkpDi8T72ZEhWgc/Lbq1YBdJ6ToQ3/bmtZtmYdsj6+G5qYZMT0Gw8DDCjdtBj6qlJkzH7BQti+Jucgsj+5ixc+0f3GsfB9ybpvWCJLsYdsqgGsYMETVQ1zpP1AaA5IQOnnkB5j71O9hAqlG39E4cpP0PKGd1hCx15gVVuYhbxFc1GTc8B0nTY+HakXPWJxUmIMBgzJXIjO7dtYlKVbtmwJjUYDf+sd9ZT6TrLuDkOoQHZ6Kjw0oQuuPMv2xLFu3Tp2yyCpmEmTJuG5555Dt27d3P4fXv59L175fZ/TAaWCDG34J8riKffLR9ibvQV9tyNiTW5PWe/cgIh2/ZEy/XQvcc7Ch1GbewgtrnvLpDNa+s8SFPz8KlIveQwRp7zr61s8CY4J5t5lQfAFUvL2c9zVY6SSF01+UxYzvG1f6MqLULz2S+R+8wTSr5hreV+VCv997lW/7GVrTPs+T7IRBqhQozPg4Z/24tlX3sKENiruwaTSP2nRkYcv9ZIuXboUY8aM8ej50/N8cem+oNcibY5tLVWZOxDRYaApmCS0JMzfuhcqDqznnmH6bpNwPclokQ6puWh9dK9z2Lu9YtdqU0Dp7zaj/ghVAmhhaQ9HyhrFaxaiYv86VtWg90kbm8ztMnGnjtXSfiAEO2K96OfQEIg5dCBTR0Qjpp5SLpXEcxY8BHVYFBLH3oTIrsMQM2Ai0i59AnWF2VwWa+hx/IXGsu+jbISnpS1lmKa84zn44u9MHkQaMGAAl7lHjRrFkkCdO3sWwHnjeSrQfr7YcNQr+xK8YzNq0NVaeM4rcN+zro7bWIiak4cBvQ6h6ZafJ9JoDU1tb1GZ8HebUX+E2kqUnklzSFmj+K+FLMNmDZko0GtPASQfdzsPZaMBakvQ11Tx/mi/ghCsSIbSz6GJYgXSYyzbsoT1GHWlpyV46CREQWRdUQ7rMVJvFZ14Es651mJfIYktudytNPs7ehx/w9dTzpSNuOHaa1CydanD+7Wc8yG0MclODUIQ6sGXInLXWowdNoB7TdeuXcu6dARlLc0nybt37+6Ux6+jrImjjIny2ShZ9y3Ktv+BuuIcXmSEZXRC4rhbOIMiWRP/shkNSWzFklf0fVapNab3kK4jqE+SRrd0Zcbvvybato+Z7DerM3cElM1oIKhq1KesQZi3IyiEtuiGvEVPo3L/Oqh7jHJJVUMQAg05uvi5Vh49FoUatFbWleZT+hGFS9/mizXH37qG9RjpgMcY7GQd9To+WZmjOvU4/owv7fsoaxDdbzxC2vS1usWAgiWvQxuXZgomKVjPXfAgD0IkjLnGNAhBPavKIASh1mgx8eGP8eXNo0zXnTx5EqtXr+YpcrpQGZxklpKSklgDk4LLkSNHon///jwQ5UzWpL6MCU375375KC8govuOQ2hKO9YjrT6xl4e0gGRT1kSGNvyjrYUqCQVL3kD+4lcQO3QGf4dpwaArK+TbDXU1Fj8V1yhz6Dr9qdsDwWY0EFQ1GlLWcIQ2Po1/Gr9v0n4gBDcSUDrAH/xz9+7di08++QSaqm6oC493Wo/RoK/j68t3ruR+LIXq7P2c5aSJUHPImSQQMhfe8lG2l40IadGNL+ZQP5uhttpCy7JwxUdQh0cj7fKnTb1rJCpPgxCVhzaZ+tZoMnxDZplFNoKmuS+44AK+EGVlZfjrr79MAeZDDz2EqqoqFn0ngXMlgzlkyBAcL9W5pEVKlGxYxCfB9JlzTy8yrHBVi1TwPubtJjH9J/IigRYp5dt/5+uorE3BZcmaL6AONS4clLI4ZS+toevUdsrmDbW1iMi8ZfuBObQIpyAyuu9YhKa2c3hfmnHl76Fej9rC4yha/hFLtYW36W3RfvAIJKAUgo/md7TwtoDtX4e9KsVCYtckcE1+zuvXr0dcXBz6Xf0kMtUJLukxhrfrzyckfU0FItr35wxH6cYf+EQUO2iqaTv6H8nmLlCg15iyad6y73OUjSDKd67gvUX1MGYZXRmEcCYbER0djfPOO48vilvKxo0bTQHmiy++yLJCISEh6HfNk9Ak9nRei9SgR+nf3/Nnwhhw6oyBhp1MpmRNmhbrdpOEUbMRO2Q6t61QiwIFMLSQIbSJLU1lbULJXJpD5XDl9voex18Wzv7afmBxPVnYlpxE2mVP1nt/fXkRjr02y/Q3yT8lT7mbKxoK0n4gBCvyifaCgC3J2tAksrtSLJSVIts9ykYuXryYr5swYQIWLlyIyZMnI7O4tl49RnukzHgIJeu/QcWuVSg8uAnQaBHeqgfiR86ykA2i/4GCrkCDTm4UANFK35PMir1shFIurti9GmGtupvKVq4MQriTjSAheMpM0uWee+6BXq/Hjh07OLh8O6sFdGYaQQ1lTGrzjnJgQbfl//wqyijbpatDSEo7zmSTVqG7z1PwXVuLgiY8GprWp98PWshQcKJ8d0OT21JfBWqy9yGq+wjTdrRoqMk9xK5Y9bW1NPXCOVBUNVxR1iBoYDL10ie4JYGOBxV7/oKhptJiG2k/EIIVCSi9IBnjjhQLBQt//vknB5EUOBYXF+OMM87grNQll1xiIXbdOTycD+aO9BjTr3jG5jp1SBh7WDvysTbXYwz0Uqe79n2OshEEla+pdGVe7nZ1EMLTbATZV/bu3Rvtu3THc48scSljUleQZcpeU4k+adwt/Df1W+Ys/C8yrnyRg2BvPE/BM+g1V2xG7VG+ayVqTuxDwuiroTolsq8Oj0J4u35sMRo37FJTxrxs+zIOYKKsAkrztpamWjj7KxUVFewKRpc1e+h7Y8wCu6KsYb64jGjXj3+P7HQGwtv2Q878u6GOiue//V1VQxA8Qc4ePpBiqc8/V+mLnD9/Plsctm3bFrfccgtmzZrFPtL+oMfYXLCXjbAod6u1iOx++sTs6iCEt7IR7miR6murjD9rKpBx1cvQxhoXKJSZPP729ShZ9zWSJ9/l1ecpeG4zWn54G4r//Bzh7ftDHRGLmqzdKNu6FOEdBiJm8OlWFYKqDdmf3I2cz+43OeWUrv+W72veO23e1tLYC2d/oK6uDpmZmaagUbGVVS45OTmmbSNbdkHKrBdcVtbQRNhflIe36s6LTQr8zQNKf1bVEAR3afYBpatSLFQKpSxP+bbfUVeWD210EqL6nIe4My8yyXxYS7HQdC/1RVIgqfRFkvc1BZE03UuZKGf1GL3p3GBPj7E54ShLQANOlfvWcu+pucC0O4MQ9T2Ou8/TmYyJ8lzDWvYwBZPKAFFYqx6oPmYrHSVZk6aDehSprKyJSaLUNA/lsDh2fBoHjrFnTDMdXxTC0juxtmzR8g+5h1cVGoHoPuchftSVFtspbS2NuXBuTGgQJjc31yJINA8ayeaU1BQIkuciBzCyjqUF/Pjx4/l3xVY2NjEFfR77zbSAc1ZZg9pIHD6/uhrTlHegqGoIgjs0+4DSVSmWvB+e5946OnCHZnRC9fE9KF41H7qSk0iacKvx/noD7v16C6bFHeMg8pdffrHpiwwPt913U+sxNjccZQkq9q41Tnf3PF3udncQor7Hced5Op0xiU4yPueoeJv9aaLiUGvV7+mN5yl41hNsbGtRIe2Sx52+X3jrnkif9VyDbS1hWo3LGqYKJDV1/J0boK8oRvK0+0zl9MbUMC0tLXUYMB46loXasHiuEtD3IAZVaN+6BQeJgwYNMgWMdKGKUFgYKXk6xrz9wFllDRIup0jReuitfPef/PqFmfVdB4qqhiC4SrP+VDsSsHUkxUL6fRW7V3HPUvzImSaZD01kLErWL0LMwEncl0YZgTUHC/Hlu3diQMcWdvsi/VGPsblhbxiCKN+5nLM9EZ0ttRldHYTwVjbCHS3S+BEzuWTP21tBgajaqlQuWZOmx5dtLa4unM2h9gpaYFnjTQ1TUjigTKKjoJHULxRoMd62zxBE9hkHfY9ZSFFTQHvaGIB+i0qMRE83p9OV9gN6HzROKmuQPi25k0V2H8Hi9JQJJZm28h3LoIlLQ8zgKbwdLdnO7uL5eUAQ/JFmHVDak4ypT4pFGbqI7DHS4vrI7iNRsv5blO9aZRp0UMGAm1/4HC/Ptg0yPIUylf1bx3P5e3NmEdQqoL5zkDN6jM0Re8MQuopinqiN6j7SJtvg6iCEt7IR5s/T2YwJPbeIjoNQuX89avMzTbIltXmZRqHz/hMs7h9aU4xffvwOEydORETEaccfofHwVVtLVa3rGqYKpGxQunkx4oZfiuJVn7qtYUpDiNnZ2Q4DxmPHjvE2BLUAtW7dmjOKvXr1wpQpU0wZxvCklnhhdTZW789DrY+m05X2A1egVgWyuK068g+3Q5EWsDY2FTEDJiFu2MWm1hn6D39+5QGcE3cn27EKQjDRrANKa8mYhqRYlN45a69d9tk95eVq2hYqbMm17bXzBEeacY6CSVf0GJsr5tkIZaKWpIGsy92uDkJ4W+NTeZ6uaJGSHSSd4HI+fxAxA439lqRFqo6I4Z5fBVr8qLJ34cILn0BMTAymT5+Oyy+/HOecc45dxx7Bd3izraVV/iZcOGA8Hv9pt0sLZ3MKl77Dn6nwVj1RbOd2cw1TUqqwHnhR/qYBRJJHU6BqjRIkDhs2zKIsTdakpL1qDU+nz/f9dPrp9gP7qhr2lDUok5k03qik4Ah6rbrGAblFWTj77LMxbtw4PPXUUxgw4LR2rSAEMs32bGFPMqYhKRYqZRA00BASn26TudSVWZYXvSXF4oxmnIKSraQM5jPTe6Nr+umhEqHhbARlH9WR8ZyJtIezgxDe1vh0J2sSmtyGHX3ouVKvHFQqhLfti4TRV5msJJXFz48v3gP9w7Pw+eef47PPPsNHH32E1NRUXHzxxRxcDh061Cm/cQF+0dZyQZta/O+5R3G97jD2dZvp0sJZoXz3alQf340W177JPvD2oP1+vHQjXvnXCBQWnu4tJrcnJUAcO3YsD7yYB40k6u8KjT2d7qv2g7evHYVWd2/AN998w85YAwcOxIUXXojHH38c3bpZOnUJQqDRbANKd6RYqISoiU3lUhHpPIamd0J11h4uN1JvnaHW+/65rmrGKTdvPV6MKa//GXSacd7GOhuRMft5jwchfKHx2VDWxJ4WqXkA7NTzTI1hZ57//ve/2LRpEweXdHnttdfQrl07XHbZZRxcUhlS8H+b0V4RH2L2Ndejze0X8GLCFdcXfW01Cv+Yx1JFNGnuKKDkbSMT8e8770HXju1MASNlIL21AGmK6XRfq2rMmDEDU6dO5aHNRx55BD179sS//vUv/v5RhlYQApFmG1C6K8WSetHDyPtuLk5++5TxSk0IZ3yK1yyE6pTPbn2P4wrNUTOuKXj4/K4Y9/KqUy3z/qvx2VhapBQIUOaELnPnzmWnHspavvXWW3j66adZbJ2CS7pQoNlUBLv3tKc2ozNnzsTek5X4OFflsutLydqvuPUj7syLG36iKhUuvOomn2iYmsu60ZBL0YqPuQeYCGvRjY+9oWkdbGxHy7b8glIKnAtP8NBRaHpH7nsmXUhnp9N9rapB7SRXXXUVL9Lou/Xkk0+yPvHNN9+MBx54wCtDnILQmATP0bcxpFgiYhCa0hYZ17zO1nYkB0FyGxRoUvkzvE0vr0mxBKtmnL9BciQ3zrwQBUVhiDvvZr/W+GwKLVKNRsP9XnShTOWSJUs4uKQSHZ30qP+NAksqjVOJ3Nc0R+9pd2xGa2truX8xJS0dyHVt4UzHO9LBTBx7I9Shzg1o+UrDVJlOp2AyZ/49bD9J7l8GGFC6aTGyP7sPGbNfsLCTLfzjfZRuWISonqMRM+B8Pk5TgJnz2X1In/ksNK26OT2d3hiqGiRj9O9//xtXX301XnrpJfzvf//De++9hzvuuAN33nknYmOlbUkIDFQGUoVthtCBudcjS/iEVHVkK3I+f6De7esTr608sAG5Xz6KxPG3IKbf+NM3GAx48Sw1zh01gocdXFmVD/v3qzg2/z67t6fP+h/CWp7ut6k6tgtFyz9ATfYBqMIiENVtBA9kmJ8MwrRqLL19lEx4m0GC86QNum/fPvzwww/YqsvwWjZizuhO8BWeZK699TzLysrw/fffc3BJQSYdRsaMGcPZlgsuuMDrJ0FX+oiV24Pde5pe8xMnTmDPnj3swKX8pAsFkyTmHZLaHi2uftW0cM569yZeOJu7tpz8/lkOutIufowXzjSIU521G6mXPA7VKTke+jvv++eQOPZmHj7TxFFJ+/Ri+adbz/J6hpIWD+e9tJJ/z/3yEWM/5w3vmCam68oKkPXODYho1x8p043Hb0oAZL5wMbcnpVxwv2lftUXZyHrrWh5OSzzvBr5u6e0jnW5JaczPX35+PlcGXn31Ve5Fvf/++zlrKeoLgr/TbANKYtRzy1hegqRiqo/tbFCKxV4DO/Ua0cpZV16IFte9ZZKSIQwlOTj6xjWc5SGf7tGjR/PkLGV16js4zJq3Dr8vW4YTn97PB8DQjC4Wt0d0GGAqV5H+WfYnd7EsjDJ1TNkFsthLu/hRm145b2jGBQNHjhzhYQGaTCXh+X79+tn0rPqzxqc/PU/SCPz666/x6aefcnmcdAInTZrEmUuSIXJHxN+b/2ug9xGXlJTYBIzK7+XlRgcWOsYo7i9dunThC/3eql1HjH1nm0sLZzqmVGdur3e71rctYI94gkLO7Y+M83q7wSPf7zApMBx94SIOZFOmWS6yaSFfeXgzWv/7c15A0/E48/kZnJlMHHuTaTsSHs988WLEDpmOhLP/xZ+NWUPacubXFdxtP3CH48ePcyWAspXp6enc20wlcnsT8ILgDzTrgNL8gGWP7E/vY302cweJk4ueYUcUKnXrqytQvvU3Xv1Sb2WE2WQwHbBmDmmDy7uG4I8//sCyZcv4Qiff0NBQnHnmmRxcUpA5ZMgQvs58Va4c/M2dKeyRs/Bh1OYesghmS/9ZgoKfX0XqJY8hor2lJIUrq/JgZceOHSzZQa/5b7/9ho4dOwZkNswfnyd5Ji9YsIAzl1u2bOFMJQ0gUOaSPusU+DRFNvausV38uo+YhL0pq2gdMNJPc69pCizMA0blJwWTyjHE04Uz2QRa61LWnDzCjmCxQ2ZwdSSi42CoNMYAsm1SJFbcNdrrr4nyvIkjz01jbdjkSXdYbEPHY3IuM6/anPj4TtSePILE8XNY7oj+n+I/F6Dq6DakX/mCSaHD0+fdWD28+/fv52CSBuQ6derEQSa1mDhj2SsIjUmzDijNSyrOBpTFa79C2bal0BXncu9kWKueiB9xhU1juL3gjYR7KZhRAswVK1agqKgIkZGRGD58OAeYBe3H4OutJ1F+6B9TQElBIWldWnv5UkCb+fJliB08FQmjr7bo/cx8+XIufSdN/D/T9e6uyv0Rdw/mf/31F84//3wWTqbMZEZGhl9kIzzBX5/nrl27TDJEBw4cQFpaGjtGUXBJGfuGpoApM+nNftG503s3aR8xHWqzsrJsAkb6SVqNit80lTmtA0b62blzZ8TFxTXKwtkaRwtcXx1TSNat96mWJCJr3i18XKPnqBwH6e/jb1/PtrfJ0+5HVLfhfH1tYRbyFs1FjZm9qDY+HakXPWLRa+mrzKqv+Oeff1hq6Mcff+SKCg3xUMuOyHkJ/kKzDiiV8nJ9Arbu4Gx5mU4glMVRspcrV65Eu1s/QqkhzHQAJ51DcmGBSo2w1j05cAzLMGZaqo7t5HJ78tR7LawAiez597BdWsZVL1tc76tsQmPg6UAGBZCULSMhYeqZjI+39bkO9Ilif3yedIj5+++/ObCk7CU5ppAuIQWWVBbv0aOH3ewrTbRXWw17OPKepqDIXpk2vP0ApF3yWKP2EVMbhZJptC5Vm5eo6TUwDxiV32mR480gwZ2FszX1VUx8UfXYkVWM819dbfqb3HoKlryBqF5jEDt0BtuP0mehYs9fgL4OSZPuRHQv43GN2o9oMIcqNqS7qisvQvHaL3lRnn7FXIvpdl/0fvqaP//8kwfi6Hxx1llnsTj6iBGWx/9gOn4IgUOzDygdnbg8wd0TV0llDfo+9pux3+nYLpRu+BYRHQax7zJNlZO9o6G2Cukzn2MZDBIezlv0DNKueAbhrXvZlIJIcL3VrZ9YXB9oq3JvlXYpmLnyyit5Rf/FF19Ig3sTQYuo5cuXc+byq6++4uCrb9++HFxeeumlJg0+ews98p7OepcGKlTQxqXaBJR1Rdk2AvPUnhLRrq/X+4jNS9TW2UbrEjUFiZRdpJ9K8FhfidoX0Ov554E8k06tsxI8lYc2saVsTdYe1OYf4ynrVje/b2GkQAtUbwfpm48W4oI311hcV7jiY+4PpwCSCE3vjPAOA1Cy5gu2IyVHHxrKOfHB/yG8dW+eUlfggaT35pyq5lxluv7bm4ahf5sEBBp02qZhOAosN2/ezMc1ylj279/f5X01R+UEwTcETlThIxKjQnHDyA545Y/TtolNJRmTWVhp+jKTXhpdTHQegshuw3Fi3q0oXPERZ10MdUYhdZXGtkmbrtOfut3bYuuNiavC7vbs1k6u/Q7/93//xwElNbiLnWDTQZk5mgany+uvv46ff/6Zg0sSdL733ns543Ll/92HVXa+jg15T1NGSslS2cMV72nzErW9YRjzEjW5vigZRmpbUX6ni79IvtDiauRzy/h3VyR4ynesQMXuVQhN68jBuTUUcDgrwePoNaa+W2qPoMvOnTv5556cckTOeNxi24RRs3mohvoj1WFRPCRJx0JCm9jSZClJtyecc63FfUMSW/L/pQTQnsq6NTWUwR4/fjwPFtLC7D//+Q9XXqil5LHHHuPPnjcW6p76ogvNi2Z5Zq1vReYLAVtnaUjLLSShBSI6D0HF3jW8Elc8xRWPcXPoOrWV57izj+MveEvYvXDFatx111149tlnpd/IjyD9vWnTpvGF9EAXLVrEmeSP1xyEJrKjW97T9L2ghZYj/URz72l7JWrz4NG8RE2LEKVEPWXKFIsytbdL1L6gqlZnOs7RcA0dO9Jn/88kwUOajSTBQ1lLRYKHIPmxpAm38gAOTVTTcI45OgOcCtLr6uo4o2sdOO7evZslqAhSAyD7we7du2P0eT3xcaXtfjTh0dC0Pv3eVR3ewoGxEgTrK4qMNxjsHOPos6E3LgIIeseopBvI0GAODehMnz6d7VLJdYdaSGganBZprVqdXhx4e6EeyMoJgm9oVgGlK6VTV/CWFIszq2VtbDKgq+P+SCVjoCs77aGroCsrsJtRcPZxmhpvCrtTZmPQ9N5+f9JvzpBO66xZs/hC0726U9O9rnhP1xZk4ejzM/j7oY6KR0zfcYgbfplpGpmg7/y36/bg+A8v2S1RU3BIweLgwYNxxRVXWJSoA1muhRbQyjGvKnOHUUvyVDBJaKMTuW2m4sB6nvhWAnJtTFKD+zYP0quqqvg1tQ4c6TpqEyAoa0tBDzkuUTBEv1MQ2bZtWwsVgBVmU972KN+1EjUn9nFfuaKJqWQqy3eu5P9RgbKyVPYmaTUFGlYLpNaf+qAFzzXXXMOf2TfffJP7KsnWcc6cOaxjmZyc7HcObNKvGXw0m3fP1RWZM9jzz/UE+kJRyFPfM6M+McoukM1jaHJb9hCvyd5nMZRD2cma3EOItCM35ItVubcPDIrdWt6PL6J8++8Ot2s550NoY5Ib7PMinLVbE5oWmu6lqoHFdU54T2sTMlh7NTSlHfS1VZzNpKENCjJTpt1rsW2RLhSbtu5g72kqvSvDMNTn6IoBQSBB1RjlmEfHB6W6YQ4NrVAwTiVjc+OEhqD9frb8H3x65zTOQpKaBUFT/RQo0sDI9ddfbwocnc3oUt+eMp1OGeriPz9HePv+UEfEoiZrN8q2LkV4h4HsN27uXR/erj8fN/Q1FYho358X3KUbf+D/OXbQVNOxm5QPgg3K8t5+++0cXL744ot4/vnn8e6777LjDjnv/LS7sEkd2KRfM7hpFgGlt7TsfC3FQkEYfaEUzThrr10SHK7Yt55X3rQiV4VHIbxdP5TvWM4+tYoOZdn2ZTwZbk+/0lurcl8eGBS7tZj+4/n/s8SAgiWvQxuXxsGkM31eBO3Pk14voXE4kl9u8Vly1ns6eeK/Lf6O7nUO8n9+FWX/LEH18akWARIFMx9/+3PA9BF7O0gPSWyF6qw9xrYZMwkeuo4gc4QwFx+jJjQWUyZPQ58eXTlopEtiov3vorPQsYP69ggNZUrVah7KoQyqNj4N8SNnIfaMaTZyaikzHkLJ+m9QsWsVCg9uAjRahLfqwdsrpXEKUunYHaxQFphK3pShfPrpp/ny+ocLEHf5/+xub089gRZm5VuXomLfOtSePMx/k04pucFRppded2cX6tKv2TwI+oDSm6VT4t/ndML1Izv6LDWvrMqzF82FOiQUYS27n5ryzkTZP79wFoGcHhToIJn9yd3I+ex+k1NO6fpveSVvXvJhDAZ0inNNWLqxDwwUqNK+Cfrf6WIOleuo3B/V42yn+7zcGcgQmgbr/l5nvKcdEXvGBRxQVh7eYpNxC5Q+Yl8E6TEDJrIET/7iVywkeJTWGWXYzyVUKtxw10NeDdJpIUrHDurbQ0IG0i6xHNJxhDokjIeN6GIPZdrf344DvigBU6mbspS33XYbLnx9OU7aKX+RekLxXwuhCgm3qYYV/PY2wtv1RcwZ06AOjeRqUMGvb7AVJ4nMO7NQl37N5kNQB5QU/Nw3bzFOLp+Pmuz9rEdGARnZFNK0YGRnyy8BlU1LNizisillAENS2rIzRGSnwaZt3lp5EBcObO2zgFJZlUd2GcqZx5L1i7h0Q9mZyC7DEHfWZTycY17iSbv0CRQt/xCFv7/HupXRfc6zkU9hVCos3ZuPQRf/H+6eMZw9l12RLmmMA4N5r5c9yneu4BxoVI9Rpuuc6fNyNJAh+Bfm/b3U81a2ZQl7T+tKC0zXUzaNsmt1RTnsPa2JsB8YaKjfmDItVaX1Pk6wYx08x/SfyEEEZfuUlhKS4KHgkiR41KHuWWX6IkinhSgdO7ypE0z97rRff6CxSsBVofHIUSXa7adypJ6giUpAxjWvITSlrem6mP4TkPfTSyjfthRxwy8FElrUu1D3l35NoXEI6oCSVk7VhTlcIonqPYbLoZTdqtizBie/fhyJ42/h9D1R8vcPKFz6NluKRZ99JQx1teyIc/KrR5FywQOI7DqsUUqnplW5eipiB01x6j7hrXsifdZzTm2r0oYgr+M4XP/yxyylQ7021N/Urp3jYYfGPDCY93pZY9DVsc1aWKvuXPJy53mQm8wjkIDSXzHvI9aV5nP2jL6XdLHm+FvXsPc0WQbagzIshHWpPBime13BXvDsjASPNx7HU6iqQQtRbzomuSvr5k0auwTsaKFen3oCfW/stZmQ3icFlFQ1o+SGo4W6N6uD7vRrCo1P0AaUSuk0rMMgpHUYZHFbzMBJOPHhbZz9UwJKatoOzeiMlAv/a2oYp0zfsdevRNm2300BZWOUTn2xKj+NynRC6Tt0AN5442k888wzrGl24403si2htd9yYx0Y7A1kmEPlFlpBm5e7XYWsCam0JNOE/ol5HzFVCEiwumHv6QrWXaXFkrm+IZVxFbccc4JputeTYb+GJHhcwZdBOlU1aCHqjWOQJ7Ju3qIpSsD2FurOqifYPJ9yY2uEJjLW4UJdGayk7yllwqk/t+bEXuirypA08TZE9znXZr8lG39A6aafeCFICgSR3UcgfsQsU8ZcBiv9n6A9qtZXOqVmYhroqM7eZ7qOysokfms+fUhDLuqQcKhCQhu1dFrfqlyxQLNH+qz/mXrFDAY9yrb8glKakC08wf0x5K5DwzuKYPruqD5Yt30//vz1e5aamDp1KntcX3fddZy5bNGiBR8Y7n7lcxybf1+Dj+nMtHV9BwbrXi+75W61FpHdbYeNnCXQhN2DHXt9Y0ofMbjN40yb+5Rs+I5/KrfRdyLv++cQ2WMkZ0y4CrFvLaqP7UR0v/HcFqIQrNO9zgbprkjwuIKvg3SqaiRHh5kCMVcW296SdfMGTVECdrRQd0Y9wRpqNyn9+3seigzN6OJwoa4MVuorSng6XxObgpDU9qg+aj/TXLjsA5Ss+xqRXYdzZY6yn6Ubf2SHOKV3VgYr/Z+gDSitV2T6mioY6qo5m1G5bx0qD27kFZBCeJvenPqn0ndkpzNg0NWgZOOPvL116bkxSqcNrcpjBk62+EIr0ikK5GVbumERCxbHDDifV4YUYOZ8dh/SZz7LPTP0BX1iyQF8cs3VuPrqq9lv+e233+aM5aOPPspWeIZRc6A75c7Z0GM6M21d34GhosrxMACtdCv3rWUZEHP9PHdoTgMZ/khDfWPpceGuBQxxqexzX7F3LfSUPVGpuE86cdwcDijN8afp3sbU4XNHgocg+TGa8iVqC0/AUF2Ooj8X8N+hqe25D93bQbqj14WOicM7Jrtsw+otWTdPaaoSsL2FurPqCdYU/PoWB3mpFz1sMV1vvlA3H6yk80CrWz6BJjoB1Sf2Ifuj2232WVdWwLMLdK5Knnyn6XptYgsU/vY2f/7ocyaDlf5PUAaU9lZkhX+8xwEVo1JzdiNx7E2m2xPPvYFXU+b9WnSwTbvsCZtJ48YqnSqr8v98tx21ZElhBp1A7ckCKaUMWn3Sas/8C0q6lFlvXcvDPhRQWn9BBw0axJfnnnsO8+fPx9IN27HlYAH0pw7a9T2ms9PWhPK4U2ffgOKje3DixAm+VEUko8XVr9q9DwULPN3d0/1yd3McyPAnnO0bO1FcVe9+0q94xuLvkPh0pEyzn0H3t+neptLhc1eCpyb7ADvrmKP8HdVrjOlE72mQ7srrQgtR0/Z7c/lYbGgEWbfGGhB1pL+rTWyFltef7nN0tgRsbwHtjnpC8bqvWTUhbsRMnjVw9Djm1UFqQ6Fgst7nd3w3uxhF9RhpcX1U95EcUFL2XHl9ZLDSvwnKgNLeiix28FQOqKjRnwY7qCQMM8tC+nJrk1ohKiYZEZ0Gs44jldZOfvMU0mbOtZisbszSKa3KNxwuwNebjtvcxr1jIWE2JwEaXqFsrCYq3uJ6TWQ8B9MsYKxcZ+cLGh8fj1tuuQV53+/ANio9OvGYrkxbG3ekwyF1K/RqUYqBAwey2HFyeks8usO+sHv5zuU8wU7Wk57Q3AYygtlYIJCme5tah89dCR7qdbPX7+atIN2T14WOWVQlCgTHFVcGRBlNCJIm/J/FPhSdYQVnS8DWC2h31BMog1207ENE95+AeJrurudx6hustIdiHazSWqqfKucpWtQoyGClf+Nf3zovYW9FRitBuhDRvccgZ8F/kPvVY0if/QL3TZ5c9Az3DlEqXyGi81BkvX09ilZ8YuO24ehxfMHfh22tFfMXv8xBLwWIlDmk3qewjM4mHbbQFl15mCi0ZTeEt+oJfXU5iv9cAHV4tEUZ0PoLSvZo2dnZnDH8YdNJ6PQqpx7TZdQaJPQ6C5/d9R+Lqz+wY7dGIu80MEArVupp9YTmNpARjMYCgTbd6y86fP4mweOt14W+z/7cE+3qgChBC/boXqPr3a+zJWDroSxX1ROoOpT/8yuI7GpZ1bO3UG9osNIeiqpA1bGd7HalUJ25w/h8y/IttpfBSv8lKN8RZ0qakd2Go+CX11BXcJydFKoObuRVojm0Qgtr1QPVx3e6/TieYvMF1YTwxHlEh0GnBM+PomT9t8j59F6kz3yOB28IKnXnLZqL/B+eN91VG5/O/ZNUIjTnSF45evUbiOxjR5Cfb/zyUjaw9e0LqR3N6cd0FXsHBvNeLwUqeXBJxEG525k+L37pGuj1CoRMR3M3Fgi06V5/0uHzJwkef3pd/G1AVIGyhZTFtM5MmuNMCdh6KMtZ9QSC+m3zvn8WYa17IXny3Q4HtpSF+o7jxfUOVtqDhuYoAUJDOVTlCm/TB7X5mchf8gYPYdJrYI4MVvovQXm2dMYTW/mQUuaOVmvGK20zjgZ9HX+xm6p0al2+pwltZUqb6TyEg+MT825lHbm0Sx7jq9WhEWyjRRPY4W37cs9O8dovkfvNE0i/Yq5lI7ZKhYJaDfvA0pQ3ZWwjMjqj6tTEu7OP6Sr2DgzmvV4K1POpjoy3Y8PofJ8XYa/XS7xlm75vjNpPyrf9gYq9a9helITIaYo0svtIxA2Zbtd3OhCme/1Rh88fJHj88XXxpwFR5fyU+eLFxoAyPBqRPUaxQxod181xtgRsvlDXOKmeUFeci9yvqTVChahuw1G+e7XF9iQ1RAt2WuwfWPUDYh6dAn1CG6Rc8azLr1HKBfcj77tnuQrGqNTc00sBLZXorZHBSv8kKANKC0/s8iKbXkLqMSzf/gf3bFDQxVZjKjVL3kT3m2CSDiI3CZIeoSxlU5VOnfniUH8n9RbSCVkJfnMWPITw1r2ROPZG03ZkoZX13hxuxk8YfZXFPoYOH4FklCIkJIQvdXGt8GON849pr6fSnf/PvNdLOQhnzD6dZXWnz8ter1dT97Q1B5ztG6Pr8he/xFkKcuKgLHj18d0oXv0Zqo78g7TLnrKQ82oIf5juVXT4aDCN/o+GBjFKt/zCCyeS29JXl0ETTZma3mwfqIj4e0uHrykleFx9XayP2yfev5WzV/Gjr+bFhr/rE7ozIEpDLORaRGoZlOSoPLgJZZt+Qm3uIaRd/rTNsZaSDv976VXUVJSirKzMdCkvLzf9XmSIgG74rS49d9KDpGoPUfDrmza3xw2/zBhQqjW4uH86Wo16FBVhiXjvGFyGsrRUPaPgkXQuQxJa8utw7LXZCEm0nF8gZLDSPwnKgNJ8RZb7y2sw1FRwyp6mG8mvlgY86vKPIeGca4wrPrYrPBdl//yKnM8f5F4R6hUs3bSYT3ZxQy+y2Hdjatk5+8XRks0cDePUVrM8AzlgJJxzrcU2pLNJosXVx3fZ3P/xR/5rkSnckVWMH19d7fRjUhO3O4Rq1I3e6+UvPW3BjCt9Y6QKkDbzOYssOF1PWcri1Z9yUBlhlZ1uER+OE0VVfjvdq+jw6UpynRrEoMwsZ2U7ncEZqbriHJSSD/n+9ci4+lUuBXpTh89GgkcFWAlJ+CRId/V1MYfMJ0g30Rp/1id0Z0CUMpHmkM0sHbuLVn7M25vbzhpR4dEX3kB4ZR6io6NNl6ioKP5JVafu0dHYpM/HSXUiDKfMLRpST6B+xrb3/ejUQv2Fa+4xtQ3Ne2SJy2Vvi3PUqZ7Kmryj0JUV8GfE8r+VwUp/JWgDSqV0GtV9BMq2/obSzYuhryzlADI0vRMSzr7KYiVMmnUkvFr2z28oWvGxyd82edIdCG/Tq8m07Jwp3yurSSoNqkLDoa8ocljCp/KEdQnf3hfUmcc1f0x3IDeTc4f2xfAhg3DWWWdh+PDh6Nevn097vZpT71ag9I2Ry41FS8UpKHNDASWJHJsHlLTfsd3Tcfe4rn7Z82quw0fyKtYSK/YGMZLG3Wyzn4guZyL7w9u4mhJ35kVe1+Gj74MiwXPfvJ+w9mgpQkkY3kdBujuviwJlMakvmjJ3xas+tbytCfQJ9Xo9ioqKcPLkSYtLXl6exd+loUlArytcGhC1B+mDFq2aj6rD/9gJKIGVq9egf5uEBrPDtCCu9la52GAApQOUhbrSPkTfT2Wx7v6u9Sha9gFnrmP6TbC4TQYr/ZegfVdOe2KfbfcLaO8kFztwMl/8ScvOuqGaJp6thWgpu1Gxbz0iOgzkpmllaq5850q+TqE6ez+XFKL7jWvwC2rRNuDEY7pDagRw7szL8Oeff+L+++9HVVUVIiMjccYZZ3BwOaXdMHx/2OC1Xq/m1rsVaH1jDVm82fSNTenpl4359QXTDQ1imKONSz3d530KX+jw0bEyctdPyNi1Cyv+XOuzIN2T16Vw+YecuSLxa+uA0huvi06n44FE6wDR0YUCR7qPxfNXqZCYmIiUlBTTpWW7TshyYUDUke0lKXeoI2Kgqyp1u5Ll9YW6SoW8JW9gaT89/ihJddg+RJaK+qpyzjYSlHWvKzUuLOh8qw6PQsFvb7N8UGhqB55bIFe0mqy9SJp0u+l70FydrgKJoA0o/VEmw13MG6pPLpoLdUgoi60bJ64zUfbPL7ySU0olNDUX3q4/i+OSpSS5y1Cpn0pGlFGMHTTVqS+o8rjZTjymK9PWyuOe378dHpkyyXjfmhps2rSJg0u6vPPOOzh58klE9x2HpLE38snG4ELgat3r5ayFpL62CuVbl/L/UXvyMP9NE4+UMaFA3Lx/yZ97twKtb8weNPVJrRTmi6JAkA6xp8PnbEBNDibQ67m0S242BA3V+VqHj75zkydP9qkEj7uvC/lAU5Y2feZcqByUa+3JnzkTFCq/FxQUcMXEHK1Wi+TkZFNwmJqaip49e1oEjOYXCiY1Gsv+RvqM9mqgBGwxIOoAen3IeMOeq40rJWBvDmXddk5HfJPdG4+sq4UmhFoR7C8WStZ9yy0OCtR3D7pQhrbnaA4oqV+05O/vuI+YAtWwjC5sCWkuI+RvTleCLf53NPYi/iST4Qnmk8+RXYbyl47KQhQsGif2hiHurMssxNdTZjyEkvXfoGLXKhQe3MTSSOGterAjhvkquL4vqPK4zj6ms9PW9h43NDQUQ4cO5cudd97JB/f9+/fziW7pX8uwpq4tDKldGxwAUhn0HHie2SERT1/Qx/ReUY+VMxaSVMan1TINMMWcMQ3q0Ej2KC/49Q1UZ+3mFohA6N0KtL4xa4rXLGTt0cSxN3NPoTW0/+/+WIMOCaFQq9UNXuhE78x2DV0aGg5ypMPnbEB97LUrTa8LOXUlnHsDLwh9GUyT5uyhQ4e47cRXuPu60HGAvo8UZNKClkS3HUHyZx279kBe9nGUlJTY3E4qFuYBYNu2bdkZzFGAGBcX59IwmLcGROk6a6mg4jW0MDcgov0Am8dwtQTsraGsk2XVOJo+AiqDAXoHgT7R6ub3G9yvO4OVgv8R1AGlt1dkxSs/QV2vycBgy54YX2M++Uy+4tbe4o5KJDQhShd3v6Cn2wamOvWYzhwUnHlcgg7knTt35su//mXMgm7Yk4nXlvyDjScqUWYI55WsCYMBMaoqdIiswuJX7se0t15E68ShFr1bzlhIaqISkHHNawhNaWu6jiaP8356CeXbliJu+KWmIFq8Zb1nLGAO6Y6SJl50n7GIGTDR4eNcdc11qDnR+BqX9QWq2pR2iL7wSZv7OBtQp138KAcVNMlMizhDra0NJX2Kh4yZBE3pCdN19gIf6+vM/zZoQqGLTGSN2dKiAqhCwvHYY4/hmWeeqfd+zuzb3nW10WkwDDYKZbvyutB3jgYMSVamQVQqjJl2KbqkRNoNEGlIxd8HRClgPvHB/7FMkLLwrzq0CZUH/mav9YguxmOapyVgT33R/zyQd/qc6mHQHQhOV4JzBH1A6a0V2cOTuuO37AjMmjULxcXFuPlm2yb6YCzf++JxNSoVHjrfdgijIQZ3bY2PuhqDEsrQ7DtRhG07d2H71i3YtX4l1q5ege0njVOg9D4tXbqUsy47I3rx++iMhSRlX+2VlShzwie3vEyLrKx4y3puLGCeMa88tBl5P76AiI6DkDh+Tr37+GrhArSPD+EBCUcX6nOr73ZlG8qENbSdM5esmjB8bZscczqgVkp89P+TU9eJeXN46M26t7v/oMFI0Bfz7+alWuuyrfnfpepoHAlpjZPaVFSoIy0CgdZj6bhYgajaHLSpPoJofZnd/TX0GI6uK9YmwNLvpOHXhZQ2SOeWpIS0sSlwhpvm3NrgcIo/D4hS+Tei0xmoOryZW5YMej1CEjIQP2o2Ys+YbtOv7kkJ2HwoyxVfdEX6iab0SYKOWhJoYaevKkPSxNtskgrOepP7q9OV4DzNIqD0xoqMPsiXDp7HZZA5c+ZwUEmDJM7gDQeWpirf++Jxa3R6jH95lUei4fT69WuXjH7tRgATqefqVj6J7du3D++//z7mzp2Ln376CW+//TZ63fcVdAj3yEKywQGRRvKWDQQ3H5eNBU5BJ6aT3zyJMFJXmHZf/a0NAM4Z3Nvh/95UrxPJbX3dgNyWs4MYFEiEpnXgTKV1QHnfPXc53evojOYqBbUVmigcD+mIo+EdvK65Sq/L+S6+LtxPp6vjcrdS6laGOSh4oes0MYmsEuDP+oSuDIhSewe5nDmDt0rA9Pxc8UVXpJ+op5P6fDWxKayQUn20nnOEE97k/uh0JbiGf52JfIy7KzIFKmm9+OKLSEhIwAMPPMBB5dNPP2233OMLB5amcrnw5uP6UjSc3ocuXbrwe7J69WrU1dVhzYZNGPnyeuMGblpI0vRh6d/fs06gde9lYwyIBIKbD2X51q5dy0H8zz//jJgJ/0GJIcypvjGCMr+5Xz7KE50pFz3MLRv1Ya9vzB9eJ2dlvpwZxODba2tsSuOuDGH4i+aqO68LDSZR4HjiPdtqUMlfC/mScdUrHHT7uz5hoAyINjSUZS79RPqhrW75hAXISfs4+6PbHd7PGW9yf3O6ElynWQWU7q7IrIOWhx9+mDOVt99+O2uRvf7666bpPl87sDSVy4Unj9vYJzB6jx566CFMmDABX63ebjqJuWshWfDrWxx8pl70sN2sma+8Zf3dzaewsBC//PILB5H0k2RXaCqWXndN2wQsO1yBfCf6xqj9IGfhfzl4oPJm5f4NFo8TkpDOAxmO+sb84XWixcuKFSvw5ZdfQqcZAnWs8fk5NYih13H5UGM1fEQZW1IasM5qOTuE4U+aqzbyZ068LjGDJvNAoMXzqijmDGZU73O5TEyLvEDQJwyWAVFz6SeVNoSDSWdxxpvcX5yuBPfwz29fI+KuTMZtt93GQeW1117LE4UfffQRvt5yolGyAd4o37uDq4/blCewHoNH4Kz/LMDLG20nS12xkCxe9zXK/lmCuBEzbYSYfekt6y+ZJXOopWDnzp348ccfOYhcs2YNZyZJjP7GG2/E+eefzxqiX246jv9+t4Pv40zfGF2vO+WAUrT8Q5vHJYUA84DSvG+sKV8n8yDym2++YfmZdu3aocvF/XHolPOMUwF1VRmOv/4vLu2GJrfhARkKJMu2LYU6LIoHwVwdwvBHzVVz+TNnXheSPwNdzFBK3xRwmvtRB4I+oT/4qPtC+skZnPUmp4THzCFtm9zpSnCPZh9QesJVV12F2NhYXHbZZchNGYD9ka4PmrgbTHlavncXZx63qU9gp4MM+n8bfmaOLCTLti5F0bIPEd1/AuLNTur28Gbvlj9llkhsftmyZRxAUiB55MgRRERE4Nxzz8Ubb7yBiRMnolWrVg6fO2XXGuobI6/qhize7PWNNcXr5CiIJCWCiy++GAMHDsT+3DKc99JKpwNqGg6L7jsWVUe2omLPnzDU1nA5Mar7KMQNu8Tk5e3sEIYyNEFGBuT6pVithrXohoTRV5lKxISzuqve0Fw1lz9z1sHMWQJFn7ApfdR9Jf3UEK54k9PrQQ5Y/pppFupHZbA3oie4xGOfLsX72429P95g7vTebh0wmmoQYU92Cc57+FPkr/gUNdn7uZxFJ0ma3qQSpvUJgvrlCn5/F9XHdrKPM2X+EsZcazFdHaZVY+nto1w+gbkTZJz89imW5Wh951emKcqKvWv5eiq58YBIPaLqFLhvf2ScV15rCoa9WRZz57N07NgxDiDp8vvvv6OiooKDJspA0uXss8/moNLXz90eyueCZEsa63VyFERedNFFpiDSuo961rx1nAX1qjrCqWC6Id1Teuxla9Yh6+O7oYlJ5uDQAANKNy2GvqoUGbNfMA0C1Zw8zC0fpLsa3r6/SXe1cu9fiOp1jkl31dnHboimfF38CWfaNBSU2xuzncXVwSqlh9LelLcjnVnyJk+ecrfNgvOnW8/ySwcsoWFkGeCFA8Onu2tRfWIvyrf9jqqj21BXnMOixGEtuhqFxE9ZIZpbUZVu+olFtDURsVzqih8xC+pTntjuZgN86XJRH0/8tAu1RbncBxbVewxnVyjbV7FnDU5+/TgSx99i8uatK8lD9qf3cimPpDAMNVUswE4ntowrXzBNbLojGt5Qmc9ZC8mqo9uR9/2zXI5Lnnx3g9aS3urdUjJLNSePoHj1Zw0G59RjV7btd9SQbMfJw+zTbp3pc+azRGXr9evXm0rZ//zzD/cEDxs2DP/9738xadIk9OjRo16RZ1ef+5FnjA5J9ghv1w9plz5h9zbKzhD3zVuMk8vnO7WAaej7Zu91UoLIhQsX4ttvv7Wbiazv9WiqIQxlaKJgxSfsipU++3/8PxNkW5j1zg2ctUyZ/oBLuqve0lwNlOEUX9NUFSZP8GZbT33e5N5uHxIaDwkoPUSRUChZ+xWqj+1ikd6Q1HZGq8NNP+LEB//mg3poSjvevnDZB2wpF9l1OIuFU7audOOPPPSRdsnjAefAopzAwjoMQlqHQRa3xQychBMf3sYOO0pAWfzXQg420/71ksmjNbRFF+QueIiDI2U7V09gSkDjSPPMXOcvrFVP9jSnUqDSu1dbeJwzOOHtByD3a3ofVIjqNhzluy1X5KGp7dhGUkGjgtd6t5TPEtmUOROcU1a17J9f+Tlp49NZasUaR58lGiZbsmQJB5E0UEM2dGQdRyXs++67D+PGjWM1A18996RJttIoNdn7eJqeMmX19Y1Rlqu6MMepx3Hm+6a8Tvd9sxVXtSszZSLpNXEliPSHIQxlaKIqcwcvkpRgktBGJyK8dS9UHFjPrx2VmV3RXfWG5mqwDKf4w4BoY+PNtp76vMmtH8ffXxfhNPKueIC5hELM4As4fW+uiUZ9QlnzbuFgM3nyXagrK0DJhkWcKTDXGtMmtkDhb29zHxNlVwLJgcV86s8a6o3RxiSjOnuf6To66Ud0GmwKJomIdv2gTWzJNpFKIODqCUwJaGL6j+cMlyUGFCx5HarQSPYOLtnwHQ8EkB1lSFpHLmtTppLsFckDnTzIiYJf37R5nLjhl1kElDx4sW4RTp6Zym4c3vgsUQuA9QCQveCcXGRih17IB2d6rqV2Akrls0T7J/cVpReSLC0pM9mnTx9cd911nIUcMmSIjRexr567PQmRfNaxU3HvoDkhGhWemNqLg0lXFjDOft+U1+nPA/lYeN+NaBWj5f5oV4NIfxjCUIYmSOqKMpTWUCaX+oXJfYa8613RXfWW5mowDKf4gqaqMHlb+skZHHmTK9JP/iABJriOBJReCqYs5GhOQaVumtqkVT5Rc3w3lyWjeoy02C6q+0g+wZHlnHKCCxQHFuupPwrYDHXVfMCo3LcOlQc3colRESXWVxRx8701YRldOONmjrMnMPOAhqaBzSeCCcrWUAYr7sxLEDfsYi59U5nUvMxHKGW+Fje8Y+GG4wjKTibpCvDBq09j3otP8sT/XXfdhTZt2ng1MHcUnFO50hnUMOC8mx/DkW/+x72PY8aMwWuvvcbZSHeeqzeeuzWGuloeSAlr08s4JGXGlL4tTEGDKwsYV75vfH8YcO0zH+GNa0Z77OHcFEMY5kMTIYmtuCXCXLmAgky6jqgrzYcjpc/6dFe9pbkayMMpzRVr6SdncNWbPCM+HDfO3+i3UmlC/UhA6UMJBZp30lUUmcSb6UBNkM6aTdaAS34HmsyBxVtTf4V/vIeyLb8Y/1CpuXSWOPYm/pPaAAgqUdqbBKSBAQosSN/MlRNYQwFN+c4VxszXqV4dV+0VHaHVqPH1XTMQec9EDtBeeeUVvPnmm5g5cybuvfdedOvmOAPkzGepvuDcFfRQIbLTYM5Ojh492u5AjSd447lXHtjAYtZRPc+2ue3vI4UOH6u+x3Hl+8bbQ4WdRSqvBZONLfN1JL/clMmh7HXBkjeQv/gVnrCl6driNV+YvoN0ondEfbqr3tRcbSr5s2DGl+VhGs5LqMpCJuL4mKL0J+uryqErK+C/K/evN7kZkbsTC9M76U1OX7uc4mrklFT7jVSa4BoSUPpQQoGsw3Sl+Yg/6wr+m8q6RNWxnSbPXqI606jZpyvLb1QHFmvM/YypHGrvYn7bsXLb8kfs4KncR0r/d8Xu1TAY9CanDzrxE+ZtAQoqTahpG/OAkva/YfdhdE+P4XKs9UWr1eKPegJ7Wh3T8whr1d1CfsUVe8WGe7ciWez+zjvvxLvvvovnn3+edUkvuOACtuccNMiyNOvsZ6m+4NxVKtVRGDVmLCK8/Fny1nMnDUJyMorqOtzmNuV7QO+wKwsYV79vvvzONcYQhvkwQ0z/iTwAR17LSk9xaHpnDi5L1nxhMZDkqu6qN4cmAnE4xd/wdXm4srIS77zzDjuQFRkikH71a6bbStZ9y33TCqTnC7pQW0vP0S55k5PejI6evaFppdIE95GA0k3MswH2oH61gt/e5D4lGhwgSKg3tEVXHhLQxiQhvE0f3i5/yRuAWmuyHVOg/U+bdR1Cy3OdCvBcvd36NvrbFSJadUfqzOcsrqNJW7oQ0b3HIGfBf5D71WNIn/2CKVOkZI4s/lddjd1sEjFuwvmoOWG/30oVGoHWty90mFUiCRR9ZQmiethmvlyxVzy9oYGX0q0KNuG7Fz/FT1qtTZA7ZcoU7N27F3/88QcPd9Bgx/Dhw9GhQwcOgK23z9dHwADblXV9wbmr+MrNx9H3wJXnztnFA38jouMgFj129NyV3519HFe/b+aP5ateNl8OYVgPMyTQSXvIdO6XJFUFGt4iRyjzYNscZ3VXfeGXHUjDKf6Crx2iqqureYFMgWROTg5mzZrFDmSPLM8zST+1uvn9BvfjrDe5P4nwC+4h31I3qW+VTmUl8iWmg3jytPstykYpF9yPvO+eRf7il41XqNSIPWMaS9XQ5LE1BpUGoaGhdrNzdCF/cUe3eXK7M9eXauJwx2/GKWlHkLUhWaXRBLJi06WUR6xfM3V4jEV2UuHlF19Aiwj7QTFlSd8+4rhEyeVutRaR3c+q93k2ZK9ofDP0UBn0SD26HOG523CinkCeZGdSU1MRFhaGrKwsfPrppwgJCUFUVBQHlebBvCq5PRIvfdrm4eoNzt0oy/pCjsPRPl157izmXVdTb9Dv7uO4+n2r77H8fQjD3tAE2TlqWp9um6k6vIW1KZXyowLprub//Aoiu9afSW4Mv2x/H07xB3zpEFVTU4P3338fTz75JB+7rrjiCvznP/9B587GzN9TCRlel36yhtozilZ+wu1iLa59wyJBUL5rFUul1eYf48+ydVDrDRF+wT0koHQTR6t06ifJWfgw/0ybOZczI+bQ0ED6zGf5ZEYl1pCElhxoHXttNkISbfv2Xn7xeb89uFImQfXbknoztUoWiPrjwpJaQR0Zx9qB1pCOZ2ja6elp8xPYrGmORcM3Hy3E228aSyzWkDRK5b61iGjf30I+xRpn7RVHdE49tbqfCldbCUiah1b6q1atQs+ePVma59JLL+Xgsj7BYEfBuXVA4Ay+yCw5u8/6nju1hqjCohDZ6QyH9//1l8U4ePAgoBrg0uO4+n1z5X8KtKEJGkKqObEPCaOvtig1uqK76s9+2c0FXzlE1dbW4sMPP8QTTzyBzMxMPj5RK0/Xrl19Lv1kDrVqkLwcWZBaU75jBSp2r2LXHXu9+I0tuyfZdEua73/ug2wAZVkoO1JXeJyFmWnC2xE0Aa4IntfkHeWsnVIab8xsgLdOYDQ1rYmKt+lfLN/+B5exlcGkyK7DUL7tD9SVnIQ21iizU3l4CwcAVL509QRW38mfsi4U0Nob9HC1zPfWFQMwvlcG3IEyZRMmTODL6tWrObCk8hGt+u+++25cfMUsp+Q4zINzd3jyp12YO6OPV1fuzkqJOHruJO1DZgD02beXneb7Ggy4/5Zr0bFjR2DaALcex5nvWyB855z1yy4/vA3Ff35udL+JiEVN1m7+rNMgBIlKK9QV5zqtuxoIftnBji882mf0b4FPPvkEjz/+OA4dOoRLLrkEP//8M5sZNIb0kzWFy+axKQj1W1K7kjnUe5k04VZ2WKMqIBkpWONr2T2RNHKMBJReygaQPMfJRXNRnbUbqTMespGucQT1fBUt+4AnT2P6TQi4bIByAsv95TXWdqQshyYmiUvYNGhRl38MCedcwyLKRNyZF6Ni95/I+ewBxAyaAkNtJQ8OhKS0Q3Tv8yz27cwJrL6Ahh6feiwjHHgDu1LmG9HZfY1Jc8466yyTG80zzzyDW2+9FY899hhSr3kDJXpj/6izwbmrrDtc4PVpSOvvgavPvWLnSm4lqK/cnR6lxra8HMTGxmLUc8tcWsC48n0LlO+cM37Z9B2EWs3fLcrU00AauXZRud+8pYPcg5zVXQ0Uv+xgRTFvoPeT3leSgKLecpqkbsjykL4bJ96/lXuI40dfjbgh0/n6hxZtxQPXXIiD2zZgxowZ+O6779C7d+9GkX6ifnS6h/ndKFtO54eMq15BwW9v2dzPuuJX3/69Lbvn657VYCBwj5x+FEzRB6vwj3mo3L+OJ9p0lWUo277MYltFyLngt7d5ACQ0tQMM+jru8avJ2oukSbdbiH0HSjZAOYGRiHvZ1t9Qunkx9JWlHECS3mTC2VdZaP1RVjLt8qd5OrdoxYdQqbUsdE5Bp3WGypkTmKMyH2lNUr8YaQ6q7ZROmrrM17dvX3z++eecFXj22Wfx7caliOw7nk/2+U4G55RdKtv+B/9efaqNoOhP0ncDf5aie51j+ZrwQV/v9WlI8++Bs89dgW6j0lV4W/snMfoeTOjbloNJdxYwzn7fAuk7Vx+UEaGT2JqDKgsnIEfQ9Lu1XWd9ftkyYd10KOYNJAhO2WdNbApCUtujmg0B6qd04w9cFbKmVmdAzKhrsPnjd9Cvn7UhhG+lnw7klSGrqMp0GyVlKIiM7juWM+Oe4G3ZPV/2rAYTElB6IZgiyGlF0eGiizVKQEm9HyV/f8d9YzQtTILeaZc9aSFrEkjZANMJTH22jSerI0hQvKGTnSsnMPOAxrxfjEWt7ZS7/anM16lTJ5bkmL3tAGZ+tpuvczY4p+xS8ar5FvtT/qZAyzqgNGIciKFS1foVv+PhWecZS8le+h44+9wJaqqnftqYwdMcBvTW3wNXFzDOft/sPVagIn7ZwYe5eQMtwFrd8gn3Alef2Ifsj26v976UzaeFJklGFa/61PJGlRql0a0Q3aJjo0o/pcdFoPcjSyz2U7b5Zw566fvpDbwlAearntVgRAJKr2QD8pF+xTNO3YfKEvWVJgIxG9DUJzDzgEaBAgh1ZLwdG0b/LPOd1bsjRnTKw5oDeRyY2wvOq45sxZFnJtm9f/qs/5ms9IrXLETF/nXIfPlyY7kzNpmHjeKGXXJa0N1gwIryVHQbOBz9OrfmBnyyGmzd2jgx7Qz5+fnYtGkTX2IrElESnubwuduDhmbqy47RSah/m3iL74GrCxhnvm+B+J2rD/HLDj7MzRuokqMoZjhD4fIPuX+YLEhtAkovloddkX6iQUTzs4WusgRFqz5FvPkxykO8IQHmi57VS4I4UykBZYAHU/5AU5/AzAN75X3ImP28x2U+ynBWHvkHj9zxOZelW7Ro2D3HK5+lBmRrYgZOttHK1CacHhiirB8FxFTupx7SuvxMlP6zhN1oMq561ShqrVJBGxKGs+95G7EbP8aDDz7ItpHDhg3j4PLCCy9ERsbpfWZnZ5uCR+Vy5IixIT4mJga9hp6NsgHXQs+Hce84zdCeNh0twqx56yz6kOQ71zDil928XNkcQX2W1FecPnMuVA6+l75wZWtI+slamoskgtQR0YgZNNlrz8He47jTs+qKrBFRdWwXipZ/wE5cqrAIRHUbwcNEVEUJdkmjwNTH8MNgypsEYjaATmB3ja1HENzHJzAKBigo8CZhoSG4++zW+PXXX1k6g4JK0mhr6s9SWOue3EJhfjFf1adMfwBJ42/hqfmYvmO5pzBpwr9RV3iC+3wVdAZgX6kWc9/8ELm5uTzpmZCQgDvuuIODZxJk79OnD9LT0zm4PP/88/HCCy+gpKSEs5nUA7pnzx4UFRVhza/f46kZlA327ntg3odE2QJXXqdg/841BJXXnpneGyF0lCfBdxegrFWYVo2503tjzuhOaO5Qxo2yaiRTRj/pb39yZXOkjkA9xGRF2tCQqFIebizM1TlI0qtsyxLEDJwCXWkB6opy+EK9z9RXSb/rKkvdepxlv/+G3bt3s96vuz2rrsga1eQcRO6CB1ltImHMNYjuOw6lW37ByUXPWEgaBSuSofQCkg3wztQfBYR0Ynfn//dVlvSSwRNw7aXTWI/tgQcewLx58/Dyyy9j/PjxaMrPErnL0KSyQxF2KxTbSWs5HY0KeGT+H2iTt86UeSRRdoJEjSkLSbJHNER0+eWX47rrruOg05Pn7o0+JPnOOcclg1rj+XtuQEnX81Gd0F78sgNQHqYhVzZHlG9byi5JJO7fEL52iKpPnYNcrmjBU7j0bb5Yc/yta1gRJPHc610OqO+64UrcWVuFiIgIXhzT4JFyoWl2MppoqGfVFVmjwhUfsdsXDZ6qw4zfHRr+K/j5VRZlj2g/wKeSRk2NBJS+CKZ0es7+uAIl1+jEdtPZgZ0N8GTqz9MTmK+CjPj4eA4ir732Wvzf//0f60mSvSJl6zwdaHEnMCfXF0NNJTfUU7aShKrDMjrb+rLTwU6vR23hcRQt/4i3D29jWdalzymdMPHjRxg4cCBuvvlmDBgwgC8tW7ZEXl4evv76ayxYsIDF2Ek7c9y4caxVR68Blbutn3tsqBr/XbQV0Gj5Mb2JeR9SUy5gAoXly5fj7+W/4Ke7b0XngSPELzsA5WHcKdvSgpOCG7LeVPR+ffE47mKuzhGS0hYp0x+02YZKytQDToGkNt51DeB2yVHYdOIYS7Rt2bKFL2vWrMF7771ndChTqdClSxdTgNm/f3/+mZaWZtGzak59skb66gpWFqHKkBJMEjQcWfj7e6jYtZoDSl9IGvkLElB6EQpoCspr8NySPW7d/6Xf9yEhKjTg5QXcmfrz1gnMl0EGrWjJn/vLL7/EnXfeyY431Hd4//33O1zpuoJ5I3vvlnH4fs5wPPLDDvx18JRVpSaEheEjOgxixyGyiixZ/y1yPr0X6TOfQ2j66eBWX16EY6/NOv2/xSQjecrdJptCc0ITW2D7wSN2pyFTUlJw44038oUyll999RUHlzNnzkR4eDiXwanncuLEiYiMNB5ED/72CXI+eA8THv4Ym7Iqjc/HBe08slYr2bCIp8Bp+ptOOLFDZiCyk9HFyLwPqSkXMIHAU089xSdKWgTRCVT8sgNPHsYd5yb6rkFXx+VuKhkTdaXGjBt99+g6TUwiVJqQJnOIUtQ5EBmHyC5n2txesuE7/ml+W03uIVTsM7bt1Bae4OFKRSqN+sYVhQdFnSMpKQnnnHMOXxSqqqqwfft2DjSVYHPx4sUoLTWW1anFJ+ay56ELs8zWNiRrVHPyMPfdh6ZbLu7pNabnVpNzwGc9q/6CHEG8iCfyAnRcqq7zvkZgU+LK1J838WWQQSdl6h+kQIocb5577jl8/PHH+N///oeLLrrIZY/thspq6XGn+3TCW3Xni4nOQ9hq8MS8WzkbkXbJY6abqME99dIn2L2JDmQVe/4yZjU9KHdRXyVlaOlCpfCFCxfiiy++4P+bAuqpU6di1KhRLNhOfZhP3XoOpr/5JzYfLXJaO6/k7x+47EVT6dFnXwlDXS3Kti3Fya8eRcoFD3BAbW2t1pQLGH9m/fr1WLp0Kb9P1p9L8cv2b3kYqjBQ0PP7779jyR/LgR7X8iCds5D8DgWOJ9672ea2kr8W8oWybKFpHZrMIcqeOkdD0KCLI6m0qF5jTAFlfeoctBAeNGgQXxT0ej0OHz7MweWGzVvxea2tVW9Dska6MuPC394EPkk9VWfu8Lqkkb8RXP9NEyLyAvXT2CcwXwcZFECR5+1VV12F22+/nUvAb731Fl555RX06tXLa2W1E8WnhX/tEZLQgp2AKvau4RW00lNJq+KIU5JJ5JEd3rYfcubfDXVUvF3PbFfLXW3btmXbSLrs37+fA0u6fPbZZxy8HD16FAuXrMKmoyUuaeeRAHNoRmekXPhfUxAU3ec8HHv9SpRt+50DSkfWak21gGlK6vs/KTtJw2TTpxtdUQT/Pn7TIo0CSFoEUCUkJycHYWFhGD58OOJD6lBUZ9+a1B40LR3ZZaiN2QP53Ef1PpcDL22csa+6qRyi7KlzmGNPis9XsntqtRodOnTgS9ehY/D5q5baxM7IGhnqjAOb5llfBbpOf+r2puhZbSyC7wjbBFBwcO9b36Jwy2/sS1xXnMP+udS4S3ZnioewsyU9ItjlBRoLXwcZ1EP5/fffs/ftv//9b+7BmTNnDh599FHuvfRGWa0hSGeSyls0Wagy690xhzKbFNSRPqe9gNKTcheJs5PsEP3vkyZNYsmhDRs2YHVlBjTdRruknaevqeDvi3lGjfqRyO1IFRJ6+joV8OJv+3Dz6I5238dgzsA5MyzSO1mNxas34d3nH4dG49zgVnPFVUtDmtql7xEdv/XVZdBEJ3Fvcvzwy0zDb84cv0nHddmyZRxAUiBJCzP63FPmjBaqY8aM4WCSBkoe+X6HhXlDycYfoK8qN2XFyExDKWnHDpyMsPROAF3MUErfJHVjXkZuSocof5QAs7e4dkbWSKU1Hp9oOt0auk596vb6HifQkYDSC1CmKf/PL1F1bCciu52FkNR2bAVXuulHnPjg30if/T+EprRzuqRHWJf1BM/xZZBBPWp0AnjppZfYTpEydVQSv/rqq3n1642ymiNIqJ0OZirSl6wHWkFbT3l7q9xFfUlUCj/vvPM4U0mM+t8yHC2wX2Z3BJ2YqemdvicU+Bp0NSjZ+CM3vMcOmmLajs4/P20/wZfGnLgNlGGRowUGtLjuTfxam4RzCioadWEaaNlhVy0NSRqGsnv0+aSJXkogsM7r/vXIuPpV9pu2d/wuLy/H6tWrTVlIKq9SaZsGQ8aOHYu5c+fi7LPPRmJiYoPl4ZJ130JXkmv6myoUoAtl8XqOhjrc+e9zUzpENbWGsT2sF9eKrFHCmOtY1kjBXNaIFvKaaOP7Rud+ayjwV2539DjBgP9+ywMERV4gevA0JE25yyLdTfZwWfNuQcnar5A8+S6nS3qEo7Ke4L+Ehobinnvu4YEV+kkSO2Sr+Oqrr2LIkCEel9WoZGVdbqGTW8W+9YjoMJCz3fqaKo4Qrf3Ly3f/yRmXMKuGcW+Vu0ijMzMzEz/99BN/rkk7L9PFYJJIPPcGPrGbS4hQtj/tsiccauk15sRtU+FqVttwSg907aGCRvES9heJncawNEwaZ9uXGNHlTGR/eBuLiMedeZHp+P3Vr6uxa90yDiJpwri2tpY1XWnxSRUN+tmqVSuXy8Otbn7f5f+VsqfWhg7+4BBFn8uDWXl4Z+2JU99mVZNKgJlLGsEFWaP4s64A1BrUZO/jc7954EnDRJRsUmiKntXGQAJKD1HkBSyGJU5BpbvQ5Daozct0uaRHBLO8QDBDwyvz58/nyehbb70VQ4cOxWXX3oKN6ROR9+OLKN/+u8P7tpzzIbQxySb7RBIjV+wTKZsdktQS4W36nJryzkTZP7+wHmXC2f/i+9cVZiFnwUM83RmS2Io/Z9XZ+1G+Yxk0cWmIGXw6y+etctehQ4c4G0uDON26dfNIO4/+F21SK0TFJCOi02AeJKJpz5PfPIW0mXO5Z7QpJ26b+7CIv0vsNKaloTmkNUhYVAD0Olz/3CfQb/iCM4/PP/88zj33XP6OuDq856/lYW9AAzF/vHoP6moTETXyX6BKcFNKgJlLGhHOyhqpw6PY6pfaIeKGXWqSDirbvoyPY1FmAWVT9Kw2BsH3H/mRJRaVM3QVRdyz4mpJL9jlBZoDZ511Fv7++2+8++67eH5TDfQ6PWL6j7fjL25AwZLXuYxGwaQj+0T6nFCWsvrEARhqKzlbGdllGOLOuswUaGlikjjLXXXkH5Rv+x0GfR20samIGTAJccMuhiYi1uvlLhpKSk5OxkMPPeRxfxA5SlCmNfWih03XRXQeiqy3r0fRik+QMu3eJg+imtuwSCBJ7DSWpaEyqEE6rzT5S6VyIrxt39MbqDXoMmoaVv/8OrRabVCWh70BKWXQENJvv/2GLv3P9AsJMEXSiPavcUHWKH7kLGR/cjdyPrsf0f3Goa40H6Xrv0V4+/5cRWrqnlVfIwGlDy2xaKVC6XJOhbtZ0gtWeYHmAg1EjLngCjxzZCWnaug9tn6fqzJ38EBNVI+zLewTrQlt0Q15i55G0vm3IarHKPuPFxnHtotOPTcvlLtoGOm7777jvsno6GiP+oNqi7JRdXAjEq2evyYiBmGteqD6+M5mpZigDItQhrloxceoPr6Lrw9r0Q0Jo68ySb4o2MtqU692nNlkqreG/fw5a+prS0OFY69dCZwawKBjeMK5NyCifX+LbXLK9ajWAV6IJ5lgc4giaStaiFKLEJX/CX+QAHNH0oigYai0S59A0fIPWcyckgHU0hY/6kr4Q8+qr5EoxQPqK+vV5mei4Lc3EdayG6J6G78o7pT0glVeoDnhyHVBoXznCj5MOgoSG7JP9GW5q74Bi+rqah7EIdFg0qKsrw/JGfTlp5rZDXqHrQFHnplk0Rpg0NWxry5lY+vK8qGNTkJUn/O4j02RUApUxQTK1JRn7UPO/HtYmJ6miA0woHTTYmR/dh8yZr+AkKTT/Xf2sto8LHJgAzKuehXq0HCvDPv5c9bUFdxty1BIu/hRHnSjYz0lDwy1thJfvjh+B4tDFAmJk50rCe/TIKM5TS0B1pCkkSNZIyK8dU+kz3oO9vCHnlVfIgGlBzgq69GUV+6Xj0IdFoXkafdb+C27U9ILRnmB5kS9bRG6OlTsXo2wVt1NAaOr9oneLnc5O2BRsXUJiwGTbJI94WzzPiRn0NJiSqVmWa34kbNNrQH6ylIULv+Ae4/rik5YtAbk/fA8v36UBQjN6ITq43tY6FhXchJJE24NWMUEZVikYMUnPMFPShFKu0JUz9HIeucGzlqaZ7Lry2pX7l/HCxZPh/1ckdgxGPQo3/YHTyDT8Ji+qtQ4Hd19JOKGTDfJrDRFwF9TU4P9h454tI/wtn34Z0THQXwMPzFvDistkGyPr4/fweAQdcstt7DW5pIlSxAS4lhjs6kkwIK1Z9WXSEDpAfbKeqQNlrPwYf5JGUeSkPC0pBeM8gLNRaakobJa5aFNHDSal7vdsU/0RrnL1QELnb4rhtw3H9FpbRvsQ3JGO4/KshSQlP3zKze8R3Y9kzP4lJGjAYfovuNRuPQt02tVfWIvKnav4gb4+JEz+bqY/hOhiYxFyfpFiBk4iTN2gaiYoGS1qR2Ceq/Me1+10YkIb90LFQfWc2CnDo1wKavtybCfKxI71MaRv/glhLboipj+E3iQrPr4bhSv/ox7fNMue4oXIr4K+ClopIEx0nfct2+fxU8SEVcntUGLq1/1ymOFJGRwCwJlKq0DSl8dv/3VIcqZ4zHJqpHDGF1Iy9cfCdaeVV/iH2fdAMW6rEflj9yvHkNd4XHuo6AJb0clPWtoeII0rawJVnmB5iJT0lBZjcvdai0iu5+eAHTHPtHTcpe7AxYn9DEOByzc0c5LHDeHA5Syf37jDBxB3rjJk+5A+e7VFq0BipVZZI+RFo9LGTDyOKdMJwWUvlBM8PUiRslqk+SIkskzh1pnSMy+9uQRbqtxJavt7rCfqxI7Ko0WaTOfs1DAiOk3nrOUxas/5aCS3Jw8CfiVoNE6YKSfFDTSBLFit0cC/HQh56DOnTujVbuOmLOsyqOytzn62hpTT2VjHr+bujzs6vFYU36SFTCuuOIKzJp1esHsjwRbz6qvkYDSA8zLehQMnlw0F9VZu5E64yG7AzbmJb3ofhNMZcK6kjxUH9vJWUprglVeoLnIlNRX7qLsUuW+tdzIbz197Y59oiNUBj0MKjXSVcU4Ly4PRZuO4pPdkYiMjGQLyaVZGny177QtmLcGLNzRzqP2EMrwWGd5qDWA2kXMWwMURwrrgIuDrVM9hd5UTGisRYx5Vpukn6isbG6rSf83XUfQFKnxv3Utq+3OsJ+rEjv0+bUnp0ZTsRRQkuyV8vmuL+CnXt36Mo3WQSMFi+TWRD+Vv0nKy9xgQKHNP8tcasug94G+t5rwaMvnmLUHtScP2/RBN/bxu7HLw+4cj6PLMpHcriveeOMNBALB0rPaGEik4iFKWe/k0nncpxTR6QzoKstYe8qc6F6jLUp6OZ8/aFHSo/JQ3FDLwYZglhdoLjIl9ZW7KvauNU5397Qtd7tqnxhuqEQVwgHzXkaDAZrKAqizd6Nu9zIcyDmEreXlqKioMJ2Eo/uMRdLE/4M3oFX82uW/oX9sJQerdDkrLAprEQHb3Ltr2GsNoGCLqD62CyHx6abrlcylrizfK4oJ7i5inpjaExmxoairq4NOp7P4Wd/v+/NPZ81iBkxEwZI3kL/4FcQOncHVjeI1X5jcOBT/YFez2u4Mi3gisWOO7lSlhloTTNfpDfh1+zEMVB20CRzJF175vJINoZJppEEw5ff6gsb6cLUtg16546//i3VeqQKlCgnnQJLczqhnPm74pc3m+O3u8bgiuhW0Ux7F4j1FuHSw7ULaHwmGntXGQAJKD1HKetR0rhyA6GINBZREfSW98Da9LO4TzPICzUWmpL5p5/Kdy3kaN6Kz871j9uwTaf8bH53GvztT7qKyKJUK92UV/H975wHeVMG98TfpSPekiw2y9xRBpgwVBRkOVMCNn+LiUz8RB+4P3CLi+MsnKi5EwQ2yQRFQhgyZIpTRUrr3yPg/55QbkjSzSduM83uePG2Te2+S5ube957xHox+9gvk/vy2wxn0trqugxOaosm0d5QNY1NpGn5Y+DCKMo+xSPKUaLVWGkDNEEExychbtxDqEA1CU9twpIjqL8kD0EApSNP3DWDq9AcRXp7jtMArTumG4vajObNA23T2pLnxYCYGzTmFvFXvonj3zy6919C0dki76VVjTShlMKgBRvn/0/GCxGXh5i+4c7u2UW1XmkXctdgxpXDrVzyqTvHlUzhVWIWrJl6LsGCVUSRee+21ZpFGmjLjqmi0h6tlGUHRCYjqPgrlx3ej9OCvvI/RRV5kxyFsz2TaWOfPx293jsd6qFCpa3jbKH+pWfUmRFC6iTGtN2WuU1fvtlJ6gWYvECg2Jba6ncmgvPzYLrZ4sRyT6Or4xITIUKNwdCbiRKUWGo0Gc9emI3fzUqdm0DNBIUi83FwYKtMgzm0YwaEaXPn0J3zgpTFzJeciov+3+QQ+2F4d6XEVW6UBlOomt4Tsb+bi7LIXjK+RPBrJk9HabPMjR48hXl/ARtPkEUo/6UbpUuV35f706E7IjOzEQtks8usEVD+oCgpiIT3plmkY2Vhn9nzWfld+Hi/UYcbK86ImfshUxPSbwPWSFAULTW6JvA0f8mPBJqLf1ai2K80i7lrsKNDnQvt9wqi7eQ625X65ZttuDO3eplaTZGpDbcoyaDKKI/z5+O3Lx2N/qVn1VgL3nXsQsReoOyjd+Mg7y5C3a5XDKJpiVVK8cwWKdv0Ebe4pqII1HBFOGH670Qi6vm1KLNNqRMn+jdy5bC3d7er4xJySSo4YuHKl7+oMeuViSIm028KywSIuLo5vs69ujPYtzqfIXPmu2CsNCE1qgbTb3kJVdjqLbZpKRUKTTIUtI/7E/n17WLDceeedGDt2rE27EjppGrs7ay1uqtdbdSYMIy7uiolOnjTbV2ihWrnSTMBRzV5Qs/P1hSTKqD7S1IfS1ai2K80inrC+oX2eoscUsaZUvjXiEhrVm5hUkOO367ZRlWePc7c+1SnrSvK5bpnqdOnCJ8Ii4+LomOyrPrENaWnkrYig9FJ7AdqeM18wf79CopqVnF+/dDqKlvPDG5xKjuxyCdvGGCrLuRyBIoIK9elLSGnT8FN/QKev9k1UoIiROiLOyhhG18cn1uZK39UZ9KZNCSTuzCKTlq/fRoOFq3VIzpYGkAAhYalARt5Ua2j5vyWZMn/ObCz6v3e5aSMlJQW33nor7rjjDrRqVd0N7qrXIkGPF+9Zg0pa7uwxvlBoMfN7s2VcOWk68vAkYVaZcRjxw25lT9vaRLUtm0Uomnzq1CmuVzxx4gTfTH/PM0RCNbqmz6WzlP2zE9nfv8plCgmXTbe5XENYpIk9jOu2UVQWQN8PGtpBEXA6JpQe3IyzXz3LtnjUze/sMdkXfWIF6/iP8vAjewFKZ22raIpre79stV7IVyxz3MXVKBp1z1OdWdL4WSzIHEXR9pzMh1qtqhMxTnWKy5cvx2OPPYb9+/ej90OLkBeaZBRQaVNf4Z9Ui3XmM+sn6tQpL5tZwiiQQDj1Ho3wLECjcTMR2WGgy6LF1Rn0fH9VBU68dm21oAyLQkSnIYgfenMNH0R7HdXO1CE5WxpgDX1VBfI3Lj5X1za4hoiadstNfNuzZw/ee+897jSdM2cORo4cyVHLMWPGuOS1SJT9/Qc32lEqOjgulaMwlrh60lSi2iXH9vBroFnAFJmvPH0AxbtXI6x1b0T3vapWUW0VDAg+cxDXXLPAKBwzMzP5c1eIj49H8+bN0axZMwwYMADNWrfBezmuTT4yFdxnv36eRS3tr6aDHrzFIk3sYVyzjaKRnnQzhcRixqIHqj1gzwlKZ47JvugTK1hHBKUX2gtkNx2Ce++9l6cIfPDBBwgNDfVJyxx3cTWKVvj7cm5ooAMXpVmoYN6yacGUMW/9ava3p8T4hg0bMHPmTGzZsgWjRo3Cxx9/jORWHe2m1aJ7j+HXbkpwfJrVZfM3fcKirraipTYz6MkihhpBQlMu4Ohf2dEdKN7xA6qy/kHKDf+tIRIcdVRbq0NasP4IftqbCdN/kb3SAIKshEg8kvjVV5SiZPcqHiBAtZWmUVTLjtuuXbvizTffxNy5c7FkyRK8++67mDhxItLa90To+Ged9lokKH0bc9HV3BhEDU5FVgSlqydNpVmEotVQqzlSyvO541K41CPmwnFm/3NXotoGqJC+7hOo48PQqVMnXHrppUbxqNxM57IrrHjJNYsdgr6fNDUsODYZSfSZnLN0skZDW6SJPYx7I2Rpf6QJVhWZh10+JnvaJ1ZoGERQeqO9QN/pSE5OxuTJk5GdnY2lS5fihwN5PmeZ4y6uRNFITFSePsQnd4rwFm3/nu1SyEQ5bujNHNF0hLtifNeuXXj00UexYsUK9O3bF2vWrOE51wr20mqaZp2NkUZ7UEq1aOePbE9SsOkTq6Jl0rQHEFKWw/59yo26upXfddFpMFz0L5dm0FMk0hTy2yNRn7/xIx59aOm/54otjVKH9MDwdvhhT6bTpQEEdXaTZUvxrhVcO6lp2pl9F5V6WUcdt2RtdPPNN/Nt9+7d+Pfi33BUBRa1zngtEkGRjpdx9aR5vlkESLnuWcfbjohFosUELqvLqYD+FzTC4v9ugqu4bLGjUuHMkic5ok61dWVHfjfbXkh8qtGv11ssdsQexrXjMZVaGLQVfPwtO7wVZUe3c5Tc1WOyJ3xihYZHBKWX2guQx1qjRo1w1VVX4aLbnkLJBeeFia9Y5riDq1E0bV4GyxhKsVBEJ37oLRyhKvzjW2R/8yL/bmlTYgvloPnLkbMY+tIaDI7IRBt1llVxRrfc3FwWI1SDRj557dq1Y9uZ+++/v8ayQd2uQGT/SSyILZsP6ABMxe22UoJE3ur32Bg6rGlnnK8KNd2IDrvLYpGUvpMj29TNTebllMKk3+lWGd0Y61ycQW8NSrnmb1qM8mN/1hCUtWnksOy4NS0NsEXsRVfzzRMdt926dUNVUg70HrLHscTypEn1tRkZGTbrFikjETbxeRbKnmhUoX2OokTPjKlZRlEXFjsEzVQn8tcvqrG9yC7DjYLSmyx2xB7G+eNx3tr3+WKOUan52JQw6q5aHZNr6xMreA/yyXmxvcCwYcPw+Ac/YsEfVqWDX1s02LMpsRZF01dVmzeT+XXq1Fe4C5ygRo5Tb9/GZtDOCkrT1KDWAKwrbYyVf/yGoAOrWJApQo3qW6n2jEQB3dezZ08Wk2RBo4g3ZVnTv987q0WZxVcv58c3qg2oVWqOVlLDhSbNXPzT6EGahdz49re5290q6iAkdR+KDR8/bfN97TtdgHVv0hhD52bQ24LSl+rwaOjKizzWYNGQHbee9Fq0xfHsEvQfNBQnj/2N06dPGw27iZiYGE43U/q5T58+GN+sGc5GV+GbTNtpYlcgUZqz4m1M2TQXX3zxBZo2ddwh7q7FjmVzki9Z7Ig9jOPjcUzfq7hZki7uKVNBFyzK+ElXj8m1MdsXvIvA+Ub4oL0ARSgW7ipCRcYhrotyZJtzfM6VNrdFKUOaL+6tFg0UvaMInxKd2XOaREpzp6NoZEVBUDpFOXAR1DBC04soomk6vs5ZlMhQaJ+JmPvCUyzGCwsL8fLLL+PVV19l38AXXngB9913H6dPnREt855aef6OoBCuLwpv3QfqiFi2v6E51Gc+eQSpk19CaOoFxmaTvLULOSpIdXQ2BaUTV/quzqC3BUVUqXGF0q2earBoyI5bT3kt2kWlQuN23TF8yMAadYuxsdaPD23dMJG2bBbpNfZJNguni59PPvmEa3xdIVAtdgLdHsZWtoGsgpSxnlFdh+PM50/wsSR16qu1OiZ7wp5KaDhEUHoxSrcpdTLTeDlHtjmJVz5YYxuVmYdR9Me33CXaUBYNSmrPmMqzcqMGJFOS2/VC+IRnnI6iUQMFQRNBTDGd8JL+4vmuWIUm0xdxIbmzXdQHNn6L+XOeRnFxMYvIRx55BAkJ1c/tDJaihRqOzJqO2vZDRIeLkbHwXq47Srmu+n9A+wCls2P7X+vwORxd6bs6g54EJ83StrQKKtj8OT9beKteHm2waKiO2/o6mT0++yn0bO5c3aXnm0XaYOfOnZg6dSouu+wyPP7445g9ezabqjuDWOwEJs5mG+jYlbtiPjsd2DomE0F0n17LFkKqsMgGtY0SPIcISh+waIjuO54bDRzZ5lgznc5hqxMVjwarC4sGStllZWXZFYuWqT3qIFWiMt27d8eVV15pFq3hVFywBl2eWul0FI3EJTVHKA0CCtE9L2NLFYryVo//owOWAbkr3+IrZ0sxaa+LuqJKi7d/z8e4ceP4JOxqytBZ0RIS35jTQlSTRoJPd270XsKof9Ww6Knt87gyg54uYDI+uI9tghQj7fJ/drBdDtnXhLe7yGwdsqW5uGXNk4i3d9zW18msNs/jyWYRqs3+/vvv2S7piSeewK+//opPP/2UfTmdfS1isRNY2Bsha4py3CQjfU1iU6vHZIJS5FwbrAn3CtsowTOIoPQBiwZXzacVDNoqnjerad4FwTGNXO42pSL+vLw86w0D526UpqbGEwWqE1SEIc3epS5n07SektpzpsnAlSgaQd2FFI0lE2Ua00cExzdGZfZx/juqS3W9ZfmJfdWTVzoNdamLmuobKdL7yIz70bSWYtxZMcGfl07Lr5MaX2iGsKZ5V2jzqyO5upI8/kkpZ7ovKDbJaHLtzPO4MoNeHRbJgrP82E6O9hr0eoTEpyGORgJeOMHseZXa03cfnozQvddwYxI1BPlCx62zJ013cOek6clmEar/nTVrFvr374/rr7+eU+Cff/45Bg829+50JPif/GZvtWenC/+0QLDY8TcszfZpOg5HGU2gLEbJ3rXVk3DOXfBbOyaTt2zpka0Ia9HN7NjR0LZRgvvIp+eDljn2zKdNoYkhdKVo6eGndJs+WNTcbmSRbjSHWYHqBZs0aWIUhhdddFENsUjRD0+NTnMlikbE9r+GC8NprnNM33FcZ0njvqDTIW7wVOPyJX9tqI7aWulMdtRF7a5fmrOiRZufWX0FHxoGbeFZ7pg8/c7tNZbL/XkB/2z2wOdQnZuN7IxocWUGPZmYNxpTs5wCNv4/vZpEoclVI9njkepM77nnHsyYMQNJSUnw5o5bRxNqPIEnTpqebBahxj+yuyJRSb8///zz+M9//mN1oIK1wQpVOoPTAjyQLHb8EVPbqJwV82GoLIWmWRf2QKUsBk3D0eacRPwltxkzKc4ek73FNkpwDxGUXogz3abWzKdrLPPXem76iGx/cY3HjmWXIDYxGYaqcv6bRGBqaqox9UzGz5ZikVJiztZaeQJXomgEpVdSJr/IzSuFf3zDBy1Nk/YshhRfQrqKpgOcpmlHbm5xtYvaXb+0Glf6pQU1mlro/ZYe3sYdkHQFT81X1ClptgzN0t20GDH9JnK3u8pkioyzoqWuGixendQHzRIGsyfnK6+8gnnz5uGNN97AXXfdhQcffBBpadYN2xuy4/bo0aNYtGgRTh6ohPqCgdCfm8PtyGuRorfagiwU713L99F0GiL/18/PNSQkI6rLJXV20vREswh971etWsVlHPSZbdq0CR999BESE807/Z0ZrGCLFgFkseOvmNpGUclV8e5VnM3RlxWxgCRPWLIGMp3l7cwx2dtso4TaI4LSC3HUbWrLfNoUNpr9+w+enUsRJktIQL749gfo16464ti4cWPjRB5vwZUomkJIXCqSJzxm8/Gyf3awOLNMd3uyi9qVK31K5atDQjmNX93lfQLFf65gP0rFTDysWU3xSlf6FEENTWvLEVUFV0RLXTdY0AXIiy++yBGv119/nSfTzJ8/n2dnUzMT1aDWRhx6quO2pKSEhwbQNCqabhQdHY0rb7wDm8+JSWe8FllQ5meyuDdF+ZsiOIqg9OaTJmUfKDo5cOBAHqjQq1cvniDUr1+1OPj893SXBisoqFVASJAadw25wOcGKwi2baMou2Mtw1ObY7K32kYJriOC0gux11DhrPk01U5SI4u1OkGF4SMvdanbtCHwdBSN093qYER0NJ9K48kualeu9CPaXcTRZpp/q68s5WhlRLsBiB14PTfnuIqroqU+GiyoDOK5557DQw89xNHKeYuW4LNDWiR1H4ayIPPUfF3PoqdSkc2bN7OIJC9G6tanOl8ajzlhwgS2fpqycKtLXotUC+bIb9FXTpqXX345d4Ffd911GDRoEF8EVLW7pNb7B31tK7R6nxusIFjn+XFdMOzltee+qaqAsY0SnEMEpRdiq6HCFfNpEikqTSQi2lzo8vN4E56MotEs5LLDW7g43HS2MTW1eLqL2tkr/Zg+Y/nmKtZETG1FS311VBfpQ3G4yaWIuq43d4KXWTkh1dUsemoeozQupbUPHTqEli1bssC96aab+HdTAtVrUYHKXpR59O+v24+zx1x3M/D1wQqC9YuxV56Zhaz1B845ZngGsY3yH7xfUQQgSuOGKaa2OcnXPGnXfFpbnMsm6GSYTfOIreFLFg0URXtoVDu3t1N6aEt1d7dFk5JlFzXdLLuoeQKEB8U4iQsSGZ5ErzfgnmFtav0/Xj1jCAtSRSjaQ3mclqf1HIkESpmSSCMRrXSCuzKLntavjVk+pW0p6kYi6dlnn+UU7tq1a/H3339zzaClmDS9iPEkvnbSpPKXGY8/h8K2l3Jd6JkvnkT6q9fwjcyrlbpmW5CX64l5N/KwBapNViAvV6rFFHwLGiU7bdo0zjC8dNd4jxyPCbGN8i8kQumFWDZuOGubo1D610ZayW6629csGtyJopk2KaloSoNJ0ThRF13UDVG/SM31Uz/YxtutTb1aXXVUz3dj0ours+gpirJjxw5OaZO3ItlekTXOO++8w2lcGm/oDOK1WD1YoeT0YZxZ/B8ERTdC3MXXg3q6i3b8iMxPZyJt6qtGX1JnvVwbYrCC4B5VVVW4+eab2Vbqww8/ZFN8or59YgXvx3cURYBh2rhBzSLO2OaYCieaUhDWwnqKzVctGlz1JTSFuqnLj+1CZMfBUJt0RBN11UVdn6LFk/Vqnuyopsiip96fvZTp2bNnsXjxYhaSe/bs4U5yiqjQibBDhw4+Y67ubYMVcjd8zPZVNJFLKROJ7DwMp9+7E/kbPkLShFkuebl6erCCULdQlH/SpElshE81x1dffXWD+cQK3o8ISi/FtHHDWdscoirnJCozjyC677gahtMK3txt6moUbc3BMziRW+ZwvZL9G7npxjLdXZdd1K6KlkqdHgYPle15ql7NnY5qSm3S+6LaVapRrTh9EJUZhzgdmjj6AUR1G1FjHepyz13zf6g4+RdUQcEIv6Av4offbrRWMp1FT5GTn376iUUknfDIO3Hs2LE8AYZmVFPnsiMcCeZAPWkqgxVoCADZV5nWHAfTxWqzLij9ext/tpZ1x3Xt5Sq4Tm0uDMmDmBrV1q9fj+XLl+OKK65oUJ9YwfsRQemlmDZupN44x+n1KAVlr+PUV7pN7WFqrHzSiphMjQlDZmG1v6Zpk5I6Ig5hLXu49dx1IcZJtLRKjMS4J95D5qc1Iz5E6pSXOVJKZH4yExUn9tZYJqxVL+Psb0vx1ZCz6KkOteDXzxAUk4SQ5Fao4HGgNdEWZiPzk0dYzNMUHprzW7jta454pd30Ko8erdLpcd/i39DmxAruzKYZ8DTlhQzUb7jhhhreiY72H/J7NTjoMg/Ek6YyWMGgq+IIpSVka0WTnKrOHjful/Xl5So4h6v7uSlFRUU8Enf79u348ccf2QmhIX1iBd9APmUvJtC7TS1x1ljZUkwSaVNfabAuameYv+6IccJQdO8xCE0zL3oPjjc3A+eatiE3md8XleA19Wqms+jpdTW952MERcWjIuMwMj+cYXWdgt+WcN1dys2vsyE4Edq4HbI+fxzFe9YgusdlnNbfmVGOdUu+w03XXYdbb72V58F7av+x12UeKCdN08EKIQlNObJMddyKRRmJTLqP0BblQNMAXq5C3e3nVHd82WWX4cCBA/j5558xYMAAl57fUz6xgu8h32Yvpq6Np32J2hore5K6EuOK+KIubULTrDMiO5j7ZFqi1kTUqJ21pCHr1Uxn0ZPTAIlJR5Qe3IzwNn2NYpIIb9kDwQlNULp/EwtKRq+Dut0QbNmyBSNGjEC3bjQTWOXR/ceyy9yy0cmfT5qmgxWie41G7soFyPlxHmIumsjNfgWbv2A/XMV9oiG8XIW62c8fHNocCx68ESdPnsS6devY4F4QnEVsgwLEMseXu02pS5hENTWcNISQrGsxrogvy0lHFBWyBz1ONWz2UOrVvG0WvSU0zlBfms/j2yzRpLUzt6lRB+GCQWPZ2oZqJumk99VXX0Gv13t8/6HllUYn2k4gYOqxGt1zNGL6X8sDATLevxsZC++BNi+zWlzSRxEaZublSg1u9eHlKtTNfv7C6mPITunDdZMiJgVXkQilD0CNG9GaEDz/435o9XpO+wVKt6knu4S9VYxbiq+cH9+AgYSiSs3Ryvhht0KTZt6xXZV7GumvTOQ6NnVkHKK7X4rYi6/nRpaGrldzZha9JUrEyzJtX31fPPTlRTBoq4y+qtllwN7V6/DHll/ZX5K6Tzt37ozHHnsM1157rXHmfH11mfsTlh6r8UOmIqbfBK6XpPrW0OSWyNvwIT9G0WNrXq6EpZdrUGySWaOgLwxW8BU8uZ8H9xqPfWUx6OKRrQmBhAhKHy2qdoQ/dJsqXcLZ37+Gkr1rbC7XZPoiBEc3crpZxZvEuJn4CgphM/rw1n3OzfVOR+G2ZTjzySNInfwSQlMvMNZTUo1naFJL6KvKUXrgV05DkshMGvdIg9erOZpFbw2DttqzkBpvLFEFhRqXUQSlkjIdMmQI33777TcWltSYQ4bls2bNwuDR43n/qcg4hJI9a9jsn+r61OEx0DRuz9G0kHOCqMbr0WmR8b97UZVzAnHDbkVsvwle0ehUn4MVTD/DoLAoBJm4IZAFF9XxKj6UDeHlKpgfJ53dz6n+lWqSK8l14ewxLlOwrBcPhP1c8DwiKH24+cQa/tRtqnQJR/e8zEp3tgG5K99CcGwKi0lXmlW8SYybiq+wph35ZqRtP0R0uBgZC+/liJAiihuNvt9sG1FdLkHOT2+i+M+VqDh1lVnXbUPUq9UmlakK1hgbPiwx6CrNlrH2PGReTt2of/zxB55//nnccsst6HJPBbQxzbi2r+LkfkR0GIiQ5JYcDS3a8T0yPrif/RVJmFtStP07FkmBaMxtOVjBmgVXZcZhjpwrEceG8nIVzh8nnd3Py/7+A8V//syR5uC4VGhzTwXkfi54HvlG+0nzCZXgkRB6/IqOuKl/K/g6pl3CNBnIcjoQ+ePxGEWLaUDONKsQjWPDkFFQ3uDWL47EV0h8Y57sU3pos1mnrSUxF45nQVl2bFcNQenM87g7lu2ff/7B3r17+bb10CmgyZUubUNp2tEV59bcfnEe1GHRNcaIWkuZ9unTB8uWLcMPv+zA9B8y2O09uu94NBr7sFn0M7LjIJxeeA+fhBuNecj8+Urykf/r51wnGKjG3MpghZJje9jyKaxVT454VZ4+gOLdqxHWujd3c3uDl2sgY3qcdHY/p0armIuuhjpEg9yf30aRFUEZKPu54FlEUHoR7oyoI92p1xkw+9u/UFSurfWUFG/BtEvYGtQkQBIwstOQGo+R8KLuU1vNAbTdUZ1S8fCl7Rvc+oXKGRwRHNOIayVJQKs01qOlQbQMz1Ausvq4J+rVaKzhqVOnjMJRuf31118oK6tuDkpISECnbj2BJvS5OT+rnKLMlOYnU35LKJUXmmJ+keQoZfp7rsa4/5hFfc9BKcDQRs3ZSN2SvPWL+HGaCGMpKAPFmFsZrBAUnQio1dxwQw1gZAdE0ciYC8fZvLhxhC8PVvDm46Sz+3lQpGPHhUDZzwXPIoLSS5DmAee7hKm+rfTAL9A07cgnOFebVYyNKmM7N6htCZU2vLPhb6uPVWQe4dF2Faf2V89EVqlQlXcaGitd0IQ2P5N/KhNlTKlNvRqNMty3b18N8VhQUD37JDIyEl26dEGPHj0wefJkboihv1NTU9nCZ8hL62ymTG1B9aMle9Zyqjk4Jonvo4grpeRiTKJhzqRMHXWZkzjWleYjpJH5d4Tqy0r2rkXq5LlQ2RDEgWDMfX6wggop1z1bq23Ut5drIFLb/dwZAmE/FzyLCEovwNURdcfn2E4nUq1hyqTnfLqo2lGXcNk/O7heyzLd7UqzijcYK1ONks5gPm+cBCGJyTOL/8P1oFHdR6Hoj++AoFCc+WwWUiY9z+/PNP1LJw16n0oDkiX2xFdhYaFROJoKSJpAQ5A1T8eOHVks0uQM+km35s2b86hDZ2bR8/Ns/w768hJjSptGiJJVEBHTewzUYZHsX0if2ZlPZyG6z1gYqqq/DyFJLRHVdaTTKVNnusxpcpKuKAdxA280+z/mrnoXER0HcYmF0q1sDW/Yf+oaGazg3dR2P3eFQNjPBc8he4kX4OqIusQrH6xxX2XmYRT98S3XOvl6UbWjLmFOd6uDEdHR3PzblWYVTzWq1HZiimntk8LZ5XOhDgnlSCTFFcNadEfxrhVQhYYjaeLjOLv0GeSteR/avNOI6DSY6yspell6eAvPvo7qcVmNCKYiviglTZMvLCOO6enVHpUkDtu2bcti8V//+hdHHLt27Yo2bdo4NRPb3ix6onDrMugKz6f3qSYUdKPPqfMwFpQUlUy54b/IW/s+8jcsgkodzEbn8ZfcZiagHaVMHe0/1Lmdu+pt3h8iuw433l+yZzVb4ySNf9Th+wsEY24ZrODd1HY/d4VA2M8FzyGCsoGpzYg6a00nOSw+VYjsOMTni6rtNZBQFLfs8BaEt+qJoPAYh9uy16xS20YVd2bk2qsRjWh3EUcUtLnVgrLsyFZEtBuA2IHXs3gMa9aFo7PhbS5E6aEt0JPPn0qFkMRmSLh0OgtKS2j7nzzzLzw7frPR+LtFixYsHK+//npjxLFDhw4ICzvfhevJWfT0Gpre/T+n1gtNamE3xepMytTe50oNPllfPs0NI43GPWqsAyQjeeqkJ79FJd3uiEAw5qbBCtnFFW6V41Dkl8ogbHm5+vMYy7qkNvu5p5/HFeRz9n/k02xgajOizhIyfC49+Cs0zbtUN3D4eFG1vQYSElLc3d3ZPN1tC3vNKq42qrg7I9dR7VNMn7F8O/7SOER2HIxGV/7b7HFViKZ6tN05GxZHUHNSRNFJXDqwN7r86yYWjp06dUJMjGMh7sspU1ufK6XczyyZzT9TJs9FMDWcnINS61R3S+luJdWtpOSp9ISNuaMTzDpoA8WYmxr8GkVpjO4Trnye9D1RGQw488MbCG87CRjWxmMXZYFObfZzTz6PM8jnHFiIoGxgXB1RZ42yv3+HvqKkhsjy1aJqa8bKCiV/recUMFnpOIOtZhVXG1U8OQvaUe1TSEJTrqM1tQkif0a6j99TUQ7MHRmtExYagp+fu7nBUowNlTK1tv9Q13/W0megzTvFNcbU+WoKNQKRcKTxgpYU/raEb2m3zENoSuuANOamfffiCxo57Y9r6eX6XM53uPvuu5HapjM+OwyPXJQFOrXZz12ltvu5Jy++Bd9BBGUDUpsRdbAhsmjKSmT7i/2iqNqWsTI1rdCEDoreqU1MkpWUJUWPnG1WSY5QIyI0qM7tnOhASjcSVZQ6pGjP6i277NY+kU9c7soFyPlxXvXMZIOe34cynpBOGs5w5+DWDX5w9kTK1NXxl5b7Dwlzqk+tOH0AyRMfr+FpSkT3GcMlB5b7W+6K+YjsOgIRbfuxiX4gG3PTvkQ12cao06EsPr444+U6b9487CqKxH0/ZcFw7iLJnYuyQIWOaTt27MA333zDt8r+93A5jLP7uas0iw93eT/35MW34FsE1hHRy6jNiDpLSEjR5IPwC/pAfW6smT8UVVt2CSsTOijlay3dTf6F2d++5Fyzil6Hwxu/Rbdu/+EGFLK9iY2NrRc7p19W/4RvP3oHyZNfsrlcdM/R0BZmcxpWGTkZmtqWxWXh5i+gDg1z+n/oDynT2oy/NN1/8tYu5HpUqj3VlRWjeO+6GjXJvH9YNDQpqW+yXBFj7vNQapLKaCjz4Wxd3P9tTseZFucaQ1zMyFi7KAskKioqsH79ehaQ3377LXvBxsXFYfTo0VC1S8ZvOSqn93NCW5CF4r1rq7d9zveVjPyJ4NhkbmZUBOrhjcsx/fhXuPXWW9GrVy+uha3Pi2/BtxBB2YB4otiZaicpYmVpoePp56lvLLuECWpYUUfEWRnDCATFJkPTrLNzzSrqILx29wR8uzgb999/P/7zn/9g0qRJuPPOO9G3b1/jQZPSNg/P+wwnF8+0+hpTp7xsrGUkb8yC35bwLF1tcQ6CoxIR2W0kYvtfc74g3mDAbxVNcf0td2BNzQmDZsQPmcoNItR1TIX1NCaNmkaIYBvzpy3xpho/d1OmrkZaTfefyjNHjVZFdLPEmclKpogx93lIPDq6WPVmj11vbRTJy8vjUaIkIlesWIGioiJupps4cSKuuuoqDBo0CCEhIRwtHvn6Rpf2cyoDopGYpih/a5p1MQpKOm5N6JqEZR++jQULFqB79+4sLG+88UYkJib61Ocs1A8qA8XQhQZh3+kCXPHmL1YfU7q8rflQmnLms8f4KrPZvYtrjKZT+OHegT4XoSSmLNxq7BL2FEqXsGKnlJGRgf/9739477332EKnZ8+eLCxvuOEG3L3kL6xZtw4ZnzyK6N5jEJrWzmxb4a17GWszKdVEZutR3UYiNK0NKk6RQfYaRHW/FImX32s2IrNdrAoH8l1/TxkfzuC0d5O7/2ecoWwLksR7n7rUK06OltQmZeot+w9FbdpE67H6sascRmuE6osySmNWaPVmZv2EpnEHxA+7xViXqpD5yUxUnNhbY1tUtkLz7DXBaqyeMaTW5Rze2ihy7NgxYyp748aNPNKURomOHTuWRSTZeFnb5+r6OKnVavHzzz9j4cKFHCEli7Fx48bhtttuw/DhwxEUFGT8nAtPHOCL6vL0PdAWnOFxnZrG7Xm6Ek3tMYWm9+Su+T/OItHgifAL+iJ++O3GY6q7n7NQ/4igbEDo6rjLUyutpr2dEZTa4lyceutm9hiz9GD0BWHhysnIU9g6SNHBe+XKlXj33Xfx/fffI7Z5B8RMehHlx3ezoXijcTMR2cHc99J0NGDmh/9G7IBJiBs82Xg/paAKty1H2q3zEJpsPjpQoytFRZDzB0pK92d/8yLih93KkUtHtEiMwIaHXIu8NQR1GSGqi/1HrdfixLt3YuSAXnjnnXfY4F2wjSJ2Sk8fNpr1R/e4DAYYULTjR3ZfSJv6KkISm5oJSoqixQ25yWxbZKsW3rJ7jYtCTzaKGJ/r3ON12ShCp97t27cbU9m7d+/mQQKXXHIJi8gxY8agadPz/xdvOE7SBK3FixezuKRBCM2aNcPNN9+Mg2kj8cfJImR+9TwqTu5HRIeBCEluyRfARTu+h6GyHKlTX+ahDASV9GR8cB9nX6h+mR4v3PY1ezCn3fQq18PX9nMWGg7vyYkFIErzQG0p/WsjN2zYS3f7cvOA0iXsSWx1CdNVNtUk0cGdIgUj/vUUH9As61UpQmVJxYl9/JPqN02J6Eh/G1Cyf5PZ/bTZVk1SamxfoTx9L0eeC7YsRdGfPyPnp3nI/vZlhLXujWiLEYTW8KUaPyVl2rN5PP/05L5aF/vPf6/uiWWL3+eTP5m/UypQ8fcUrHvskjCjlKoqOJRFBV0QkfVV6pSXuAyEopaWqDURnKI1vZGYtPTYdRZKx5LoInGrbMOVRhFa31P1kJTCpo53EmNUYvPWW2+hW7duWLJkCQu2n376CXfddZdTYrK+j5NJSUmYMWMG9uzZg61bt+Lyyy/HvA+/xNb0Qv6fRfcdzxmUhJF38tjbuIsnIfXGuXzcLNyy1LgdKg+iOveU659nq7TYAdfyRXtV1j8o3rOm1p+z0LD4ptLwI2ozos60u5uu2sNadPV5YeFNXcJ0oD+jPgKd/nyXec6Pb8BQWUaFRVyrSZFCTVpbo6UPQSfMGr6R5xqGTKGPurCsyuZJLYh849RqbsohI3eaV04po5gLxzllUCw1fnW9/zTn6MzMmTMxffp0fPbZZ3j//ffRvn17j7xmf/TYLT+xD+Gte5sNIwimY1ezLij9exvv5+rQcLP1SYRQfbjl/a567DZ0owjVQ/7www8chVTqIVu2bImrr76aU9kDBw7kekhfOk5S6v3CCy/kW/Ly3fj095P8fwprWrOznFLdZF9EKW6F0oObeQoWNQEphLfswfXhpfs3cRTbl72UAxURlA1MbUbUEVU5J1moRPcdZ7Oezl+EhVmXsE5vNv/aGSgQSAfJu4aad/E6ZecUFIKI9gMQ3roP1BGxqMpOR+G2ZTjzySNInfwSQlMvYN9IglI9IXGpNSKXuuLqqIgpGQXlnM7Zeiy3hrAMiU+zOy3GHs5Mkgk06qLLnFwB3n77bW7muv3227lhYfbs2XjooYfcFgf+6LFLF12WF1zGiy6dlpvPTM36q3JPI/2VifyYOjKOo12xF1/PtXaueOw2VKPIP//8wwLStB6SopHUAEgikoYMeLoGtyHcFIiNh+3Xb1JqX1eaz24JBAVI9KX5CLVwVSA0ae3YtcTXvZQDFRGUDUxtR9RRzVGLmd8HjLCgK/Dckkq8tLLa3NtVXl9zGPGRoU75m5naOdEVt9lVd9t+iOhwMTIW3std19QkQJZNQTHJyFu3EOoQDR8oyYQ8f+PH3FFuqKrpG0nbv31gK2xPz6v3STKBSF11mQ8ZMgR//vknnnrqKTz++OOctqT6MrJYCWQsPXZdMesPjk9DWItuXG+nrypH6YFf2YeVRGbSuEec9til2sJH3lmGvF2r7DaJGAx6lOxZyxfv1ClNdZ3kOUolK7H9JpgJ4Se/3YcBFzSqsT9Q2QPVQyoiklLCSj3k/PnzuR6ySRPn3Bl8yU3BGS9lcufQFeUgbuCN/Lfip0vZtRqvJyqe//80/U1pMvVFL+VARWoovQD6IpMQ8CT+JiwobfXiyoO18u2k4ykVrFPairbjCEc2S+RzSZN6ytN3V58gg0ORfM1sTuedXfYCTr19K7K/fxWxF0+COiwaKhu+kSRw66v2SThvzL3qgcGY0q8FNy5Zfuvob7qfHl89YzAv7+j/GRERgRdffJFrykhYUBrw0UcfRVlZGQIVS49dMuvX5p5is/7K7HRUnj3G3xFrZv3UYBg38AbODJCFTfLVT7BbQumBTag4daCGx64tSFTl/Polp1fDWnRH/IhpvJ3yE3uR8cH9/Bp4O1UVyPnxdTayj+55OeKH38GODgW/fMojDE37VinyR9s1rYekekcqk6HPneohKVr95ZdfIjs7m+shyeu2PsRkXe/ntfFSrso5gdxVb3P0mZpHCYO2ovo1mIwxNb6uoFCzZZz5nAXvQSS/F9BQI+p8hfpOWznj38gz03VaPhmpNBEITWqBtNve4pQ4jfCj9A4Jzbw17yOseRebz9MQNaKBTm2MuZ2BLF7++OMPzJ07F88++yy+/vprrq0kz0B/80l09aLMXbP+mAvHo/jPlSg7tsssNW7r4k9pCIrqOw6JYx8yEy+RHQfh9MJ7uEmk0Rh6LBgpk18yy0RQDR9FKQt++QTlx//k+j7TRpErb7gdG777AsXFxWjVqhWuvfZaYz1kcHCwX+/nzl5808VC1pdPcyd3o3GPGiPTqmCNWe25KQZdpdkyzjyP4D14x54veERY0JU01eX4k7CgtBXVBNmiKvcU8jctZi8zfVkx205EdhqCmH7ja4xndJS2qqys5IkUS77+Boa4y+3WOJGtCQlG0+gjLU/C0nTGOnXhWzNiN52R21C1T4JzxtyuQPWTlPqeMGEC11YOHjyYO3r/+9//IibmfEOKL/okuoK1izJ3zPqD6AKOsg3lRU5d/CkNQc40iZDYtLYcTUciQUnLKYKSoKzE3+rG3JRF9j51UQ/p7fu5o/8/NZZSdJd+pkyei2BqNDRJaxNK46mlCOWsjoWnsjcNaRBsI5+SF0HCYs6EruwBZstSxha0fIi6uhs57OgG+AuUXlJmwlqiLTzL/o9kIh7d60rEj7iDoxd0Esj+xvZoQ9O0VWlpKZYvX46pU6ciJSUFl156Kdb+/BOiUM6PUxrMEqqzKj28DWEte9psiNJXVSB/42KuE6LZ447snOiCgnzfqJaJcPT5K4/T8rSeiEnvolOnTti0aRPeeOMNLFq0iEUHTT5xdPFEvo00+YScH2gWueWeT3/T/fQ4LUfL03reBl0sWduDg8KiENasM4tJovzYLvamNPWhtHUBx+ufM722vCiz1xBkq0lEHWFf4Oto4hY/p/lyFGlL7jEMjz32mE2z8UDB2udM5QtZS5+BNu8Ukq95ksW7KcHRjbjB0dL9QvH0DU0x9+y19zkL3oVEKL0Md4uqn8v5jq1MyNeMugp9GSVtZYuSveugryhB2uQXjZFBtpugIvu9a6ErL+YTmCVK2uqK62/D+m8/Z1FJJ/z77ruPI0v0v3v6u7/4pJ25fC7UIaHQNOl4rsv7BIr/XMHdqfFDbzZu8+zyOSweKdVNfpUlu1ehKj+TayvJU88ZOyel9qm+JskIdQt5m9I+RVGsadOm4YorruC58a+99hoaNaqOuJmWdSgR6tr4JFLJjDMNZ/XtsUvi155Zf2XGYbbgUi7M6LtDEUPTCBUJQGrKUablOPLYddQoYtkkYovCrV9xOQvZHVkijSLWP2eK3tLUsIrTB5A88XE+blqD6mOpEYqCAsExSXwflTNQnW2MhdeuL3spBxryKXkh7giLefPmYdeuXTzzlboOyYjWH3zsrKGvrD6IBUXGmd3P3YMqNVRq27s3HfiOBjXBk08+ifHjx6Ndu3ZW7Zwi2l3EJyCaeEPPRxGSiHYDEDvwem7OUaDO7uI9q1G8awWnwjVNO6PR2IdrjJVzxs6pPmqfhPqDPAdpCtNHH33EptD0+5tvvsl1dxTdamifxPrw2CWz/oJfP0NYq57caV15+gCKd6+uYdZPUavsb1/iIQH0/aIa5dLDW7ikJarHZdCcs5qx57Frr1HEWpOINQo2L+HoacKou6G2clGqNIr44kjbuvycaTpY2ZGtCG9zIXRlxSjeu87qPPHY/tdy9/6ZT2chus9YGKrKuL42JKklorqO9Csv5UBCRi/6CK4Ii5MnT6J3794cdaOTl60icW8XK0NeWmc3wlF2dDuylsxGeJt+iBt0I9Th0TwnOGfFfD4oJYy4w63xhPUxS1wILDIzM3Hvvfdi6dKlHLm8bPqzmLvuvOGzu8yd0NVryh/ogpjS8kRVXgZyf16Aysy/jWb9UV2GV5v1mzTMUFQ/f/0iHj2rp5SzSoWQxGbcnU2C0jS9TF3MSRod/08zMjL4J93+LtDh+4qOVuvzMhc/zBeTqVNeNqvrqznm9CVEdRuJxNH32Xx/y+4awBOeAh3Tz9nWHHYFU6u7yrPHkbf2/epZ3upgNjqPv+Q2BEWa/0+pC10yMb6BCEo/ZcOGDRg+fDj+/e9/s6WJrxX8U9qqq40556bk//o5Cn/70sxmImbAdYgfPMXhcziac16fM3KFwII6wKc/Mhuh455h+5qSPWvseiUSRbtWcLSchhroK4oRFJWIsOZdEXfx9SzQvHH/qouLMtD40zOHkPn542zdY2nh1LTrRagY9m/zVcpLkPnpo9AVnuUmEcu6PoWyf3Yia+nTCG/ZE0kTH7c7meqHewdKhPIccvEtECIo/ZjXX3+dU2xffPEF+o+40uW6TDJcr43ZrSfYd7oAV7z5i8PlKKVSsm8d1+SQD2Tp37+jZPdqxI+cxqMqHeHopEC1bZ60c/KmCJLQsEx69xdsO16AM1+/wFOWIjoMREhyS46kFe34HobKcp59TQbfRM7KBZwCpnphSsOS+Cz6cyW5aiPt1jc54uZtJ2GPX5QZDFBDjzHqXWiTFo/U1FSzW3R0NGdeuphcjFKTyJnPn0DlmSNImfSczbo+Mlk/89ljCE1uheRJz/GQgtpejAYacvEtEPJt8GPuv/9+bNu2DQ+9/TXC90T7VMG/M75jJX9tQO6K+Wg87d1qX8hzxd500qG0GdkHmc4Ors3ziE+kUBdQpmDLsWoHgei+47ne1p5XIpF46d01thPerj8yFz3ATWix/a8xNpwdySqqlzRhSUmJMdVsmnY2vZVGXYCg/lM984QqFf47oQeu7DbarFynsUm5jmmjiLNNItRsR56JNFs6iRrp7IhJQhpFzBEvZYGQb4QfQ/VG/W6djc1rjtTqyrGhCv61Wi1OHP/H4XJFO37kphdFTCpEtLkQJXtWs72PqX+cNZzxNxOfSKEuG86c8Uq0BQkggtwOFGi7i7ekc2NXbb9/WVlZVsWhpWgkY29TaNwgRQrJgistLY1ruenvo+H52FRg3jxXG3o1j8OC9X/zMcleuY7SKHJ2teMmEeosP7PkSR5IQD6ZZUd+N1smJD7VTIhKo4h15OJbEEHpx1C69rU1Nb2+6mrCTG1PXvv37+eOdOVGXerlWgOa//tLjkjYgr3krHVgUo0Vofy0gSv+ZvU9I1fwb+z5JJp6JZINlSW6skJOc5PlCnVOEzRa0Pi43sDOEOQSYLq9goICu5FE5TEaGWhZCUU2R0pambrW+/fvXyPdTLf4+Hibvoym1kgu1drx/G81DFDhz5MFVtc19eckdwYSnrQcXVQSZUe28c0SFpRlRVxbSVBmw5LILsPNBKUjl4ZARi6+AxsRlH6KvQkz2d+/Zhx/Zo0m0xex+ayzE2ZcEY9//fWXmXj8888/jTOPybqHIhpkeUQ/n9imw4m8aoNxa5CtSNmxnTwtx7R5gVLhZBtEFhSeTFuJT6TgCRz5JDrySjw5/ybg3Ng6auCJH3Enwlv1NFvmeHYJxoy/GmczThrForUGFooiKmKwbdu2xt9N709OTuYJQO5S24sydVAQf690BufLdUh4kvd/2uQ5VAFjF2poMu0+todSoyrfa9vIxXfgIk05Adh1R9Y6VXnVkyfOY0Duyrd4fm3j2xdY3aYrBf9VVVXYt28fi8YdO3YYxWN5eTlHMNq3b8+iUbn16NGjxmi6p77dZ/Q3swZ52535bBafVKN7X1HdlHNkG8qPbkdU91FIvNy25Qe9lyn9WtQ6Legr1kuC9+Go4Yy8EjM+epBT3ik3zq3RaVx+fDc3mtByJDypmYfqJy1pffALtIoPsRpJVBpYGgpnLsoSIkKx80Q+vAlpFHENufgOLERQ+rkvmLOUn9iHM588grjBUxE74Fq7y1r6gtEM7L179xqFI912797NERG1Wo0OHToYhWOvXr1YPDpzMnPmfVBnZv4vn6LqzFHoyorO+9tdNNGu5Ye19yEI9cHO9DyMf3uz1cec9UpUIH/HjIXTETfslhquBr7ik2jtouy73ac92uDhKcSlofbIxbf/I5+mH+Jowow1OE0MFXdG24O2++p3f6CH7rBRPO7Zs4dFJYnHjh07snCkEXNK5DEysnZzWMkHk6yL7PmbkV9fyrVPu7RdSVsJDYmtRjDySjyzZDb/JK9ER2KSCIlP48Y0ilRaCkpnGs68ARIVptZdpuU6FZlHkL/hI86qEJrGHRA/7JYaE6gMOi0KflvCfp7a4hwERyUisttIjtw6urB0FmkU8eznLPgfIigDsODfEjoYlx74BZqmHY0Gybag7X6z7QjeW3g3OnXqxKLxpptu4p/du3evtXi0BdXUkHWRJw1zqfCbtisIDQFFZijdZ7pHUwo7a+kz0OadYq9EW8bb1tBXVRprKmvTcOZtUO0dNXSQmDyz+D8Iim7E5u0GGNjZIfPTmUib+ipCEpsa18n+7hU+htF0m9C0Nqg4dRAFmxZzs03i5fdyPSXl4tQuXmhLo4ggOI8IygAs+Lek7J8d0JcVIrLTUKeWD0lojIzsXCTF2fd49ATibyb4G6Y+iYQzXom0DI0sDLJwNaCSj6qzx2pkFnzVJ5HKXKiRgyBBqAoOZXN3xU82svMwnH7vTo5aJk2YxfdVZBxC6YFNiB0wCXGDJ/N90T1HIygiBoXbliO695VsVk70aBqL7en50igiCHWA7x1xBLsczylxOK7QarpbHYyIjgOdXier1IAk923lnEL8zQR/Q/FJJNGSt9axV6Khsgyn3roZER0HcfRSFRLGQrJ4z2qoNZGIvXiSX/gkmpbrUF13eOveZsMJgqMSENasC0r/3sYCWx0ajooT1enxiE6DzbYV0XEwCrctQ8n+TSwoabtdm8RhzoRu0igiCHWACEo/w5kJM6bQQbns8Ba2HXE0Vcad53EX8TcT/Aky3ya/RMIZr0RViIadC6jDu/TgrzBUVSIoKgGRHYcgdsB1ZqUqvuyTaFquY9BVcYTSEvpfQKdF1dnj0DTpwMvx/RbL8nL0/808Yu7PObZz9Q2dpVFEEDyIfHP8DFcL8UsPbeH5wJGdh9bp83gC8TcT/AVqOLuwWTR+P1GI1BvnOFyexjImjJjm1w1nluU6IQlNOaVP6X6lsYbEI91HaItyoDm3HEHz0EPiUo3rK5FLXXGO8T6KSJKINB3TKI0iguAZRFAGQMG/PUr+Wg9VaDjC2zr2lvSGgn8xFxf8gSVLlmDti08g8po5VqNwgdhwZlmuE91rNHJXLkDOj/PYBgwGPQo2f8HWSkojExF+QR8ExSQjb91CnsEdmtqm2k5s48eAOoijuQq0fYpIiogUBM8jgtLPC/7toSstQPmxXYjsOBjqkDCnn8MbCv4pwiNpK8HXyMvLw/Tp0/HZZ5/hmmuuwajRHfDcz9Up70BvOLMso6HGGm1hNgq3fm2c7BWa2pbFZeHmL6AOrT5mkSBPvmY2sr+Zi7PLXqheOSiE7YUKNi+B6txytp5HEATPIGddPy/4t0fJ/o08J9eVdLc3FvxL2krwBVatWoVbbrkFxcXFWLx4MW644QaeGlWOYGk4s1FGEz9kKmL6TeB6SWo+Ck1uibwNH/JjwSbjVkOTWiDttrdQlZ0OfXkxz0AnoZm35n2ENe/i8HkEQXAf+Wb5acG/M00rZIasjohDWMseTm/blwv+BaEhKC0txb333otRo0bx1CgaBHDjjTeymFQazuZM6Mpj/eiCzRVoeVqPJrhMH9YG/lCuYwlZJYU168xikqCsCnlTmvpQEvT/JGFJywaFR6MifTenyU2Pb77szykI3o5EKP0QZybMEGlTX3Fpu75c8C8IruKJUopt27ZhypQpSE9Px7x58zjdTROlLJGGM+fKdSirUplxGPHDboVKZTseoq+qQP7Gxec64Qd7VbmOIPgr8s3yU2TCjCC4jrHZ62AWdxzXaPZKiOCSEsoC0IWbLaqqqvDcc8/h+eefR8+ePbFz506OTtpDGs7My3XK0/ei4NfPENaqJ9ThMag8fQDFu1cjrHVvRPe9ymy9s8vnsHikVLe+ohQlu1ehKj+TayvVmgivLdcRBH9CZTDQQCrBH/n893SPTpihtJov12gJgi1ofrSr0UHKAliLDh44cICjkiQin3jiCcyaNQshISG1el2B1nBGYnrk6xv596q8DOT+vACVmX+zXy55bUZ1GY6YC8exjZIpBVuWssm7riCLayc1TTsjbtCNNWZ+r54x2O9EuCB4CyIo/Zz56w57rODf12u0BMHWhZc7hvk0GpRS1nq9HvPnz8cjjzyCFi1a4OOPP0bfvn3r9LX7I1MWbnVYruMqSrkORYAFQagbRFAGALU9YVJBuyYk2GzCTKBFTAT/xlMXXHdcmIJ1b83EmjVruAFnzpw5iIjw/brGhooWU7lOhQftfahxafWMIX5RayoI3ooIygChNim9sn924P1pw9Ghc1eP1JUJgj+XhOh++wj/e+x2jBw50mPbDFSkXEcQfA8RlAGGswX/1/dtiknXTERQ/8nIDk5yu65MELwxClZWWsLG2TRZpTLjEHsYJo5+AFHdRtRYx2DQo3jnChTt+gna3FNQBWsQktwKCcNv51o9iYJ5FinXEQTfQgRlAGMvfW1Mk+v00Lmwh1jWlQmCN9fpVeRm4tQ7tyEoJgnBcamoSN9jU1Bmf/8ajyqN7HIJNE06wFBZjsozR3kwQHirnlKn54X1rablOoIg1C0iKIU6iww8NKodmzYLgrd2Ehu0VRyVDIqKR0XGYWR+OMOqoCzZv4lH+yWNn4WI9gPsbl86ib23A18QhLpDuimEGhEBT4hJgraTFKWRCIHgVVDJhyI8VMEhLCYdUfj7coSmtWMxSalvQ1WlcZa0KbTdxVvSec684BnEn1MQfAMRlIJZJIDSS/aoyDyCgl8+RcXJvzi6w95wPS5DTJ+xVpd/8tt9GHBBI4kUCF4DNZe5kj4lo+zK04cQ3Ws0z5Eu2v49DOSLGJuCuKE3I7LjIOOytF0SPE9BBKWnoYY/Eur0vxW3CUHwPuQbKBihtBLVKtmCur6zlj6D0JQLEDtgElShYdDmZ0JXlG1zHdoebVfqygRvoLhCy04FrqDNy6DkOKe9oVYjfugtPH2l8I9vkf3Ni/x7eOvexuUpekaCRwRO3UH/286NYxv6ZQiCYIIc8QSG0klUo2QvSpP9/asIv6AvksY/aneOrikUsaHtHskqkjSU0OAczykxS5U6g76qrPpnWSFSp74CTeP2/Hd423449fZtKNj8hZmgpO1T9EwEjyAIgYRzqkAImLoyW1B3q74kH/GDp7KY1FeWcy2ZMyh1ZYLQ0FCK1FXIHoigFLciJgl1aDjC21yIitOHYNDr3H4eQRAEX0YilIJTdWXlx3ZBpYmAtjgHWV8/V+3DFxKGyC7DkDD8Dp6fawupKxO8Baq3c5WgqAT+qY6Mq/kY3afXsoWQKizSrecRBEHwZURQCk7VlVXlngb0Opz96llEdRuFsCE3oTx9D4q2fwd9eQmSrvqP3fWlrkzwBqh5g+LwrqS9g6MTERQZD11xbo3HdEU5fDGl0oQb71Odex5BEIRAQi6jBafqygxV5TBUVbCpc8LIO9k+hX5Sh3fp/o2oyj1lf/1zdWV1BYnVfacLsDM9j3/S34JgSURoENKiQ1xfr+Mg6ArPouyfncb7dKUFKD2yFWEtupnVFJN9jVw4CYIQaMhRT3Cq3ktJaUd2HGJ2f2SnoSjetQIVp1T5C24AABa6SURBVA4gJKGJ28/jCkZfOpkxLjigsrISX375JV577TUcje+NuL5joVR4FJ6LsisRyLIj26A951wQ03sM1GGRiO1/DUoP/IKzy15ATN9xUGsieQQjdDrEDZ5qVi9MXoiCIAiBhghKwal6r6CoRFRlp1fXjJneH1ndyUrTRjzxPJ6anEH3HM8txcdbj2PRb8dkckaAkpubi3fffRfz58/H6dOnMXLkSNw97So8s/38PlO4dRl0hVnGv0sPbQboBiCq8zAWlJTyTpn8IvLWLkThH9+wkNQ0aY9GYx7kOd4KtC+SsbYgCEKgIYJScKquLDT1ApQf2wltUQ5CEpsa79cWVUd1giLsW6R4qq7MdLYv4cigWnmc5jaPeG2DzBgPEA4ePIg33ngDixYtgl6vx+TJk/HAAw+gS5cu/Pi6gupZ3rR/NL37f05tMyQuFckTHrP5uDLLW+yxBEEIRKSGUuB6L0oP212mQ/U0kOLdP5vdz3+rg6Bp3tXu+iEVBfjqi0+Rn5/v1ozxmV/vQYVW79KkE4KWp/VofdqO4H8YDAasWbMGV155JTp06ICvvvoKM2fORHp6Ot5//32jmCQoWh1sxyarNtD2aLuCIAiBiAhKgaFaQ3s+lBShjOw2EqV/bcDZ5XNRtOMHnF0+h/+O6TeBO2FtoYIBodmHcdNNNyE5ORlXXHEFPvjgA05HNtSM8S9+F19Mf6GiooL3px49emDEiBE4ceIE/01C8sknn+R9zhIqfaBotSd5ZmxnKakQBCFgURnosl4IeKjBZeTrG+0uY9BpUfDbEhTvXs0NDMGxSYjudSVi+l7lcPurZwxGeFUhvv76ayxduhSbNm1CUFAQLrnkElx99dUYN24ckpKSbNZMUrqaIoyWlB/fjTOfzbK6XuqUl6Fp0sHqY5pgNVbPGOI1AsBfZhPX5/vIysrCO++8g7feeot/pwuVGTNm8D6lUjkXfaRotScuVB4e1R7Th7VxezuCIAi+ighKwciUhefryjyFUldmOcs7MzMTy5Yt487bDRs2sAAYOnQoi8vx48cjJSXFqdelCMro3mMQmtbO7LHw1r1s1nbael31ib90qdf3+9i7dy9ef/11LF68GGq1GjfffDPuv/9+tG9/fopNbetyXdn3aR+iNDdFJq+TulxBEAIcEZSCU5HA2uJMJJCiS8uXL2dxuW7dOq6FGzx4MIvLnkMuxw2f7Le5riIoG42bicgOA11+fRQ5re8mCme61BWUx72xS70+3wc11qxcuZJtf1atWoXGjRvjnnvuwbRp05CYmOhT70UQBMEfEUEp1IjWUOOKp5g7oatL0Zvs7Gx88803nBZfvXo1ki+fjrCuo2ye4E0FZXirXlCFaKBSBzn1XCQMpvRrgac8XEtXl9Ewb+lSr6/3UVpaio8//pgjkgcOHEDv3r05rX3NNdcgNNT2uE+3o62Hsni6U41oa2IE+0ySNZB0cwuCIJxHBKXgtXVleXl5uHzBNmQW2556owhKVWg4DJVlgEoNTbPOiB92KzRpbR0+R4vECGx4aBh86f/60Kh2uGeY4/fmy+8jIyODayOpRpKat6jGloTkwIEDna6PdBd/qWsVBEGoD+ToKNSATvKNojQNXlcWEhGNM3bEZPUThvAYyPDWfaCOiGXz9cJty3Dmk0eQOvkl7k73hhnjnu5ST4rSNEjdXl2/j507d3Ja+/PPP4dGo8Ftt92G++67D61bnzcPry9on+jc2L6/qiAIglCNRCgFr60ro5ncV7z5i8vrVeWdRsbCezlSmXLdMw6X/+HegXUqHFytTS3Y/AXyN36MkEbN0fj2BV7TpW7vfVSePY6CXz5FZeYR6EryufQgJLEZW0pFtLXd+ETv4+f7B+HPzWtZSK5fvx7NmzdnEXn77bcjNlYEnSAIgi8gEUrBJiRWqAu6oerKajv7OyS+McLb9uMRega9zmFNpadnjFtColyZ7OMIbWE2WzOpQsLsL6c38Hbrs0vd3vug0YX6yjJEdh2OoKgEGKoqUHpwM85+9SwSLrsH0T0us7pelU6PoQ+/i2OLHkL//v2xZMkS7vIPDpZDkyAIgi8hR23BIWT1Qo0rT6FzvdaVuTP7OzimEaDTsrBRaexH8abdditaJ4RyZKxZs2bGG/1N3pju1OyRGKcIr7PkrVsITeP2MOj10JcV2lyOosG03SNZRfXSHOLofYRf0JdvpkT3vhIZix5A4bblNgUl69PUDli66hdMHHGxx1+3IAiCUD+IoBS8tq7MmRnjttDmZ0IVHApVqP1IH229fZMEnE7/B9u3b8fJkyd58ooC1fE1bdrUKDAtBSf9tJeWpciuo3IBhfL0vSg98CvSbpmH3FXvOFyetrt4S3q9dKm78j4UKDIcHN0IFZn2R13SdveUxmGiB16nIAiC0DCIoBS8fsb48dxSm8voSgtqmJdXnjmK0sPbEN66N1Qq+1HOFomRWPzfhca/qaT47NmzPL6PRvfRT+V2+PBhrF27FqdPn2ZfRIXo6GibgnPNfueamig1TyIyqvsohCa3dLg8v3e9gcsQKHJc15BpuTPvQ19ZDoO2AvqKUpQd3oqyo9sR0bF6Drw3vA9BEAShbhBBKXg1NGHl463HbYoZmiuuDgmFpknHc13eJ1D85wpuCokferPDyBjVfppC6W2a/Uw38jy0hlarZVsbU8Gp/E5RTjJpJ1FKVkbNZyyhjTp8n8U7f4K28CxSrn8erlAfXerFFVqegOMMeWvfR/GuFdV/qNSIaNcfCaPucrhefXXbC4IgCHWDHL0Fr4bG9S367ZjNxyPaXYSSfeu5Tk9fWcrRyoh2AxA78HpuzrEHiVRqJHIVahhRopC2KCsrw/o/j+Cub9Idbk9XVoj8TZ8gbsB1NkdF2oJkdt9LRiOoMAN1hS4mDYbB9zu1LM11j+gwELqiHJQe+AUGgx7QVTn1Pqg2V2x6BEEQfBMRlILXNwSRFZGtWd4xfcbyzVWUWd511dASHh6O1MZNKfbmcFmyCFKHRyG6z5haPVfvCy9Cgr4AdUWuOhabnFyWrILoRkR1HY4znz+BrKXPIHXqqw6bm+q6214QBEGoO0RQCl4P+VqS/6ErDSGOIPN12m5d4kyXelXuKRTvWon44XdAV5RrvN+gq+K6Sm3+Ge5SDwq3LXxnPvxgnUb2yA90Uy38QImIDhcjd8V8aHNPISSRBHbddPULgiAIDYsISsEn/DBp9rMnZ4zTJJ+6NgV3pkudUsMw6JG3+l2+WXLqndsQ3WcsEkZMs7q+6tzzeGu3Pdk2EfqKErvL1cf7EARBEOoOEZSCTzCpb3NkF1d4bMZ4fYwtdKZLPSSpBZImPGY1DU5G4SQkg+PSbK5PpvJ13cjiVLd9ST6CIuPM7jPotCjZuxaqYA1P/bFHfbwPQRAEoe6QI7jgM3jLjHFPdqlXNxH1r3F/4e/f8E9rj9nrUm+o95GzYj4MlaXQNOuCoOhE6IrzUPLXemhzTiL+ktugDg33ivchCIIg1A1StCT4XKSSZlhTQ40iRuyhPE7L03r1KSaVLnVP1n56oku9Lt5HJHlNqtQo2vkjclcuQNHvy9nUPGniE4i5cLzXvA9BEAShblAZyMlZEHyQhpox7ipTFm612aVeW5Qu9fqc5e0v70MQBEHwPCIoBb+gPmeMu8qJ3FLuUq/woC2OJljNEde6bizyx/chCIIgeB5JeQt+NWO8Z/N4/uktYtK0S92T1EeXuiX0fE9e0cHn34cgCILgebznrCsIfow3dqm7GtWl+eUrF8xGwdEqxA6a7DPd9oIgCELdIylvQahHPv89vUG71I11pwezeD53jbrThAju6KYmHJpSpECHiQceeABvvvkmPv30U+CCAT7VbS8IgiDULSIoBaGeoVrEWcv2YNORbBZY9gSZ8jiNn6TJPrVND7v7nM888wxmz56NBQsW4K677mqw9yEIgiB4JyIoBcHPu9TdjYoOiTqD/5t5C55//nnMmjWrwd6HIAiC4L2IoBQEP+5Sn7/usEfqNjtr/8b3L94LlUrls932giAIQt0hglIQ/BSKTHpy/vncCV2l7lEQBEGwighKQfBD7HlGVmQcQsmeNShP3wNtwRmow2OgadwecYOnICShic1timekIAiCYAvxoRQEP4SaZahm0hqFW5ai9OBmhLXojvgR0xDV/VKUn9iLjA/uR+XZYza3Sduj7QqCIAiCJRKhFAQ/g5pkRr6+0ebj5Sf3Q5PWBqqgEON9VbmncHrhPYjscDEajXnI7vZXzxgszTWCIAiCGRKhFAQ/gzquqUPbFmFNO5qJSYJS3aGNmqMq+4TdbdN2F29J99hrFQRBEPwDEZSC4GeQabkr9kAEJSp0pflQR8TYXY62S/ZAgiAIgmCKCEpB8COKK7Q8AcdVSvath64oB5EdBjlclrwmyR5IEARBEBREUAqCH3E8p8TMWNwZqnJOIHfV29A06YDIrsMdLk/bJ69JQRAEQVAQx2FB8GJcNQqn5VxBV5yHrC+fhloTiUbjHoVKHeTUeq4+jyAIguDfiKAUBC/DOMrwYBanr2uMMkyIwLD2ybixX3O0TTHvtibR6Sz68hKcWTKbf6ZMnovg6ESn13XleQRBEAT/RwSlIHiRGTn5PG46ks3d1NYaa+ie47ml+HjrcSz67RgGtWmEF8Z3NZqNUwSTRKejtLdBW4mspc9Am3cKKZOe4w5vZ1Gdex5BEARBUJAwgyB4yZhEmmyz+WgO/+2oS1t5nJan9Wh9gtLhFMG0h0Gvw9nlc1Fx+gCSxs2EpklHl15r88QImc8tCIIgmCFnBUFoYOavO4yXfz5Uq3VJWNKNZnZnF1fgnmFtOR1OEUxbojRv7UKUHdmK8DYXQldWjOK968wej+oyzObzUeR0WLvkWr1WQRAEwX8RQSkIDQhFFmsrJi2h7SRFaXBt78acDrdF5Zmj/LPsyDa+WWJPUJJInXyR8+lxQRAEITCQ0YuC0IA1k5SurrDRMa2vLEPh1q9RcfogKjMOQV9ejMTRDyCq2wib2wxRG6D68XkUtL8Cka16QM8Vj56BopMDWifi49v6eWybgiAIgn8gNZSC0EBQA47WTq2kvrQQBb9+xj6RIcmtnLbzCe4/BR/fezlCgp2zAHKWYLWKG4AEQRAEwRIRlILQQNZA1M1tr/kmKCoBTe/5GE3v/gDxw251arvkI1ka2wKt27bD02M7e/AVA8+M7WzsJhcEQRAEU0RQCkIDQD6TlEK2hyo4BEFR8S5vm7a7eEs6JvVtjodGtYMneHhUe1zXV2onBUEQBOuIoBSEBoBMyx1ZA9UW2u66Q1n8O3V9z5nQFZpgtUMBawktT+vNndAV04e1qZPXKgiCIPgHIigFoZ4prtDyBJy6JD2nlMc2EhSpXD1jCDfUEI6EpfI4LU/rSWRSEARBcITYBglCPXM8p8ThJBt3oe3TDPDOjWP5b6p9pO5s41jHQ1ksOmuMdUyMYJ9JsgZqk2w+1lEQBEEQbCGCUhDchCKBJN6ow5pmXNNYQnuTZGi5+sDa89Ds76fGdsZT6Ozy6xYEQRAEW8jZQxBqgTHSdzCL09c1In0JETyx5sZ+zVnEmULirT5w9DwkHpUIpiAIgiC4gwhKQXDRjJz8I8nyh2oNrTXW0D3Hc0t5/CFNrBnUphH7NyqWOxQJJNFZl2lv1bnnEQRBEIT6QJpyBMGFMYk02Wbz0Rz+21GXtvI4LU/r0fpKZJAimHUJ1UJK+loQBEGoL+SMIwhOMH/d4VrP3CZhSbeZX+9BdnEFW/lQOpwimI5EaeH276AvL4GuOJf/ptnb2qJs/j2m9xiow2pGISlySo01giAIglBfiKAUBAdQZLG2YtIS2k5SlAbX9krjdLgjCrcug66w2lOSKD20GaAbgKjOw6wKShKp1KUtCIIgCPWFymAw1LWDiSD4dM0kpasrbHRmG7RVyN+0GCX71kFfXoyQpJaIGzwF4a162txmiBpQ/fQ8CtuNRmTrntB58BtI0UnyjySLIEEQBEGoL6SGUhDsQA04Wjtp6ewfXkPh78sR2Wko4kdMg0qtRtaXT6H8xD6b61Tp9Ai6aDI+um80goM8+xUMVqu4AUgQBEEQ6hMRlIJgxxqIurlt1TlWnD6I0v0bETfkJsRfciuie1yGlOtfQHBMMvLXf2B7wyo1ymJboHWbtnh6bGePvuZnxnY2dpMLgiAIQn0hglIQbEA+k/bGFJYe/JXFIQlJBVVwKKK6j0TFqQPQFp61uS5td/GWdB6L+NCodh55vQ+Pai9jEgVBEIQGQQSlINiATMvtdWFXnjmKkIQmUGvMI4Khae2Mj9uCtkvjDwnq+p4zoSs0wWqHc7YtoeVpvbkTumL6sDYurSsIgiAInkIEpSBYobhCyxNw7EFWPkFR8TXuD4pKMD5uD5qlTeMPCYpUrp4xhBtqeBsOhKXyOC1P60lkUhAEQWhIxDZIEKxwPKfE4SQbg7YSCAqpcT+lvY2P21sf4FnayvhDqn2k7mzjWMdDWSw6a4x1TIxgn0myBmqTbD7WURAEQRAaAhGUgmCFShs2QTWEo66qxv2KkFSEpavPQ7O/nxrbGU+hM0cwSXTScjSbm8YpygQcQRAEwduQM5MgWIHEmyMota0rqh7DaIqS6lZS3+48D4lHJYIpCIIgCN6K1FAKghUoEuioPSY0uTWqck9BX2Fea1l5unqqTmhKa7vrq849jyAIgiD4OiIoBcFGZLC5Az/HiA4XAwY9inatMJucU7xnFUIbt0dwTJLd9akWUtLXgiAIgj8gZzNBsMGw9sn4eOtxm9ZBmsbtEdFhIPI3fAh9aT6C4xujZM8aaAuykHL5/Q67tKmxRhAEQRD8AZnlLQg2oG7rka9vtLsMNeDkb6ye5a0rL0ZockvEDZqM8Na9HW5/9YzB0qUtCIIg+AUiKAXBDlMWbsXmozl2Dc5dhaKT5B9JFkGCIAiC4A9IDaUg2OGF8V0R7OL0GkfQ9mi7giAIguAviKAUBDuQ2fjTYzt7dJvPjO3M2xUEQRAEf0EEpSA4gMYiPjSqej63uzw8qr2MSRQEQRD8DqmhFAQn+fz3dMz+dh+0eoNLNZVUM0lpbopMipgUBEEQ/BERlILgAidySzFr2R5sOpLNQtGesFQeH9SmEddMSppbEARB8FdEUApCLS2FPtmajnWHspCeUwrTL5HqnGk5+UxOvqi5WAMJgiAIfo8ISkFwk5IKLY7llKBSq+fZ3DROUSbgCIIgCIGECEpBEARBEATBLaTLWxAEQRAEQXALEZSCIAiCIAiCW4igFARBEARBENxCBKUgCIIgCILgFiIoBUEQBEEQBLcQQSkIgiAIgiC4hQhKQRAEQRAEwS1EUAqCIAiCIAhuIYJSEARBEARBcAsRlIIgCIIgCIJbiKAUBEEQBEEQ3EIEpSAIgiAIguAWIigFQRAEQRAEtxBBKQiCIAiCILiFCEpBEARBEATBLURQCoIgCIIgCG4hglIQBEEQBEFwCxGUgiAIgiAIgluIoBQEQRAEQRDcQgSlIAiCIAiC4BYiKAVBEARBEAS3EEEpCIIgCIIguIUISkEQBEEQBMEtRFAKgiAIgiAIbiGCUhAEQRAEQXALEZSCIAiCIAiCW4igFARBEARBENxCBKUgCIIgCILgFiIoBUEQBEEQBLcQQSkIgiAIgiC4hQhKQRAEQRAEwS1EUAqCIAiCIAhuIYJSEARBEARBcAsRlIIgCIIgCIJbiKAUBEEQBEEQ3EIEpSAIgiAIguAWIigFQRAEQRAEuMP/Ay6kn/heWMT+AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n=100\n", + "graph_100 = rx.PyGraph()\n", + "graph_100.add_nodes_from((np.arange(0,n,1)))\n", + "w=1.0\n", + "elist=[]\n", + "\n", + "for edge in backend.coupling_map:\n", + " if (edge[0] list[tuple[str, float]]:\n", + " \"\"\"Convert the graph to Pauli list.\n", + "\n", + " This function does the inverse of `build_max_cut_graph`\n", + " \"\"\"\n", + " pauli_list = []\n", + " for edge in list(graph.edge_list()):\n", + " paulis = [\"I\"] * len(graph)\n", + " paulis[edge[0]], paulis[edge[1]] = \"Z\", \"Z\"\n", + "\n", + " weight = graph.get_edge_data(edge[0], edge[1])\n", + "\n", + " pauli_list.append((\"\".join(paulis)[::-1], weight))\n", + "\n", + " return pauli_list\n", + "\n", + "\n", + "max_cut_paulis = build_max_cut_paulis(graph_100)\n", + "\n", + "cost_hamiltonian = SparsePauliOp.from_list(max_cut_paulis)\n", + "print(\"Cost Function Hamiltonian:\", cost_hamiltonian)" + ] + }, { - "data": { - "text/plain": [ - "\"Output" + "cell_type": "markdown", + "id": "4e576068-53e7-4a06-a83b-87e95de141e9", + "metadata": {}, + "source": [ + "## Step 2: Optimize problem for quantum hardware execution\n", + "\n", + "### SWAP strategy with the SAT initial mapping\n", + "\n", + "We will demonstrate how to build and optimize QAOA circuits using the **SWAP strategy with SAT initial mapping**, a specifically designed transpiler pass for QAOA applied to quadratic problems.\n", + "\n", + "In this example, we choose a SWAP insertion strategy for blocks of commuting two-qubit gates, which applies layers of SWAP gates that are simultaneously executable on the coupling map. This strategy is presented in [\\[1\\]](#references) and is passed into `Commuting2qGateRouter` which is exposed as a standardized Qiskit transpiler pass (see [`Commuting2qGateRouter`](/docs/api/qiskit/qiskit.transpiler.passes.Commuting2qGateRouter)). We use a line swap strategy in this example.\n", + "\n" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 6))\n", - "plt.plot(\n", - " [result_dict[i][\"cvar_fval\"] for i in range(1, iter_counts + 1)],\n", - " label=\"CVaR\",\n", - ")\n", - "plt.plot(\n", - " [result_dict[i][\"fval\"] for i in range(1, iter_counts + 1)],\n", - " label=\"Standard\",\n", - ")\n", - "plt.legend()\n", - "plt.xlabel(\"Iteration\")\n", - "plt.ylabel(\"Cost\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "128e3c8a-b189-45a9-bd56-f1fd5783e9b1", - "metadata": {}, - "source": [ - "The following cost is the result of the standard expectation value." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "7d74369a-2762-4a4c-8a74-6f1ab00870ab", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "bitstring (int): 836126190501096404140024081113, probability: 6.333122229259025e-05, objective value: -68.0\n" - ] - } - ], - "source": [ - "# sort the result_dict[iter_counts]['evaluated] by the CVaR value\n", - "sorted_result_dict = [\n", - " (k, v)\n", - " for k, v in sorted(\n", - " result_dict[iter_counts][\"evaluated\"].items(),\n", - " key=lambda item: item[1][1],\n", - " )\n", - "]\n", - "print(\n", - " f\"bitstring (int): {sorted_result_dict[0][0]}, probability: {sorted_result_dict[0][1][0]}, objective value: {sorted_result_dict[0][1][1]}\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "12e9b599-29ff-4cfe-8870-5a9c67389776", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 20, + "id": "7c54402a-7696-4b0e-826a-6e0ac4c54395", + "metadata": {}, + "outputs": [], + "source": [ + "# Extract longest path with no repeated nodes\n", + "nodes = rx.longest_simple_path(graph_100)\n", + "\n", + "# Collect even edges and odd edges\n", + "even_edges = [\n", + " (nodes[i], nodes[i + 1])\n", + " if nodes[i] < nodes[i + 1]\n", + " else (nodes[i + 1], nodes[i])\n", + " for i in range(0, len(nodes) - 1, 2)\n", + "]\n", + "odd_edges = [\n", + " (nodes[i], nodes[i + 1])\n", + " if nodes[i] < nodes[i + 1]\n", + " else (nodes[i + 1], nodes[i])\n", + " for i in range(1, len(nodes) - 1, 2)\n", + "]\n", + "edge_list = [\n", + " (edge[0], edge[1]) if edge[0] < edge[1] else (edge[1], edge[0])\n", + " for edge in graph_100.edge_list()\n", + "]\n", + "\n", + "swap_strategy = SwapStrategy(CouplingMap(edge_list), (even_edges, odd_edges))" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result bitstring (binary) : [1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1]\n", - "The value of the cut is: 90\n" - ] - } - ], - "source": [ - "def to_bitstring(integer, num_bits):\n", - " result = np.binary_repr(integer, width=num_bits)\n", - " return [int(digit) for digit in result]\n", - "\n", - "\n", - "def evaluate_sample(x: Sequence[int], graph: rx.PyGraph) -> float:\n", - " assert len(x) == len(\n", - " list(graph.nodes())\n", - " ), \"The length of x must coincide with the number of nodes in the graph.\"\n", - " return sum(\n", - " x[u] * (1 - x[v]) + x[v] * (1 - x[u])\n", - " for u, v in list(graph.edge_list())\n", - " )\n", - "\n", - "\n", - "bitstring = to_bitstring(\n", - " sorted_result_dict[0][0], len(list(remapped_graph.nodes()))\n", - ")\n", - "bitstring = bitstring[::-1]\n", - "print(f\"Result bitstring (binary) : {bitstring}\")\n", - "\n", - "cut_value = evaluate_sample(bitstring, remapped_graph)\n", - "print(f\"The value of the cut is: {cut_value}\")" - ] - }, - { - "cell_type": "markdown", - "id": "a40d2b0d-a941-4f9d-8827-331b8976e2f6", - "metadata": {}, - "source": [ - "Finally, let's draw a graph based on the CVaR result.\n", - "We split the graph nodes into two sets based on the CVaR result.\n", - "The nodes in the first set are colored in gray, and the nodes in the second set are colored in purple.\n", - "The edges between the two sets are the edges that are cut by the partitioning." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "d4b8a149-0693-4724-94bc-0df9ec934707", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "ba53fd1d-0d68-43e3-9102-4c6e64f04de7", + "metadata": {}, + "source": [ + "#### Remap the graph using a SAT mapper\n", + "\n", + "Even when a circuit consists of commuting gates (this is the case for the QAOA circuit, but also for Trotterized simulations of Ising Hamiltonians), finding a good initial mapping is a challenging task. The SAT-based approach presented in [\\[2\\]](#references) enables the discovery of effective initial mappings for circuits with commuting gates, resulting in a significant reduction in the number of required SWAP layers. This approach has been demonstrated to scale to up to *500 qubits*, as illustrated in the paper.\n", + "\n", + "The following code demonstrates how to use the `SATMapper` from Matsuo et al. to remap the graph. This process allows the problem to be mapped to a more optimal initial state for a specified SWAP strategy, resulting in a significant reduction in the number of SWAP layers required to execute the circuit.\n", + "\n", + "In `SATMapper`, the problem of finding a good initial mapping is formulated as a SAT problem. A SAT solver is used to find such an initial mapping for the QAOA circuit. `python-sat` (`pysat` for short) is a Python library for a SAT solver, and we will use it to solve the SAT problem in this example.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "df9d861a-64e8-49ee-aed1-c41f437fa743", + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"A class to solve the SWAP gate insertion initial mapping problem\n", + "using the SAT approach from https://arxiv.org/abs/2212.05666.\n", + "\"\"\"\n", + "\n", + "\n", + "@dataclass\n", + "class SATResult:\n", + " \"\"\"A data class to hold the result of a SAT solver.\"\"\"\n", + "\n", + " satisfiable: bool # Satisfiable is True if the SAT model could be solved\n", + " # in a given time.\n", + " solution: dict # The solution to the SAT problem if it is satisfiable.\n", + " mapping: list # The mapping of nodes in the pattern graph to nodes in the\n", + " # target graph.\n", + " elapsed_time: float # The time it took to solve the SAT model.\n", + "\n", + "\n", + "class SATMapper:\n", + " r\"\"\"A class to introduce a SAT-approach to solve\n", + " the initial mapping problem in SWAP gate insertion for commuting gates.\n", + "\n", + " When this pass is run on a DAG it will look for the first instance of\n", + " :class:`.Commuting2qBlock` and use the program graph :math:`P` of this block\n", + " of gates to find a layout for a given swap strategy. This layout is found\n", + " with a binary search over the layers :math:`l` of the swap strategy. At each\n", + " considered layer a subgraph isomorphism problem formulated as a SAT is solved\n", + " by a SAT solver. Each instance is whether it is possible to embed the program\n", + " graph :math:`P` into the effective connectivity graph :math:`C_l` that is\n", + " achieved by applying :math:`l` layers of the swap strategy to the coupling map\n", + " :math:`C_0` of the backend. Since solving SAT problems can be hard, a\n", + " ``time_out`` fixes the maximum time allotted to the SAT solver for each\n", + " instance. If this time is exceeded the considered problem is deemed\n", + " unsatisfiable and the binary search proceeds to the next number of swap\n", + " layers :math:``l``.\n", + " \"\"\"\n", + "\n", + " def __init__(self, timeout: int = 60):\n", + " \"\"\"Initialize the SATMapping.\n", + "\n", + " Args:\n", + " timeout: The allowed time in seconds for each iteration of the SAT\n", + " solver. This variable defaults to 60 seconds.\n", + " \"\"\"\n", + " self.timeout = timeout\n", + "\n", + " def find_initial_mappings(\n", + " self,\n", + " program_graph: rx.Graph,\n", + " swap_strategy: SwapStrategy,\n", + " min_layers: int | None = None,\n", + " max_layers: int | None = None,\n", + " ) -> dict[int, SATResult]:\n", + " r\"\"\"Find an initial mapping for a given swap strategy. Perform a\n", + " binary search over the number of swap layers, and for each number\n", + " of swap layers solve a subgraph isomorphism problem formulated as\n", + " a SAT problem.\n", + "\n", + " Args:\n", + " program_graph (rx.Graph): The program graph with commuting gates, where\n", + " each edge represents a two-qubit gate.\n", + " swap_strategy (SwapStrategy): The swap strategy to use to find the\n", + " initial mapping.\n", + " min_layers (int): The minimum number of swap layers to consider.\n", + " Defaults to the maximum degree of the\n", + " program graph - 2.\n", + " max_layers (int): The maximum number of swap layers to consider.\n", + " Defaults to the number of qubits in the\n", + " swap strategy - 2.\n", + "\n", + " Returns:\n", + " dict[int, SATResult]: A dictionary containing the results of the SAT\n", + " solver for each number of swap layers.\n", + " \"\"\"\n", + " num_nodes_g1 = len(program_graph.nodes())\n", + " num_nodes_g2 = swap_strategy.distance_matrix.shape[0]\n", + " if num_nodes_g1 > num_nodes_g2:\n", + " return SATResult(False, [], [], 0)\n", + " if min_layers is None:\n", + " # use the maximum degree of the program graph - 2\n", + " # as the lower bound.\n", + " min_layers = max((d for _, d in program_graph.degree)) - 2\n", + " if max_layers is None:\n", + " max_layers = num_nodes_g2 - 1\n", + "\n", + " variable_pool = IDPool(start_from=1)\n", + " variables = np.array(\n", + " [\n", + " [variable_pool.id(f\"v_{i}_{j}\") for j in range(num_nodes_g2)]\n", + " for i in range(num_nodes_g1)\n", + " ],\n", + " dtype=int,\n", + " )\n", + " vid2mapping = {v: idx for idx, v in np.ndenumerate(variables)}\n", + " binary_search_results = {}\n", + "\n", + " def interrupt(solver):\n", + " # This function is called to interrupt the solver when the\n", + " # timeout is reached.\n", + " solver.interrupt()\n", + "\n", + " # Make a cnf (conjunctive normal form) for the one-to-one\n", + " # mapping constraint\n", + " cnf1 = []\n", + " for i in range(num_nodes_g1):\n", + " clause = variables[i, :].tolist()\n", + " cnf1.append(clause)\n", + " for k, m in combinations(clause, 2):\n", + " cnf1.append([-1 * k, -1 * m])\n", + " for j in range(num_nodes_g2):\n", + " clause = variables[:, j].tolist()\n", + " for k, m in combinations(clause, 2):\n", + " cnf1.append([-1 * k, -1 * m])\n", + "\n", + " # Perform a binary search over the number of swap layers to find the\n", + " # minimum number of swap layers that satisfies the subgraph isomorphism\n", + " # problem.\n", + " while min_layers < max_layers:\n", + " num_layers = (min_layers + max_layers) // 2\n", + "\n", + " # Create the connectivity matrix. Note that if the swap strategy\n", + " # cannot reach full connectivity then its distance matrix will have\n", + " # entries with -1. These entries must be treated as False.\n", + " d_matrix = swap_strategy.distance_matrix\n", + " connectivity_matrix = (\n", + " (-1 < d_matrix) & (d_matrix <= num_layers)\n", + " ).astype(int)\n", + " # Make a cnf for the adjacency constraint\n", + " cnf2 = []\n", + " for e_0, e_1 in list(program_graph.edge_list()):\n", + " clause_matrix = np.multiply(\n", + " connectivity_matrix, variables[e_1, :]\n", + " )\n", + " clause = np.concatenate(\n", + " (\n", + " [[-variables[e_0, i]] for i in range(num_nodes_g2)],\n", + " clause_matrix,\n", + " ),\n", + " axis=1,\n", + " )\n", + " # Remove 0s from each clause\n", + " cnf2.extend([c[c != 0].tolist() for c in clause])\n", + "\n", + " cnf = CNF(from_clauses=cnf1 + cnf2)\n", + "\n", + " with Solver(bootstrap_with=cnf, use_timer=True) as solver:\n", + " # Solve the SAT problem with a timeout.\n", + " # Timer is used to interrupt the solver when the\n", + " # timeout is reached.\n", + " timer = Timer(self.timeout, interrupt, [solver])\n", + " timer.start()\n", + " status = solver.solve_limited(expect_interrupt=True)\n", + " timer.cancel()\n", + " # Get the solution and the elapsed time.\n", + " sol = solver.get_model()\n", + " e_time = solver.time()\n", + "\n", + " print(\n", + " f\"Layers: {num_layers}, Status: {status}, Time: {e_time}\"\n", + " )\n", + " if status:\n", + " # If the SAT problem is satisfiable, convert the solution\n", + " # to a mapping.\n", + " mapping = [vid2mapping[idx] for idx in sol if idx > 0]\n", + " binary_search_results[num_layers] = SATResult(\n", + " status, sol, mapping, e_time\n", + " )\n", + " max_layers = num_layers\n", + " else:\n", + " # If the SAT problem is unsatisfiable, return the last\n", + " # satisfiable solution.\n", + " binary_search_results[num_layers] = SATResult(\n", + " status, sol, [], e_time\n", + " )\n", + " min_layers = num_layers + 1\n", + "\n", + " return binary_search_results\n", + "\n", + " def remap_graph_with_sat(\n", + " self, graph: rx.Graph, swap_strategy, max_layers\n", + " ):\n", + " \"\"\"Applies the SAT mapping.\n", + "\n", + " Args:\n", + " graph (nx.Graph): The graph to remap.\n", + " swap_strategy (SwapStrategy): The swap strategy to use\n", + " to find the initial mapping.\n", + "\n", + " Returns:\n", + " tuple: A tuple containing the remapped graph, the edge map, and the\n", + " number of layers of the swap strategy that was used to find the\n", + " initial mapping. If no solution is found then the tuple contains\n", + " None for each element. Note the returned edge map `{k: v}` means that\n", + " node `k` in the original graph gets mapped to node `v` in the\n", + " Pauli strings.\n", + " \"\"\"\n", + " num_nodes = len(graph.nodes())\n", + " results = self.find_initial_mappings(\n", + " graph, swap_strategy, 0, max_layers\n", + " )\n", + " solutions = [k for k, v in results.items() if v.satisfiable]\n", + "\n", + " if len(solutions):\n", + " min_k = min(solutions)\n", + " edge_map = dict(results[min_k].mapping)\n", + " # Create the remapped graph\n", + " remapped_graph = rx.PyGraph()\n", + " remapped_graph.add_nodes_from(range(num_nodes))\n", + " mapping = dict(results[min_k].mapping)\n", + " for i, graph_edge in enumerate(list(graph.edge_list())):\n", + " remapped_edge = tuple(mapping[node] for node in graph_edge)\n", + " remapped_graph.add_edge(*remapped_edge, graph.edges()[i])\n", + " return remapped_graph, edge_map, min_k\n", + " else:\n", + " return None, None, None" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "e689e09e-6ca7-4154-8602-d1d954ebe80b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Layers: 0, Status: True, Time: 0.02229800000000015\n", + "Map from old to new nodes: {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20, 21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26, 27: 27, 28: 28, 29: 29, 30: 30, 31: 31, 32: 32, 33: 33, 34: 34, 35: 35, 36: 36, 37: 37, 38: 38, 39: 39, 40: 40, 41: 41, 42: 42, 43: 43, 44: 44, 45: 45, 46: 46, 47: 47, 48: 48, 49: 49, 50: 50, 51: 51, 52: 52, 53: 53, 54: 54, 55: 55, 56: 56, 57: 57, 58: 58, 59: 59, 60: 60, 61: 61, 62: 62, 63: 63, 64: 64, 65: 65, 66: 66, 67: 67, 68: 68, 69: 69, 70: 70, 71: 71, 72: 72, 73: 73, 74: 74, 75: 75, 76: 76, 77: 77, 78: 78, 79: 79, 80: 80, 81: 81, 82: 82, 83: 83, 84: 84, 85: 85, 86: 86, 87: 87, 88: 88, 89: 89, 90: 90, 91: 91, 92: 92, 93: 93, 94: 94, 95: 95, 96: 96, 97: 97, 98: 98, 99: 99}\n", + "Min SWAP layers: 0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sm = SATMapper(timeout=10)\n", + "remapped_graph, edge_map, min_swap_layers = sm.remap_graph_with_sat(\n", + " graph=graph_100, swap_strategy=swap_strategy, max_layers=1\n", + ")\n", + "print(\"Map from old to new nodes: \", edge_map)\n", + "print(\"Min SWAP layers:\", min_swap_layers)\n", + "draw_graph(remapped_graph, node_size=200, with_labels=True, width=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ecf6e8c3-65c2-4430-8dd3-d67b8842045d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIZIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIZIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIZIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIZIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIZIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIZIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIZIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIII', 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1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j])\n" + ] + } + ], + "source": [ + "remapped_max_cut_paulis = build_max_cut_paulis(remapped_graph)\n", + "# define a qiskit SparsePauliOp from the list of paulis\n", + "remapped_cost_operator = SparsePauliOp.from_list(remapped_max_cut_paulis)\n", + "print(remapped_cost_operator)" + ] + }, + { + "cell_type": "markdown", + "id": "5ae531be-80eb-4acd-b84a-7d466fd872e7", + "metadata": {}, + "source": [ + "#### Build a QAOA circuit with the SWAP strategy and the SAT mapping\n", + "\n", + "We only want to apply the SWAP strategies to the cost operator layer, so we start by creating the isolated block that we will later transform and append to the final QAOA circuit.\n", + "\n", + "For this, we can use the [`QAOAAnsatz`](/docs/api/qiskit/qiskit.circuit.library.QAOAAnsatz) class from Qiskit. We input an empty circuit to the `initial_state` and `mixer_operator` fields to make sure we are building an isolated cost operator layer.\n", + "We also define the edge\\_coloring map so that RZZ gates are positioned next to SWAP gates. This strategic placement allows us to exploit CX cancellations, optimizing the circuit for better performance.\n", + "This process is executed within the `create_qaoa_swap_circuit` function.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "57d5eb53-9cda-4c38-a00b-26ed4b533bcd", + "metadata": {}, + "outputs": [], + "source": [ + "def make_meas_map(circuit: QuantumCircuit) -> dict:\n", + " \"\"\"Return a mapping from qubit index (the key) to classical bit (the value).\n", + "\n", + " This allows us to account for the swapping order introduced by the SWAP strategy.\n", + " \"\"\"\n", + " creg = circuit.cregs[0]\n", + " qreg = circuit.qregs[0]\n", + "\n", + " meas_map = {}\n", + " for inst in circuit.data:\n", + " if inst.operation.name == \"measure\":\n", + " meas_map[qreg.index(inst.qubits[0])] = creg.index(inst.clbits[0])\n", + "\n", + " return meas_map\n", + "\n", + "\n", + "def apply_swap_strategy(\n", + " circuit: QuantumCircuit,\n", + " swap_strategy: SwapStrategy,\n", + " edge_coloring: dict[tuple[int, int], int] | None = None,\n", + ") -> QuantumCircuit:\n", + " \"\"\"Transpile with a SWAP strategy.\n", + "\n", + " Returns:\n", + " A quantum circuit transpiled with the given swap strategy.\n", + " \"\"\"\n", + "\n", + " pm_pre = PassManager(\n", + " [\n", + " FindCommutingPauliEvolutions(),\n", + " Commuting2qGateRouter(\n", + " swap_strategy,\n", + " edge_coloring,\n", + " ),\n", + " ]\n", + " )\n", + " return pm_pre.run(circuit)\n", + "\n", + "\n", + "def apply_qaoa_layers(\n", + " cost_layer: QuantumCircuit,\n", + " meas_map: dict,\n", + " num_layers: int,\n", + " gamma: list[float] | ParameterVector = None,\n", + " beta: list[float] | ParameterVector = None,\n", + " initial_state: QuantumCircuit = None,\n", + " mixer: QuantumCircuit = None,\n", + "):\n", + " \"\"\"Applies QAOA layers to construct circuit.\n", + "\n", + " First, the initial state is applied. If `initial_state` is None, we begin in the\n", + " initial superposition state. Next, we alternate between layers of the cost operator\n", + " and the mixer. The cost operator is alternatively applied in order and in reverse\n", + " instruction order. This allows us to apply the swap strategy on odd `p` layers\n", + " and undo the swap strategy on even `p` layers.\n", + " \"\"\"\n", + "\n", + " num_qubits = cost_layer.num_qubits\n", + " new_circuit = QuantumCircuit(num_qubits, num_qubits)\n", + "\n", + " if initial_state is not None:\n", + " new_circuit.append(initial_state, range(num_qubits))\n", + " else:\n", + " # all h state by default\n", + " new_circuit.h(range(num_qubits))\n", + "\n", + " if gamma is None or beta is None:\n", + " gamma = ParameterVector(\"γ'\", num_layers)\n", + " if mixer is None or mixer.num_parameters == 0:\n", + " beta = ParameterVector(\"β'\", num_layers)\n", + " else:\n", + " beta = ParameterVector(\"β'\", num_layers * mixer.num_parameters)\n", + "\n", + " if mixer is not None:\n", + " mixer_layer = mixer\n", + " else:\n", + " mixer_layer = QuantumCircuit(num_qubits)\n", + " mixer_layer.rx(-2 * beta[0], range(num_qubits))\n", + "\n", + " for layer in range(num_layers):\n", + " bind_dict = {cost_layer.parameters[0]: gamma[layer]}\n", + " cost_layer_ = cost_layer.assign_parameters(bind_dict)\n", + " bind_dict = {\n", + " mixer_layer.parameters[i]: beta[layer + i]\n", + " for i in range(mixer_layer.num_parameters)\n", + " }\n", + " layer_mixer = mixer_layer.assign_parameters(bind_dict)\n", + "\n", + " if layer % 2 == 0:\n", + " new_circuit.append(cost_layer_, range(num_qubits))\n", + " else:\n", + " new_circuit.append(cost_layer_.reverse_ops(), range(num_qubits))\n", + "\n", + " new_circuit.append(layer_mixer, range(num_qubits))\n", + "\n", + " for qidx, cidx in meas_map.items():\n", + " new_circuit.measure(qidx, cidx)\n", + "\n", + " return new_circuit\n", + "\n", + "\n", + "def create_qaoa_swap_circuit(\n", + " cost_operator: SparsePauliOp,\n", + " swap_strategy: SwapStrategy,\n", + " edge_coloring: dict = None,\n", + " theta: list[float] = None,\n", + " qaoa_layers: int = 1,\n", + " initial_state: QuantumCircuit = None,\n", + " mixer: QuantumCircuit = None,\n", + "):\n", + " \"\"\"Create the circuit for QAOA.\n", + "\n", + " Notes: This circuit construction for QAOA works for quadratic terms in `Z` and will be\n", + " extended to first-order terms in `Z`. Higher-orders are not supported.\n", + "\n", + " Args:\n", + " cost_operator: the cost operator.\n", + " swap_strategy: selected swap strategy\n", + " edge_coloring: A coloring of edges that should correspond to the coupling\n", + " map of the hardware. It defines the order in which we apply the Rzz\n", + " gates. This allows us to choose an ordering such that `Rzz` gates will\n", + " immediately precede SWAP gates to leverage CNOT cancellation.\n", + " theta: The QAOA angles.\n", + " qaoa_layers: The number of layers of the cost operator and the mixer operator.\n", + " initial_state: The initial state on which we apply layers of cost operator\n", + " and mixer.\n", + " mixer: The QAOA mixer. It will be applied as is onto the QAOA circuit. Therefore,\n", + " its output must have the same ordering of qubits as its input.\n", + " \"\"\"\n", + "\n", + " num_qubits = cost_operator.num_qubits\n", + "\n", + " if theta is not None:\n", + " gamma = theta[: len(theta) // 2]\n", + " beta = theta[len(theta) // 2 :]\n", + " qaoa_layers = len(theta) // 2\n", + " else:\n", + " gamma = beta = None\n", + "\n", + " # First, create the ansatz of 1 layer of QAOA without mixer\n", + " cost_layer = QAOAAnsatz(\n", + " cost_operator,\n", + " reps=1,\n", + " initial_state=QuantumCircuit(num_qubits),\n", + " mixer_operator=QuantumCircuit(num_qubits),\n", + " ).decompose()\n", + "\n", + " # This will allow us to recover the permutation of the measurements that the swaps introduce.\n", + " cost_layer.measure_all()\n", + "\n", + " # Now, apply the swap strategy for commuting gates\n", + " cost_layer = apply_swap_strategy(cost_layer, swap_strategy, edge_coloring)\n", + "\n", + " # Compute the measurement map (qubit to classical bit).\n", + " # We will apply this for odd layers where the swaps were inserted.\n", + " if qaoa_layers % 2 == 1:\n", + " meas_map = make_meas_map(cost_layer)\n", + " else:\n", + " meas_map = {idx: idx for idx in range(num_qubits)}\n", + "\n", + " cost_layer.remove_final_measurements()\n", + "\n", + " # Finally, introduce the mixer circuit and add measurements following measurement map\n", + " circuit = apply_qaoa_layers(\n", + " cost_layer, meas_map, qaoa_layers, gamma, beta, initial_state, mixer\n", + " )\n", + "\n", + " return circuit" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "7793ef92-ce59-4fd7-b43f-48d4e3427e3a", + "metadata": {}, + "outputs": [], + "source": [ + "# We can define the edge_coloring map so that RZZ gates are positioned right before SWAP gates to exploit CX cancellations\n", + "# We use greedy edge coloring from rustworkx to color the edges of the graph. This coloring is used to order the RZZ gates in the circuit.\n", + "\n", + "edge_coloring_idx = rx.graph_greedy_edge_color(graph_100)\n", + "edge_coloring = {\n", + " edge: edge_coloring_idx[idx]\n", + " for idx, edge in enumerate(list(graph_100.edge_list()))\n", + "}\n", + "edge_coloring = {tuple(sorted(k)): v for k, v in edge_coloring.items()}" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "82ae28b3-85eb-4487-8100-1e622e93cccf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qaoa_circ = create_qaoa_swap_circuit(\n", + " remapped_cost_operator,\n", + " swap_strategy,\n", + " edge_coloring=edge_coloring,\n", + " qaoa_layers=1,\n", + ")\n", + "qaoa_circ.draw(output=\"mpl\", fold=False)" + ] + }, + { + "cell_type": "markdown", + "id": "e2afd1a7-0980-433b-a3a8-303d7e7718b1", + "metadata": {}, + "source": [ + "## Step 3: Execute using Qiskit primitives\n", + "\n", + "### Define a CVaR cost function\n", + "\n", + "This example shows how to use the Conditional Value at Risk (CVaR) cost function introduced in [\\[3\\]](#references) within the variational quantum optimization algorithms.\n", + "\n", + "The CVaR of a random variable $X$ for a confidence level $α ∈ (0, 1]$ is defined as\n", + "$CVaR_{\\alpha}(X) = \\mathbb{E} \\lbrack X | X \\leq F_X^{-1}(\\alpha) \\rbrack$\n", + "where $F_X^{-1}(p)$ is the inverse cumulative distribution function of $X$. In other words, CVaR is the expected value of the lower $\\alpha$-tail of the distribution of $X$.\n", + "\n" + ] + }, { - "data": { - "text/plain": [ - "\"Output" + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Write (causes no increase in depth, just increases number of qubits to number of qubits on device)\n", + "\n", + "pass_manager = generate_preset_pass_manager(\n", + " backend=backend,\n", + " optimization_level=3,\n", + ")\n", + " \n", + "transpiled_qaoa_circ = pass_manager.run(qaoa_circ)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "e6794cf3-7fbe-46a5-bdc0-5faad1235365", + "metadata": {}, + "outputs": [], + "source": [ + "# Utility functions for the evaluation of the expectation value of a measured state\n", + "# In this code, for optimization, the measured state is converted into a bit string,\n", + "# and the sign of the value is determined by taking the exclusive OR of the bits\n", + "# corresponding to Pauli Z.\n", + "\n", + "_PARITY = np.array(\n", + " [-1 if bin(i).count(\"1\") % 2 else 1 for i in range(256)],\n", + " dtype=np.complex128,\n", + ")\n", + "\n", + "\n", + "def evaluate_sparse_pauli(state: int, observable: SparsePauliOp) -> complex:\n", + " \"\"\"Utility for the evaluation of the expectation value of a measured state.\n", + "\n", + " Args:\n", + " state (int): The measured state.\n", + " observable (SparsePauliOp): The observable to evaluate the expectation value for.\n", + "\n", + " Returns:\n", + " complex: The expectation value of the measured state.\n", + " \"\"\"\n", + " packed_uint8 = np.packbits(observable.paulis.z, axis=1, bitorder=\"little\") # convert observable to array with 8 bit integer\n", + " state_bytes = np.frombuffer(\n", + " state.to_bytes(packed_uint8.shape[1], \"little\"), dtype=np.uint8 # convert bistring to array with 8 bit integer\n", + " )\n", + " reduced = np.bitwise_xor.reduce(packed_uint8 & state_bytes, axis=1) # take bitwise xor of the result of 'and' conditional on the above two, return 0 or 1\n", + " return np.sum(observable.coeffs * _PARITY[reduced])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c5e5f7a4-01f6-4a02-9114-3bb0e24be1a2", + "metadata": {}, + "outputs": [], + "source": [ + "def qaoa_sampler_cost_fun(\n", + " params, ansatz, hamiltonian, sampler, aggregation=None\n", + "):\n", + " \"\"\"Standard sampler-based QAOA cost function to be plugged into optimizer routines.\n", + "\n", + " Args:\n", + " params (np.ndarray): Parameters for the ansatz.\n", + " ansatz (QuantumCircuit): Ansatz circuit.\n", + " hamiltonian (SparsePauliOp): Hamiltonian to be minimized.\n", + " sampler (QAOASampler): Sampler to be used.\n", + " aggregation (Callable | float | None): Aggregation function to be applied to\n", + " the sampled results. If None, the sum of the expectation values is returned.\n", + " If float, the CVaR with the given alpha is used.\n", + " \"\"\"\n", + " # Run the circuit\n", + " job = sampler.run([(ansatz, params)])\n", + " sampler_result = job.result()\n", + " sampled_int_counts = sampler_result[0].data.c.get_int_counts() # bitstrings are stored as integers\n", + " shots = sum(sampled_int_counts.values())\n", + " int_count_distribution = {\n", + " key: val / shots for key, val in sampled_int_counts.items()\n", + " }\n", + "\n", + " # a dictionary containing: {state: (measurement probability, value)}\n", + " evaluated = {\n", + " state: (\n", + " probability,\n", + " np.real(evaluate_sparse_pauli(state, hamiltonian)),\n", + " )\n", + " for state, probability in int_count_distribution.items()\n", + " }\n", + "\n", + " # If aggregation is None, return the sum of the expectation values.\n", + " # If aggregation is a float, return the CVaR with the given alpha.\n", + " # Otherwise, use the aggregation function.\n", + " if aggregation is None:\n", + " result = sum(\n", + " probability * value for probability, value in evaluated.values()\n", + " )\n", + " elif isinstance(aggregation, float):\n", + " cvar_aggregation = _get_cvar_aggregation(aggregation)\n", + " result = cvar_aggregation(evaluated.values())\n", + " else:\n", + " result = aggregation(evaluated.values())\n", + "\n", + " global iter_counts, result_dict\n", + " iter_counts += 1\n", + " temp_dict = {}\n", + " temp_dict[\"params\"] = params.tolist()\n", + " temp_dict[\"cvar_fval\"] = result\n", + " temp_dict[\"fval\"] = sum(\n", + " probability * value for probability, value in evaluated.values()\n", + " )\n", + " temp_dict[\"distribution\"] = sampled_int_counts\n", + " temp_dict[\"evaluated\"] = evaluated\n", + " result_dict[iter_counts] = temp_dict\n", + " print(f\"Iteration {iter_counts}: {result}\")\n", + "\n", + " return result\n", + "\n", + "\n", + "def _get_cvar_aggregation(alpha: float | None) -> Callable:\n", + " \"\"\"Return the CVaR aggregation function with the given alpha.\n", + "\n", + " Args:\n", + " alpha (float | None): Alpha value for the CVaR aggregation. If None, 1 is used\n", + " by default.\n", + " Raises:\n", + " ValueError: If alpha is not in [0, 1].\n", + " \"\"\"\n", + " if alpha is None:\n", + " alpha = 1\n", + " elif not 0 <= alpha <= 1:\n", + " raise ValueError(f\"alpha must be in [0, 1], but {alpha} was given.\")\n", + "\n", + "\n", + " def cvar_aggregation(\n", + " objective_dict: Iterable[tuple[float, float]],\n", + " ) -> float:\n", + " \"\"\"Return the CVaR of the given measurements.\n", + " Args:\n", + " objective_dict (Iterable[tuple[float, float]]): An iterable of tuples containing\n", + " the measured bit string and the objective value based on the bit string.\n", + "\n", + " \"\"\"\n", + " sorted_measurements = sorted(objective_dict, key=lambda x: x[1])\n", + " # accumulate the probabilities until alpha is reached\n", + " accumulated_percent = 0.0\n", + " cvar = 0.0\n", + " for probability, value in sorted_measurements:\n", + " cvar += value * min(probability, alpha - accumulated_percent)\n", + " accumulated_percent += probability\n", + " if accumulated_percent >= alpha:\n", + " break\n", + " return cvar / alpha\n", + "\n", + " return cvar_aggregation" + ] + }, + { + "cell_type": "markdown", + "id": "63fa2ab4-5354-4022-ab46-e9bbf73870de", + "metadata": {}, + "source": [ + "The CVaR can be used as an error mitigation technique as previously discussed [\\[4\\]](#references). In this example, we determine $\\alpha$ and the number of shots according to the circuit's error rate.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "032bf312-4bf4-40f4-81f0-2ae8a719b98b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "layer fidelity 0.6544189744623636\n", + "\n", + "The corresponding CVaR aggregation value is: 0.38642994025160377\n", + "To mitigate the twirled noise, increase shots by a factor of 2.5877912031063173\n" + ] + } + ], + "source": [ + "num_2q_ops = transpiled_qaoa_circ.count_ops()[\"cz\"] # the two qubit gates on our backend are cz's.\n", + "\n", + "for el in backend.properties().general:\n", + " if el.name[:2] == \"lf\" and el.name[3:] == str(n): # pick out lf_100, lf of the best 100q chain\n", + " lf = el.value # layer fidelity\n", + " print(\"layer fidelity\", lf)\n", + " eplg = 1 - lf ** (1 / (n - 1)) # error per layered gate (EPLG)\n", + " fid_cz = 1 - eplg\n", + " gamma_cz = 1 / fid_cz**2\n", + " gamma_circ = gamma_cz ** num_2q_ops\n", + "\n", + "cvar_aggregation = 1 / np.sqrt(gamma_circ)\n", + "print(\"\")\n", + "print(\"The corresponding CVaR aggregation value is: \", cvar_aggregation)\n", + "print(\n", + " \"To mitigate the twirled noise, increase shots by a factor of\", np.sqrt(gamma_circ)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1: -10.010143313246703\n", + "Iteration 2: -10.778378196890142\n", + "Iteration 3: -33.11648157016098\n", + "Iteration 4: 23.37766951385026\n", + "Iteration 5: -8.456279717029192\n", + "Iteration 6: -20.250216714385534\n", + "Iteration 7: -45.87610233094513\n", + "Iteration 8: -20.4522784936772\n", + "Iteration 9: -49.63202770645467\n", + "Iteration 10: -49.10586683562599\n", + "Iteration 11: -49.15188090417742\n", + "Iteration 12: -45.84209193245048\n", + "Iteration 13: -49.634028318131016\n", + "Iteration 14: -48.542306019104984\n", + "Iteration 15: -49.8580968258604\n", + "Iteration 16: -49.7000485034442\n", + " message: Return from COBYLA because the trust region radius reaches its lower bound.\n", + " success: True\n", + " status: 0\n", + " fun: -49.8580968258604\n", + " x: [ 4.262e+00 2.820e+00]\n", + " nfev: 16\n", + " maxcv: 0.0\n" + ] + } + ], + "source": [ + "iter_counts = 0\n", + "result_dict={}\n", + "init_params=[np.pi, np.pi/2]\n", + "\n", + "with Session(backend=backend) as session:\n", + " sampler=Sampler(mode=session)\n", + " sampler.options.default_shots = int(1000/cvar_aggregation)\n", + " sampler.options.dynamical_decoupling.enable=True\n", + " sampler.options.dynamical_decoupling.sequence_type='XY4'\n", + " sampler.options.twirling.enable_gates=True\n", + " sampler.options.twirling.enable_measure=True\n", + "\n", + " result = minimize(qaoa_sampler_cost_fun,\n", + " init_params,\n", + " args= (transpiled_qaoa_circ,\n", + " remapped_cost_operator,\n", + " sampler,\n", + " cvar_aggregation\n", + " ),\n", + " method='COBYLA',\n", + " tol=1e-2)\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "id": "1d190fa4-3bbe-412a-b296-6dddd3ad2b12", + "metadata": {}, + "source": [ + "## Step 4: Post-process and return result in desired classical format\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "761821cb-9a0c-4efb-806b-75513302d34a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(\n", + " [result_dict[i][\"cvar_fval\"] for i in range(1, iter_counts + 1)],\n", + " label=\"CVaR\",\n", + ")\n", + "plt.plot(\n", + " [result_dict[i][\"fval\"] for i in range(1, iter_counts + 1)],\n", + " label=\"Standard\",\n", + ")\n", + "plt.legend()\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"Cost\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "38aadfcb-aec9-4dbb-a9d3-319239eae196", + "metadata": {}, + "source": [ + "The following retrieves the best solution from the sampled bitstrings\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "7e8af29e-c99b-41f2-b6dd-2be471e1af21", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bitstring (int): 1056839274160662489278455764297, probability: 0.00038654812524159255, objective value: -71.0\n" + ] + } + ], + "source": [ + "# sort the result_dict[iter_counts]['evaluated] by the CVaR value\n", + "sorted_result_dict = [\n", + " (k, v)\n", + " for k, v in sorted(\n", + " result_dict[iter_counts][\"evaluated\"].items(),\n", + " key=lambda item: item[1][1],\n", + " )\n", + "]\n", + "print(\n", + " f\"bitstring (int): {sorted_result_dict[0][0]}, probability: {sorted_result_dict[0][1][0]}, objective value: {sorted_result_dict[0][1][1]}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Consider the Hamiltonian $H_C$ for the **Max-Cut** problem. Let each vertex of the graph be associated with a qubit in state $|0\\rangle$ or $|1\\rangle$, where the value denotes the set the vertex is in. The goal of the problem is to maximize the number of edges $(v_1, v_2)$ for which $v_1 = |0\\rangle$ and $v_2 = |1\\rangle$, or vice-versa. If we associate the $Z$ operator with each qubit, where\n", + "\n", + "$$\n", + " Z|0\\rangle = |0\\rangle \\qquad Z|1\\rangle = -|1\\rangle\n", + "$$\n", + "\n", + "then an edge $(v_1, v_2)$ belongs to the cut if the eigenvalue of $(Z_1|v_1\\rangle) \\cdot (Z_2|v_2\\rangle) = -1$; in other words, the qubits associated with $v_1$ and $v_2$ are different. Similarly, $(v_1, v_2)$ does not belong to the cut if the eigenvalue of $(Z_1|v_1\\rangle) \\cdot (Z_2|v_2\\rangle) = 1$" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "5ea9e6aa-4297-4687-b484-1695d415bad5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result bitstring (binary) : [1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1]\n", + "The value of the cut is: 91\n" + ] + } + ], + "source": [ + "from typing import Sequence\n", + "\n", + "def to_bitstring(integer, num_bits):\n", + " result = np.binary_repr(integer, width=num_bits)\n", + " return [int(digit) for digit in result]\n", + "\n", + "\n", + "def evaluate_sample(x: Sequence[int], graph: rx.PyGraph) -> float:\n", + " assert len(x) == len(\n", + " list(graph.nodes())\n", + " ), \"The length of x must coincide with the number of nodes in the graph.\"\n", + " return sum(\n", + " x[u] * (1 - x[v]) + x[v] * (1 - x[u]) # x[u] = x[v] if same cut, x[u] \\neq x[v] if different cuts\n", + " for u, v in list(graph.edge_list())\n", + " )\n", + "\n", + "\n", + "bitstring = to_bitstring(\n", + " sorted_result_dict[0][0], len(list(remapped_graph.nodes()))\n", + ")\n", + "bitstring = bitstring[::-1]\n", + "print(f\"Result bitstring (binary) : {bitstring}\")\n", + "\n", + "cut_value = evaluate_sample(bitstring, remapped_graph)\n", + "print(f\"The value of the cut is: {cut_value}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c5879546-35ab-4876-bed9-262b85f130cc", + "metadata": {}, + "source": [ + "Finally, let's draw a graph based on the CVaR result.\n", + "We split the graph nodes into two sets based on the CVaR result.\n", + "The nodes in the first set are colored in gray, and the nodes in the second set are colored in purple.\n", + "The edges between the two sets are the edges that are cut by the partitioning.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "852dfeed-2871-4ca1-9754-15c95293198e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_result(G, x):\n", + " colors = [\"tab:grey\" if i == 0 else \"tab:purple\" for i in x]\n", + " pos, _default_axes = rx.spring_layout(G), plt.axes(frameon=True)\n", + " rx.visualization.mpl_draw(\n", + " G, node_color=colors, node_size=100, alpha=0.8, pos=pos\n", + " )\n", + "\n", + "\n", + "plot_result(graph_100, to_bitstring(sorted_result_dict[0][0], 100))" + ] + }, + { + "cell_type": "markdown", + "id": "82f5c13b-a141-4657-adfd-bb18e88ad9f2", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "\\[1] Weidenfeller, J., Valor, L. C., Gacon, J., Tornow, C., Bello, L., Woerner, S., & Egger, D. J. (2022). Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware. Quantum, 6, 870.\n", + "\n", + "\\[2] Matsuo, A., Yamashita, S., & Egger, D. J. (2023). A SAT approach to the initial mapping problem in SWAP gate insertion for commuting gates. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 106(11), 1424-1431.\n", + "\n", + "\\[3] Barkoutsos, P. K., Nannicini, G., Robert, A., Tavernelli, I., & Woerner, S. (2020). Improving variational quantum optimization using CVaR. Quantum, 4, 256.\n", + "\n", + "\\[4] Barron, S. V., Egger, D. J., Pelofske, E., Bärtschi, A., Eidenbenz, S., Lehmkuehler, M., & Woerner, S. (2023). Provable bounds for noise-free expectation values computed from noisy samples. arXiv preprint arXiv:2312.00733.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c048cd84-d6e8-4b8a-926e-a7f7724a86e2", + "metadata": {}, + "source": [ + "## Tutorial survey\n", + "\n", + "Please take one minute to provide feedback on this tutorial. Your insights will help us improve our content offerings and user experience.\n", + "\n", + "[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_cZwpScxyXVDpIeq)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "d325feb7-b9c5-4e6a-b1ee-93e5f96213cb", + "metadata": {}, + "source": [ + "© IBM Corp. 2025\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "a1b8767d", + "metadata": {}, + "source": [ + "© IBM Corp., 2017-2025" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "def plot_result(G, x):\n", - " colors = [\"tab:grey\" if i == 0 else \"tab:purple\" for i in x]\n", - " pos, _default_axes = rx.spring_layout(G), plt.axes(frameon=True)\n", - " rx.visualization.mpl_draw(\n", - " G, node_color=colors, node_size=100, alpha=0.8, pos=pos\n", - " )\n", - "\n", - "\n", - "plot_result(graph_100, to_bitstring(sorted_result_dict[0][0], 100))" - ] - }, - { - "cell_type": "markdown", - "id": "848ad704-177e-44cc-90d5-4d1d22b3ecfc", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "[1] Weidenfeller, J., Valor, L. C., Gacon, J., Tornow, C., Bello, L., Woerner, S., & Egger, D. J. (2022). Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware. Quantum, 6, 870.\n", - "\n", - "[2] Matsuo, A., Yamashita, S., & Egger, D. J. (2023). A SAT approach to the initial mapping problem in SWAP gate insertion for commuting gates. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 106(11), 1424-1431.\n", - "\n", - "[3] Barkoutsos, P. K., Nannicini, G., Robert, A., Tavernelli, I., & Woerner, S. (2020). Improving variational quantum optimization using CVaR. Quantum, 4, 256.\n", - "\n", - "[4] Barron, S. V., Egger, D. J., Pelofske, E., Bärtschi, A., Eidenbenz, S., Lehmkuehler, M., & Woerner, S. (2023). Provable bounds for noise-free expectation values computed from noisy samples. arXiv preprint arXiv:2312.00733." - ] - }, - { - "cell_type": "markdown", - "id": "3a26644d-eddb-4755-b0c3-b2fd4fbe7ec4", - "metadata": {}, - "source": [ - "## Tutorial survey\n", - "\n", - "Please take this short survey to provide feedback on this tutorial. Your insights will help us improve our content offerings and user experience.\n", - "\n", - "[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_cZwpScxyXVDpIeq)" - ] - } - ], - "metadata": { - "description": "This notebook introduces advanced techniques to improve the performance of the Quantum Approximate Optimization Algorithm (QAOA) with a large number of qubits.", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3" + ], + "metadata": { + "description": "This notebook introduces advanced techniques to improve the performance of the Quantum Approximate Optimization Algorithm (QAOA) with a large number of qubits.", + "kernelspec": { + "display_name": "tut", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + }, + "title": "Advanced techniques for QAOA" }, - "title": "Advanced techniques for QAOA" - }, - "nbformat": 4, - "nbformat_minor": 4 + "nbformat": 4, + "nbformat_minor": 4 } From 7a95783060877217591ccf9b7096af9029969dad Mon Sep 17 00:00:00 2001 From: Ibrahim Shehzad Date: Tue, 25 Nov 2025 20:49:02 -0500 Subject: [PATCH 03/28] run formatting checks on notebook --- .../advanced-techniques-for-qaoa.ipynb | 2688 ++++++++--------- package-lock.json | 1 + ...9d768e2-adeb-4678-a691-05480df67b47-0.avif | Bin 17968 -> 0 bytes ...350d321-ee74-4de8-9599-b4ac6d507ff7-1.avif | Bin 17732 -> 0 bytes ...14fd708-03cd-421f-95c9-b5d6f539ea73-0.avif | Bin 80684 -> 0 bytes ...90d3b82-fef2-4bd3-85e2-12c53404d613-0.avif | Bin 20256 -> 0 bytes ...61821cb-9a0c-4efb-806b-75513302d34a-0.avif | Bin 0 -> 6301 bytes ...2ae28b3-85eb-4487-8100-1e622e93cccf-0.avif | Bin 0 -> 101100 bytes ...398b537-8863-414b-b8cb-e9f92436ddc1-0.avif | Bin 7511 -> 0 bytes ...52dfeed-2871-4ca1-9754-15c95293198e-0.avif | Bin 0 -> 8323 bytes ...b864d32-9483-45ea-831f-60488e330adb-0.avif | Bin 0 -> 21036 bytes ...ac2da80-4deb-495a-a250-c851b9c2fa9d-0.avif | Bin 7734 -> 0 bytes ...4b8a149-0693-4724-94bc-0df9ec934707-0.avif | Bin 8457 -> 0 bytes ...689e09e-6ca7-4154-8602-d1d954ebe80b-1.avif | Bin 0 -> 18607 bytes scripts/config/cspell/dictionaries/qiskit.txt | 9 +- 15 files changed, 1353 insertions(+), 1345 deletions(-) delete mode 100644 public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/39d768e2-adeb-4678-a691-05480df67b47-0.avif delete mode 100644 public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/4350d321-ee74-4de8-9599-b4ac6d507ff7-1.avif delete mode 100644 public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/514fd708-03cd-421f-95c9-b5d6f539ea73-0.avif delete mode 100644 public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/690d3b82-fef2-4bd3-85e2-12c53404d613-0.avif create mode 100644 public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/761821cb-9a0c-4efb-806b-75513302d34a-0.avif create mode 100644 public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/82ae28b3-85eb-4487-8100-1e622e93cccf-0.avif delete mode 100644 public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/8398b537-8863-414b-b8cb-e9f92436ddc1-0.avif create mode 100644 public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/852dfeed-2871-4ca1-9754-15c95293198e-0.avif create mode 100644 public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/8b864d32-9483-45ea-831f-60488e330adb-0.avif delete mode 100644 public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/aac2da80-4deb-495a-a250-c851b9c2fa9d-0.avif delete mode 100644 public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/d4b8a149-0693-4724-94bc-0df9ec934707-0.avif create mode 100644 public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/e689e09e-6ca7-4154-8602-d1d954ebe80b-1.avif diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 9fa6a2d8d54..0e144520cba 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -1,1369 +1,1369 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "1a869c2d-ae51-47d3-8a41-bfcf5e505f59", - "metadata": {}, - "source": [ - "# Advanced techniques for QAOA\n", - "\n", - "*Usage estimate: 25 minutes on IBM Sherbrooke (NOTE: This is an estimate only. Your runtime may vary.)*\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "ea97567d-810f-4cca-8edf-a47d70ea870a", - "metadata": {}, - "source": [ - "## Background\n", - "\n", - "This notebook introduces advanced techniques to improve the performance of the **Quantum Approximate Optimization Algorithm (QAOA)** with a large number of qubits.\n", - "Please see [Solve utility-scale quantum optimization problems](/docs/tutorials/quantum-approximate-optimization-algorithm) for an introduction to QAOA.\n", - "\n", - "The advanced techniques in this notebook include:\n", - "\n", - "* **SWAP strategy with SAT initial mapping**: This is a specifically designed transpiler pass for QAOA that uses a SWAP strategy and a SAT solver together to improve the selection of which physical qubits on the QPU to use. The SWAP strategy exploits the commutativity of the QAOA operators to reorder gates so that layers of SWAP gates can be simultaneously executed, thus reducing the depth of the circuit [\\[1\\]](#references). The SAT solver is used to find an initial mapping that minimizes the number of SWAP operations needed to map the qubits in the circuit to the physical qubits on the device [\\[2\\]](#references) .\n", - "* **CVaR cost function**: Typically the expected value of the cost Hamiltonian is used as the cost function for QAOA, but as was shown in [\\[3\\]](#references) , focusing on the tail of the distribution, rather than the expected value, can improve the performance of QAOA for combinatorial optimization problems. The CVaR accomplishes this. For a given set of shots with corresponding objective values of the considered optimization problem, the Conditional Value at Risk (CVaR) with confidence level $\\alpha \\in [0, 1]$ is defined as the average of the $\\alpha$ best shots [\\[3\\]](#references).\n", - " Thus, $\\alpha = 1$ corresponds to the standard expected value, while $\\alpha=0$ corresponds to the minimum of the given shots, and $\\alpha \\in (0, 1)$ is a tradeoff between focusing on better shots, while still applying some averaging to smooth out the optimization landscape. Additionally, the CVaR can be used as an error mitigation technique to improve the quality of the objective value estimation [\\[4\\]](#references).\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "40fb546e-85e0-450b-a5ea-5d08950d129f", - "metadata": {}, - "source": [ - "## Requirements\n", - "\n", - "Before starting this tutorial, be sure you have the following installed:\n", - "\n", - "* Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", - "* Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\n", - "* Rustworkx graph library (`pip install rustworkx`)\n", - "* Python SAT (`pip install python-sat`)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "50285e5f-1a7b-471c-a223-1ae0af19d9ed", - "metadata": {}, - "source": [ - "## Setup\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d019ea68-61e0-4341-84c7-e612ca10dde7", - "metadata": {}, - "outputs": [], - "source": [ - "from __future__ import annotations\n", - "\n", - "import numpy as np\n", - "import rustworkx as rx\n", - "from dataclasses import dataclass\n", - "from itertools import combinations\n", - "from threading import Timer\n", - "from IPython.display import Image\n", - "from collections.abc import Callable, Iterable\n", - "from pysat.formula import CNF, IDPool\n", - "from pysat.solvers import Solver\n", - "from scipy.optimize import minimize\n", - "from rustworkx.visualization import mpl_draw as draw_graph\n", - "\n", - "from qiskit.quantum_info import SparsePauliOp\n", - "from qiskit.circuit.library import QAOAAnsatz\n", - "from qiskit.circuit import QuantumCircuit, ParameterVector\n", - "from qiskit.transpiler import CouplingMap, PassManager\n", - "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", - "from qiskit.transpiler.passes.routing.commuting_2q_gate_routing import (\n", - " SwapStrategy,\n", - " FindCommutingPauliEvolutions,\n", - " Commuting2qGateRouter,\n", - ")\n", - "\n", - "from qiskit_ibm_runtime import QiskitRuntimeService, Session\n", - "from qiskit_ibm_runtime import SamplerV2 as Sampler" - ] - }, - { - "cell_type": "markdown", - "id": "8b77b0c9-f5a6-476e-86b8-069ba14f9ab3", - "metadata": {}, - "source": [ - "\n", - "\n", - "### Max-Cut Problem\n", - "\n", - "Let's consider solving the **Max-Cut** problem on a graph with 100 nodes using QAOA.\n", - "The Max-Cut problem is a combinatorial optimization problem that is defined on a graph $G = (V, E)$, where $V$ is the set of vertices and $E$ is the set of edges. The goal is to partition the vertices into two sets, $S$ and $V \\setminus S$, such that the number of edges between the two sets is maximized.\n", - "In this example, we use a graph with 100 nodes that is based on a hardware coupling map.\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Map classical inputs to a quantum problem\n", - "\n", - "### Graph → Hamiltonian\n", - "\n", - "First, map the problem onto a quantum circuit that is suited for the QAOA. Details on this process can be found in the [introductory QAOA tutorial.](/docs/tutorials/quantum-approximate-optimization-algorithm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "# Instantiate runtime to access backend\n", - "service = QiskitRuntimeService()\n", - "backend=service.least_busy(min_num_qubits=100, operational=True, simulator=False)\n", - "print(backend)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "backend.coupling_map.is_symmetric" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n=100\n", - "graph_100 = rx.PyGraph()\n", - "graph_100.add_nodes_from((np.arange(0,n,1)))\n", - "w=1.0\n", - "elist=[]\n", - "\n", - "for edge in backend.coupling_map:\n", - " if (edge[0] list[tuple[str, float]]:\n", - " \"\"\"Convert the graph to Pauli list.\n", - "\n", - " This function does the inverse of `build_max_cut_graph`\n", - " \"\"\"\n", - " pauli_list = []\n", - " for edge in list(graph.edge_list()):\n", - " paulis = [\"I\"] * len(graph)\n", - " paulis[edge[0]], paulis[edge[1]] = \"Z\", \"Z\"\n", - "\n", - " weight = graph.get_edge_data(edge[0], edge[1])\n", - "\n", - " pauli_list.append((\"\".join(paulis)[::-1], weight))\n", - "\n", - " return pauli_list\n", - "\n", - "\n", - "max_cut_paulis = build_max_cut_paulis(graph_100)\n", - "\n", - "cost_hamiltonian = SparsePauliOp.from_list(max_cut_paulis)\n", - "print(\"Cost Function Hamiltonian:\", cost_hamiltonian)" - ] - }, - { - "cell_type": "markdown", - "id": "4e576068-53e7-4a06-a83b-87e95de141e9", - "metadata": {}, - "source": [ - "## Step 2: Optimize problem for quantum hardware execution\n", - "\n", - "### SWAP strategy with the SAT initial mapping\n", - "\n", - "We will demonstrate how to build and optimize QAOA circuits using the **SWAP strategy with SAT initial mapping**, a specifically designed transpiler pass for QAOA applied to quadratic problems.\n", - "\n", - "In this example, we choose a SWAP insertion strategy for blocks of commuting two-qubit gates, which applies layers of SWAP gates that are simultaneously executable on the coupling map. This strategy is presented in [\\[1\\]](#references) and is passed into `Commuting2qGateRouter` which is exposed as a standardized Qiskit transpiler pass (see [`Commuting2qGateRouter`](/docs/api/qiskit/qiskit.transpiler.passes.Commuting2qGateRouter)). We use a line swap strategy in this example.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "7c54402a-7696-4b0e-826a-6e0ac4c54395", - "metadata": {}, - "outputs": [], - "source": [ - "# Extract longest path with no repeated nodes\n", - "nodes = rx.longest_simple_path(graph_100)\n", - "\n", - "# Collect even edges and odd edges\n", - "even_edges = [\n", - " (nodes[i], nodes[i + 1])\n", - " if nodes[i] < nodes[i + 1]\n", - " else (nodes[i + 1], nodes[i])\n", - " for i in range(0, len(nodes) - 1, 2)\n", - "]\n", - "odd_edges = [\n", - " (nodes[i], nodes[i + 1])\n", - " if nodes[i] < nodes[i + 1]\n", - " else (nodes[i + 1], nodes[i])\n", - " for i in range(1, len(nodes) - 1, 2)\n", - "]\n", - "edge_list = [\n", - " (edge[0], edge[1]) if edge[0] < edge[1] else (edge[1], edge[0])\n", - " for edge in graph_100.edge_list()\n", - "]\n", - "\n", - "swap_strategy = SwapStrategy(CouplingMap(edge_list), (even_edges, odd_edges))" - ] - }, - { - "cell_type": "markdown", - "id": "ba53fd1d-0d68-43e3-9102-4c6e64f04de7", - "metadata": {}, - "source": [ - "#### Remap the graph using a SAT mapper\n", - "\n", - "Even when a circuit consists of commuting gates (this is the case for the QAOA circuit, but also for Trotterized simulations of Ising Hamiltonians), finding a good initial mapping is a challenging task. The SAT-based approach presented in [\\[2\\]](#references) enables the discovery of effective initial mappings for circuits with commuting gates, resulting in a significant reduction in the number of required SWAP layers. This approach has been demonstrated to scale to up to *500 qubits*, as illustrated in the paper.\n", - "\n", - "The following code demonstrates how to use the `SATMapper` from Matsuo et al. to remap the graph. This process allows the problem to be mapped to a more optimal initial state for a specified SWAP strategy, resulting in a significant reduction in the number of SWAP layers required to execute the circuit.\n", - "\n", - "In `SATMapper`, the problem of finding a good initial mapping is formulated as a SAT problem. A SAT solver is used to find such an initial mapping for the QAOA circuit. `python-sat` (`pysat` for short) is a Python library for a SAT solver, and we will use it to solve the SAT problem in this example.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "df9d861a-64e8-49ee-aed1-c41f437fa743", - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"A class to solve the SWAP gate insertion initial mapping problem\n", - "using the SAT approach from https://arxiv.org/abs/2212.05666.\n", - "\"\"\"\n", - "\n", - "\n", - "@dataclass\n", - "class SATResult:\n", - " \"\"\"A data class to hold the result of a SAT solver.\"\"\"\n", - "\n", - " satisfiable: bool # Satisfiable is True if the SAT model could be solved\n", - " # in a given time.\n", - " solution: dict # The solution to the SAT problem if it is satisfiable.\n", - " mapping: list # The mapping of nodes in the pattern graph to nodes in the\n", - " # target graph.\n", - " elapsed_time: float # The time it took to solve the SAT model.\n", - "\n", - "\n", - "class SATMapper:\n", - " r\"\"\"A class to introduce a SAT-approach to solve\n", - " the initial mapping problem in SWAP gate insertion for commuting gates.\n", - "\n", - " When this pass is run on a DAG it will look for the first instance of\n", - " :class:`.Commuting2qBlock` and use the program graph :math:`P` of this block\n", - " of gates to find a layout for a given swap strategy. This layout is found\n", - " with a binary search over the layers :math:`l` of the swap strategy. At each\n", - " considered layer a subgraph isomorphism problem formulated as a SAT is solved\n", - " by a SAT solver. Each instance is whether it is possible to embed the program\n", - " graph :math:`P` into the effective connectivity graph :math:`C_l` that is\n", - " achieved by applying :math:`l` layers of the swap strategy to the coupling map\n", - " :math:`C_0` of the backend. Since solving SAT problems can be hard, a\n", - " ``time_out`` fixes the maximum time allotted to the SAT solver for each\n", - " instance. If this time is exceeded the considered problem is deemed\n", - " unsatisfiable and the binary search proceeds to the next number of swap\n", - " layers :math:``l``.\n", - " \"\"\"\n", - "\n", - " def __init__(self, timeout: int = 60):\n", - " \"\"\"Initialize the SATMapping.\n", - "\n", - " Args:\n", - " timeout: The allowed time in seconds for each iteration of the SAT\n", - " solver. This variable defaults to 60 seconds.\n", - " \"\"\"\n", - " self.timeout = timeout\n", - "\n", - " def find_initial_mappings(\n", - " self,\n", - " program_graph: rx.Graph,\n", - " swap_strategy: SwapStrategy,\n", - " min_layers: int | None = None,\n", - " max_layers: int | None = None,\n", - " ) -> dict[int, SATResult]:\n", - " r\"\"\"Find an initial mapping for a given swap strategy. Perform a\n", - " binary search over the number of swap layers, and for each number\n", - " of swap layers solve a subgraph isomorphism problem formulated as\n", - " a SAT problem.\n", - "\n", - " Args:\n", - " program_graph (rx.Graph): The program graph with commuting gates, where\n", - " each edge represents a two-qubit gate.\n", - " swap_strategy (SwapStrategy): The swap strategy to use to find the\n", - " initial mapping.\n", - " min_layers (int): The minimum number of swap layers to consider.\n", - " Defaults to the maximum degree of the\n", - " program graph - 2.\n", - " max_layers (int): The maximum number of swap layers to consider.\n", - " Defaults to the number of qubits in the\n", - " swap strategy - 2.\n", - "\n", - " Returns:\n", - " dict[int, SATResult]: A dictionary containing the results of the SAT\n", - " solver for each number of swap layers.\n", - " \"\"\"\n", - " num_nodes_g1 = len(program_graph.nodes())\n", - " num_nodes_g2 = swap_strategy.distance_matrix.shape[0]\n", - " if num_nodes_g1 > num_nodes_g2:\n", - " return SATResult(False, [], [], 0)\n", - " if min_layers is None:\n", - " # use the maximum degree of the program graph - 2\n", - " # as the lower bound.\n", - " min_layers = max((d for _, d in program_graph.degree)) - 2\n", - " if max_layers is None:\n", - " max_layers = num_nodes_g2 - 1\n", - "\n", - " variable_pool = IDPool(start_from=1)\n", - " variables = np.array(\n", - " [\n", - " [variable_pool.id(f\"v_{i}_{j}\") for j in range(num_nodes_g2)]\n", - " for i in range(num_nodes_g1)\n", - " ],\n", - " dtype=int,\n", - " )\n", - " vid2mapping = {v: idx for idx, v in np.ndenumerate(variables)}\n", - " binary_search_results = {}\n", - "\n", - " def interrupt(solver):\n", - " # This function is called to interrupt the solver when the\n", - " # timeout is reached.\n", - " solver.interrupt()\n", - "\n", - " # Make a cnf (conjunctive normal form) for the one-to-one\n", - " # mapping constraint\n", - " cnf1 = []\n", - " for i in range(num_nodes_g1):\n", - " clause = variables[i, :].tolist()\n", - " cnf1.append(clause)\n", - " for k, m in combinations(clause, 2):\n", - " cnf1.append([-1 * k, -1 * m])\n", - " for j in range(num_nodes_g2):\n", - " clause = variables[:, j].tolist()\n", - " for k, m in combinations(clause, 2):\n", - " cnf1.append([-1 * k, -1 * m])\n", - "\n", - " # Perform a binary search over the number of swap layers to find the\n", - " # minimum number of swap layers that satisfies the subgraph isomorphism\n", - " # problem.\n", - " while min_layers < max_layers:\n", - " num_layers = (min_layers + max_layers) // 2\n", - "\n", - " # Create the connectivity matrix. Note that if the swap strategy\n", - " # cannot reach full connectivity then its distance matrix will have\n", - " # entries with -1. These entries must be treated as False.\n", - " d_matrix = swap_strategy.distance_matrix\n", - " connectivity_matrix = (\n", - " (-1 < d_matrix) & (d_matrix <= num_layers)\n", - " ).astype(int)\n", - " # Make a cnf for the adjacency constraint\n", - " cnf2 = []\n", - " for e_0, e_1 in list(program_graph.edge_list()):\n", - " clause_matrix = np.multiply(\n", - " connectivity_matrix, variables[e_1, :]\n", - " )\n", - " clause = np.concatenate(\n", - " (\n", - " [[-variables[e_0, i]] for i in range(num_nodes_g2)],\n", - " clause_matrix,\n", - " ),\n", - " axis=1,\n", - " )\n", - " # Remove 0s from each clause\n", - " cnf2.extend([c[c != 0].tolist() for c in clause])\n", - "\n", - " cnf = CNF(from_clauses=cnf1 + cnf2)\n", - "\n", - " with Solver(bootstrap_with=cnf, use_timer=True) as solver:\n", - " # Solve the SAT problem with a timeout.\n", - " # Timer is used to interrupt the solver when the\n", - " # timeout is reached.\n", - " timer = Timer(self.timeout, interrupt, [solver])\n", - " timer.start()\n", - " status = solver.solve_limited(expect_interrupt=True)\n", - " timer.cancel()\n", - " # Get the solution and the elapsed time.\n", - " sol = solver.get_model()\n", - " e_time = solver.time()\n", - "\n", - " print(\n", - " f\"Layers: {num_layers}, Status: {status}, Time: {e_time}\"\n", - " )\n", - " if status:\n", - " # If the SAT problem is satisfiable, convert the solution\n", - " # to a mapping.\n", - " mapping = [vid2mapping[idx] for idx in sol if idx > 0]\n", - " binary_search_results[num_layers] = SATResult(\n", - " status, sol, mapping, e_time\n", - " )\n", - " max_layers = num_layers\n", - " else:\n", - " # If the SAT problem is unsatisfiable, return the last\n", - " # satisfiable solution.\n", - " binary_search_results[num_layers] = SATResult(\n", - " status, sol, [], e_time\n", - " )\n", - " min_layers = num_layers + 1\n", - "\n", - " return binary_search_results\n", - "\n", - " def remap_graph_with_sat(\n", - " self, graph: rx.Graph, swap_strategy, max_layers\n", - " ):\n", - " \"\"\"Applies the SAT mapping.\n", - "\n", - " Args:\n", - " graph (nx.Graph): The graph to remap.\n", - " swap_strategy (SwapStrategy): The swap strategy to use\n", - " to find the initial mapping.\n", - "\n", - " Returns:\n", - " tuple: A tuple containing the remapped graph, the edge map, and the\n", - " number of layers of the swap strategy that was used to find the\n", - " initial mapping. If no solution is found then the tuple contains\n", - " None for each element. Note the returned edge map `{k: v}` means that\n", - " node `k` in the original graph gets mapped to node `v` in the\n", - " Pauli strings.\n", - " \"\"\"\n", - " num_nodes = len(graph.nodes())\n", - " results = self.find_initial_mappings(\n", - " graph, swap_strategy, 0, max_layers\n", - " )\n", - " solutions = [k for k, v in results.items() if v.satisfiable]\n", - "\n", - " if len(solutions):\n", - " min_k = min(solutions)\n", - " edge_map = dict(results[min_k].mapping)\n", - " # Create the remapped graph\n", - " remapped_graph = rx.PyGraph()\n", - " remapped_graph.add_nodes_from(range(num_nodes))\n", - " mapping = dict(results[min_k].mapping)\n", - " for i, graph_edge in enumerate(list(graph.edge_list())):\n", - " remapped_edge = tuple(mapping[node] for node in graph_edge)\n", - " remapped_graph.add_edge(*remapped_edge, graph.edges()[i])\n", - " return remapped_graph, edge_map, min_k\n", - " else:\n", - " return None, None, None" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "e689e09e-6ca7-4154-8602-d1d954ebe80b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Layers: 0, Status: True, Time: 0.02229800000000015\n", - "Map from old to new nodes: {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20, 21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26, 27: 27, 28: 28, 29: 29, 30: 30, 31: 31, 32: 32, 33: 33, 34: 34, 35: 35, 36: 36, 37: 37, 38: 38, 39: 39, 40: 40, 41: 41, 42: 42, 43: 43, 44: 44, 45: 45, 46: 46, 47: 47, 48: 48, 49: 49, 50: 50, 51: 51, 52: 52, 53: 53, 54: 54, 55: 55, 56: 56, 57: 57, 58: 58, 59: 59, 60: 60, 61: 61, 62: 62, 63: 63, 64: 64, 65: 65, 66: 66, 67: 67, 68: 68, 69: 69, 70: 70, 71: 71, 72: 72, 73: 73, 74: 74, 75: 75, 76: 76, 77: 77, 78: 78, 79: 79, 80: 80, 81: 81, 82: 82, 83: 83, 84: 84, 85: 85, 86: 86, 87: 87, 88: 88, 89: 89, 90: 90, 91: 91, 92: 92, 93: 93, 94: 94, 95: 95, 96: 96, 97: 97, 98: 98, 99: 99}\n", - "Min SWAP layers: 0\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sm = SATMapper(timeout=10)\n", - "remapped_graph, edge_map, min_swap_layers = sm.remap_graph_with_sat(\n", - " graph=graph_100, swap_strategy=swap_strategy, max_layers=1\n", - ")\n", - "print(\"Map from old to new nodes: \", edge_map)\n", - "print(\"Min SWAP layers:\", min_swap_layers)\n", - "draw_graph(remapped_graph, node_size=200, with_labels=True, width=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "ecf6e8c3-65c2-4430-8dd3-d67b8842045d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZII', 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'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIZIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIZIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIZIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIZIIIIIIIIIIIIIIIII', 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1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j])\n" - ] - } - ], - "source": [ - "remapped_max_cut_paulis = build_max_cut_paulis(remapped_graph)\n", - "# define a qiskit SparsePauliOp from the list of paulis\n", - "remapped_cost_operator = SparsePauliOp.from_list(remapped_max_cut_paulis)\n", - "print(remapped_cost_operator)" - ] - }, - { - "cell_type": "markdown", - "id": "5ae531be-80eb-4acd-b84a-7d466fd872e7", - "metadata": {}, - "source": [ - "#### Build a QAOA circuit with the SWAP strategy and the SAT mapping\n", - "\n", - "We only want to apply the SWAP strategies to the cost operator layer, so we start by creating the isolated block that we will later transform and append to the final QAOA circuit.\n", - "\n", - "For this, we can use the [`QAOAAnsatz`](/docs/api/qiskit/qiskit.circuit.library.QAOAAnsatz) class from Qiskit. We input an empty circuit to the `initial_state` and `mixer_operator` fields to make sure we are building an isolated cost operator layer.\n", - "We also define the edge\\_coloring map so that RZZ gates are positioned next to SWAP gates. This strategic placement allows us to exploit CX cancellations, optimizing the circuit for better performance.\n", - "This process is executed within the `create_qaoa_swap_circuit` function.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "57d5eb53-9cda-4c38-a00b-26ed4b533bcd", - "metadata": {}, - "outputs": [], - "source": [ - "def make_meas_map(circuit: QuantumCircuit) -> dict:\n", - " \"\"\"Return a mapping from qubit index (the key) to classical bit (the value).\n", - "\n", - " This allows us to account for the swapping order introduced by the SWAP strategy.\n", - " \"\"\"\n", - " creg = circuit.cregs[0]\n", - " qreg = circuit.qregs[0]\n", - "\n", - " meas_map = {}\n", - " for inst in circuit.data:\n", - " if inst.operation.name == \"measure\":\n", - " meas_map[qreg.index(inst.qubits[0])] = creg.index(inst.clbits[0])\n", - "\n", - " return meas_map\n", - "\n", - "\n", - "def apply_swap_strategy(\n", - " circuit: QuantumCircuit,\n", - " swap_strategy: SwapStrategy,\n", - " edge_coloring: dict[tuple[int, int], int] | None = None,\n", - ") -> QuantumCircuit:\n", - " \"\"\"Transpile with a SWAP strategy.\n", - "\n", - " Returns:\n", - " A quantum circuit transpiled with the given swap strategy.\n", - " \"\"\"\n", - "\n", - " pm_pre = PassManager(\n", - " [\n", - " FindCommutingPauliEvolutions(),\n", - " Commuting2qGateRouter(\n", - " swap_strategy,\n", - " edge_coloring,\n", - " ),\n", - " ]\n", - " )\n", - " return pm_pre.run(circuit)\n", - "\n", - "\n", - "def apply_qaoa_layers(\n", - " cost_layer: QuantumCircuit,\n", - " meas_map: dict,\n", - " num_layers: int,\n", - " gamma: list[float] | ParameterVector = None,\n", - " beta: list[float] | ParameterVector = None,\n", - " initial_state: QuantumCircuit = None,\n", - " mixer: QuantumCircuit = None,\n", - "):\n", - " \"\"\"Applies QAOA layers to construct circuit.\n", - "\n", - " First, the initial state is applied. If `initial_state` is None, we begin in the\n", - " initial superposition state. Next, we alternate between layers of the cost operator\n", - " and the mixer. The cost operator is alternatively applied in order and in reverse\n", - " instruction order. This allows us to apply the swap strategy on odd `p` layers\n", - " and undo the swap strategy on even `p` layers.\n", - " \"\"\"\n", - "\n", - " num_qubits = cost_layer.num_qubits\n", - " new_circuit = QuantumCircuit(num_qubits, num_qubits)\n", - "\n", - " if initial_state is not None:\n", - " new_circuit.append(initial_state, range(num_qubits))\n", - " else:\n", - " # all h state by default\n", - " new_circuit.h(range(num_qubits))\n", - "\n", - " if gamma is None or beta is None:\n", - " gamma = ParameterVector(\"γ'\", num_layers)\n", - " if mixer is None or mixer.num_parameters == 0:\n", - " beta = ParameterVector(\"β'\", num_layers)\n", - " else:\n", - " beta = ParameterVector(\"β'\", num_layers * mixer.num_parameters)\n", - "\n", - " if mixer is not None:\n", - " mixer_layer = mixer\n", - " else:\n", - " mixer_layer = QuantumCircuit(num_qubits)\n", - " mixer_layer.rx(-2 * beta[0], range(num_qubits))\n", - "\n", - " for layer in range(num_layers):\n", - " bind_dict = {cost_layer.parameters[0]: gamma[layer]}\n", - " cost_layer_ = cost_layer.assign_parameters(bind_dict)\n", - " bind_dict = {\n", - " mixer_layer.parameters[i]: beta[layer + i]\n", - " for i in range(mixer_layer.num_parameters)\n", - " }\n", - " layer_mixer = mixer_layer.assign_parameters(bind_dict)\n", - "\n", - " if layer % 2 == 0:\n", - " new_circuit.append(cost_layer_, range(num_qubits))\n", - " else:\n", - " new_circuit.append(cost_layer_.reverse_ops(), range(num_qubits))\n", - "\n", - " new_circuit.append(layer_mixer, range(num_qubits))\n", - "\n", - " for qidx, cidx in meas_map.items():\n", - " new_circuit.measure(qidx, cidx)\n", - "\n", - " return new_circuit\n", - "\n", - "\n", - "def create_qaoa_swap_circuit(\n", - " cost_operator: SparsePauliOp,\n", - " swap_strategy: SwapStrategy,\n", - " edge_coloring: dict = None,\n", - " theta: list[float] = None,\n", - " qaoa_layers: int = 1,\n", - " initial_state: QuantumCircuit = None,\n", - " mixer: QuantumCircuit = None,\n", - "):\n", - " \"\"\"Create the circuit for QAOA.\n", - "\n", - " Notes: This circuit construction for QAOA works for quadratic terms in `Z` and will be\n", - " extended to first-order terms in `Z`. Higher-orders are not supported.\n", - "\n", - " Args:\n", - " cost_operator: the cost operator.\n", - " swap_strategy: selected swap strategy\n", - " edge_coloring: A coloring of edges that should correspond to the coupling\n", - " map of the hardware. It defines the order in which we apply the Rzz\n", - " gates. This allows us to choose an ordering such that `Rzz` gates will\n", - " immediately precede SWAP gates to leverage CNOT cancellation.\n", - " theta: The QAOA angles.\n", - " qaoa_layers: The number of layers of the cost operator and the mixer operator.\n", - " initial_state: The initial state on which we apply layers of cost operator\n", - " and mixer.\n", - " mixer: The QAOA mixer. It will be applied as is onto the QAOA circuit. Therefore,\n", - " its output must have the same ordering of qubits as its input.\n", - " \"\"\"\n", - "\n", - " num_qubits = cost_operator.num_qubits\n", - "\n", - " if theta is not None:\n", - " gamma = theta[: len(theta) // 2]\n", - " beta = theta[len(theta) // 2 :]\n", - " qaoa_layers = len(theta) // 2\n", - " else:\n", - " gamma = beta = None\n", - "\n", - " # First, create the ansatz of 1 layer of QAOA without mixer\n", - " cost_layer = QAOAAnsatz(\n", - " cost_operator,\n", - " reps=1,\n", - " initial_state=QuantumCircuit(num_qubits),\n", - " mixer_operator=QuantumCircuit(num_qubits),\n", - " ).decompose()\n", - "\n", - " # This will allow us to recover the permutation of the measurements that the swaps introduce.\n", - " cost_layer.measure_all()\n", - "\n", - " # Now, apply the swap strategy for commuting gates\n", - " cost_layer = apply_swap_strategy(cost_layer, swap_strategy, edge_coloring)\n", - "\n", - " # Compute the measurement map (qubit to classical bit).\n", - " # We will apply this for odd layers where the swaps were inserted.\n", - " if qaoa_layers % 2 == 1:\n", - " meas_map = make_meas_map(cost_layer)\n", - " else:\n", - " meas_map = {idx: idx for idx in range(num_qubits)}\n", - "\n", - " cost_layer.remove_final_measurements()\n", - "\n", - " # Finally, introduce the mixer circuit and add measurements following measurement map\n", - " circuit = apply_qaoa_layers(\n", - " cost_layer, meas_map, qaoa_layers, gamma, beta, initial_state, mixer\n", - " )\n", - "\n", - " return circuit" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "7793ef92-ce59-4fd7-b43f-48d4e3427e3a", - "metadata": {}, - "outputs": [], - "source": [ - "# We can define the edge_coloring map so that RZZ gates are positioned right before SWAP gates to exploit CX cancellations\n", - "# We use greedy edge coloring from rustworkx to color the edges of the graph. This coloring is used to order the RZZ gates in the circuit.\n", - "\n", - "edge_coloring_idx = rx.graph_greedy_edge_color(graph_100)\n", - "edge_coloring = {\n", - " edge: edge_coloring_idx[idx]\n", - " for idx, edge in enumerate(list(graph_100.edge_list()))\n", - "}\n", - "edge_coloring = {tuple(sorted(k)): v for k, v in edge_coloring.items()}" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "82ae28b3-85eb-4487-8100-1e622e93cccf", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qaoa_circ = create_qaoa_swap_circuit(\n", - " remapped_cost_operator,\n", - " swap_strategy,\n", - " edge_coloring=edge_coloring,\n", - " qaoa_layers=1,\n", - ")\n", - "qaoa_circ.draw(output=\"mpl\", fold=False)" - ] - }, - { - "cell_type": "markdown", - "id": "e2afd1a7-0980-433b-a3a8-303d7e7718b1", - "metadata": {}, - "source": [ - "## Step 3: Execute using Qiskit primitives\n", - "\n", - "### Define a CVaR cost function\n", - "\n", - "This example shows how to use the Conditional Value at Risk (CVaR) cost function introduced in [\\[3\\]](#references) within the variational quantum optimization algorithms.\n", - "\n", - "The CVaR of a random variable $X$ for a confidence level $α ∈ (0, 1]$ is defined as\n", - "$CVaR_{\\alpha}(X) = \\mathbb{E} \\lbrack X | X \\leq F_X^{-1}(\\alpha) \\rbrack$\n", - "where $F_X^{-1}(p)$ is the inverse cumulative distribution function of $X$. In other words, CVaR is the expected value of the lower $\\alpha$-tail of the distribution of $X$.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Write (causes no increase in depth, just increases number of qubits to number of qubits on device)\n", - "\n", - "pass_manager = generate_preset_pass_manager(\n", - " backend=backend,\n", - " optimization_level=3,\n", - ")\n", - " \n", - "transpiled_qaoa_circ = pass_manager.run(qaoa_circ)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "e6794cf3-7fbe-46a5-bdc0-5faad1235365", - "metadata": {}, - "outputs": [], - "source": [ - "# Utility functions for the evaluation of the expectation value of a measured state\n", - "# In this code, for optimization, the measured state is converted into a bit string,\n", - "# and the sign of the value is determined by taking the exclusive OR of the bits\n", - "# corresponding to Pauli Z.\n", - "\n", - "_PARITY = np.array(\n", - " [-1 if bin(i).count(\"1\") % 2 else 1 for i in range(256)],\n", - " dtype=np.complex128,\n", - ")\n", - "\n", - "\n", - "def evaluate_sparse_pauli(state: int, observable: SparsePauliOp) -> complex:\n", - " \"\"\"Utility for the evaluation of the expectation value of a measured state.\n", - "\n", - " Args:\n", - " state (int): The measured state.\n", - " observable (SparsePauliOp): The observable to evaluate the expectation value for.\n", - "\n", - " Returns:\n", - " complex: The expectation value of the measured state.\n", - " \"\"\"\n", - " packed_uint8 = np.packbits(observable.paulis.z, axis=1, bitorder=\"little\") # convert observable to array with 8 bit integer\n", - " state_bytes = np.frombuffer(\n", - " state.to_bytes(packed_uint8.shape[1], \"little\"), dtype=np.uint8 # convert bistring to array with 8 bit integer\n", - " )\n", - " reduced = np.bitwise_xor.reduce(packed_uint8 & state_bytes, axis=1) # take bitwise xor of the result of 'and' conditional on the above two, return 0 or 1\n", - " return np.sum(observable.coeffs * _PARITY[reduced])" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "c5e5f7a4-01f6-4a02-9114-3bb0e24be1a2", - "metadata": {}, - "outputs": [], - "source": [ - "def qaoa_sampler_cost_fun(\n", - " params, ansatz, hamiltonian, sampler, aggregation=None\n", - "):\n", - " \"\"\"Standard sampler-based QAOA cost function to be plugged into optimizer routines.\n", - "\n", - " Args:\n", - " params (np.ndarray): Parameters for the ansatz.\n", - " ansatz (QuantumCircuit): Ansatz circuit.\n", - " hamiltonian (SparsePauliOp): Hamiltonian to be minimized.\n", - " sampler (QAOASampler): Sampler to be used.\n", - " aggregation (Callable | float | None): Aggregation function to be applied to\n", - " the sampled results. If None, the sum of the expectation values is returned.\n", - " If float, the CVaR with the given alpha is used.\n", - " \"\"\"\n", - " # Run the circuit\n", - " job = sampler.run([(ansatz, params)])\n", - " sampler_result = job.result()\n", - " sampled_int_counts = sampler_result[0].data.c.get_int_counts() # bitstrings are stored as integers\n", - " shots = sum(sampled_int_counts.values())\n", - " int_count_distribution = {\n", - " key: val / shots for key, val in sampled_int_counts.items()\n", - " }\n", - "\n", - " # a dictionary containing: {state: (measurement probability, value)}\n", - " evaluated = {\n", - " state: (\n", - " probability,\n", - " np.real(evaluate_sparse_pauli(state, hamiltonian)),\n", - " )\n", - " for state, probability in int_count_distribution.items()\n", - " }\n", - "\n", - " # If aggregation is None, return the sum of the expectation values.\n", - " # If aggregation is a float, return the CVaR with the given alpha.\n", - " # Otherwise, use the aggregation function.\n", - " if aggregation is None:\n", - " result = sum(\n", - " probability * value for probability, value in evaluated.values()\n", - " )\n", - " elif isinstance(aggregation, float):\n", - " cvar_aggregation = _get_cvar_aggregation(aggregation)\n", - " result = cvar_aggregation(evaluated.values())\n", - " else:\n", - " result = aggregation(evaluated.values())\n", - "\n", - " global iter_counts, result_dict\n", - " iter_counts += 1\n", - " temp_dict = {}\n", - " temp_dict[\"params\"] = params.tolist()\n", - " temp_dict[\"cvar_fval\"] = result\n", - " temp_dict[\"fval\"] = sum(\n", - " probability * value for probability, value in evaluated.values()\n", - " )\n", - " temp_dict[\"distribution\"] = sampled_int_counts\n", - " temp_dict[\"evaluated\"] = evaluated\n", - " result_dict[iter_counts] = temp_dict\n", - " print(f\"Iteration {iter_counts}: {result}\")\n", - "\n", - " return result\n", - "\n", - "\n", - "def _get_cvar_aggregation(alpha: float | None) -> Callable:\n", - " \"\"\"Return the CVaR aggregation function with the given alpha.\n", - "\n", - " Args:\n", - " alpha (float | None): Alpha value for the CVaR aggregation. If None, 1 is used\n", - " by default.\n", - " Raises:\n", - " ValueError: If alpha is not in [0, 1].\n", - " \"\"\"\n", - " if alpha is None:\n", - " alpha = 1\n", - " elif not 0 <= alpha <= 1:\n", - " raise ValueError(f\"alpha must be in [0, 1], but {alpha} was given.\")\n", - "\n", - "\n", - " def cvar_aggregation(\n", - " objective_dict: Iterable[tuple[float, float]],\n", - " ) -> float:\n", - " \"\"\"Return the CVaR of the given measurements.\n", - " Args:\n", - " objective_dict (Iterable[tuple[float, float]]): An iterable of tuples containing\n", - " the measured bit string and the objective value based on the bit string.\n", - "\n", - " \"\"\"\n", - " sorted_measurements = sorted(objective_dict, key=lambda x: x[1])\n", - " # accumulate the probabilities until alpha is reached\n", - " accumulated_percent = 0.0\n", - " cvar = 0.0\n", - " for probability, value in sorted_measurements:\n", - " cvar += value * min(probability, alpha - accumulated_percent)\n", - " accumulated_percent += probability\n", - " if accumulated_percent >= alpha:\n", - " break\n", - " return cvar / alpha\n", - "\n", - " return cvar_aggregation" - ] - }, - { - "cell_type": "markdown", - "id": "63fa2ab4-5354-4022-ab46-e9bbf73870de", - "metadata": {}, - "source": [ - "The CVaR can be used as an error mitigation technique as previously discussed [\\[4\\]](#references). In this example, we determine $\\alpha$ and the number of shots according to the circuit's error rate.\n", - "\n" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "id": "1a869c2d-ae51-47d3-8a41-bfcf5e505f59", + "metadata": {}, + "source": [ + "# Advanced techniques for QAOA\n", + "\n", + "*Usage estimate: 3 minutes on IBM Kingston (NOTE: This is an estimate only. Your runtime may vary.)*" + ] + }, + { + "cell_type": "markdown", + "id": "ea97567d-810f-4cca-8edf-a47d70ea870a", + "metadata": {}, + "source": [ + "## Background\n", + "\n", + "This notebook introduces advanced techniques to improve the performance of the **Quantum Approximate Optimization Algorithm (QAOA)** with a large number of qubits.\n", + "Please see [Solve utility-scale quantum optimization problems](/docs/tutorials/quantum-approximate-optimization-algorithm) for an introduction to QAOA.\n", + "\n", + "The advanced techniques in this notebook include:\n", + "\n", + "* **SWAP strategy with SAT initial mapping**: This is a specifically designed transpiler pass for QAOA that uses a SWAP strategy and a SAT solver together to improve the selection of which physical qubits on the QPU to use. The SWAP strategy exploits the commutativity of the QAOA operators to reorder gates so that layers of SWAP gates can be simultaneously executed, thus reducing the depth of the circuit [\\[1\\]](#references). The SAT solver is used to find an initial mapping that minimizes the number of SWAP operations needed to map the qubits in the circuit to the physical qubits on the device [\\[2\\]](#references) .\n", + "* **CVaR cost function**: Typically the expected value of the cost Hamiltonian is used as the cost function for QAOA, but as was shown in [\\[3\\]](#references) , focusing on the tail of the distribution, rather than the expected value, can improve the performance of QAOA for combinatorial optimization problems. The CVaR accomplishes this. For a given set of shots with corresponding objective values of the considered optimization problem, the Conditional Value at Risk (CVaR) with confidence level $\\alpha \\in [0, 1]$ is defined as the average of the $\\alpha$ best shots [\\[3\\]](#references).\n", + " Thus, $\\alpha = 1$ corresponds to the standard expected value, while $\\alpha=0$ corresponds to the minimum of the given shots, and $\\alpha \\in (0, 1)$ is a tradeoff between focusing on better shots, while still applying some averaging to smooth out the optimization landscape. Additionally, the CVaR can be used as an error mitigation technique to improve the quality of the objective value estimation [\\[4\\]](#references)." + ] + }, + { + "cell_type": "markdown", + "id": "40fb546e-85e0-450b-a5ea-5d08950d129f", + "metadata": {}, + "source": [ + "## Requirements\n", + "\n", + "Before starting this tutorial, be sure you have the following installed:\n", + "\n", + "* Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", + "* Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\n", + "* Rustworkx graph library (`pip install rustworkx`)\n", + "* Python SAT (`pip install python-sat`)" + ] + }, + { + "cell_type": "markdown", + "id": "50285e5f-1a7b-471c-a223-1ae0af19d9ed", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d019ea68-61e0-4341-84c7-e612ca10dde7", + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "import numpy as np\n", + "import rustworkx as rx\n", + "from dataclasses import dataclass\n", + "from itertools import combinations\n", + "from threading import Timer\n", + "from collections.abc import Callable, Iterable\n", + "from pysat.formula import CNF, IDPool\n", + "from pysat.solvers import Solver\n", + "from scipy.optimize import minimize\n", + "from rustworkx.visualization import mpl_draw as draw_graph\n", + "\n", + "from qiskit.quantum_info import SparsePauliOp\n", + "from qiskit.circuit.library import QAOAAnsatz\n", + "from qiskit.circuit import QuantumCircuit, ParameterVector\n", + "from qiskit.transpiler import CouplingMap, PassManager\n", + "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", + "from qiskit.transpiler.passes.routing.commuting_2q_gate_routing import (\n", + " SwapStrategy,\n", + " FindCommutingPauliEvolutions,\n", + " Commuting2qGateRouter,\n", + ")\n", + "\n", + "from qiskit_ibm_runtime import QiskitRuntimeService, Session\n", + "from qiskit_ibm_runtime import SamplerV2 as Sampler" + ] + }, + { + "cell_type": "markdown", + "id": "8b77b0c9-f5a6-476e-86b8-069ba14f9ab3", + "metadata": {}, + "source": [ + "### Max-Cut Problem\n", + "\n", + "Let's consider solving the **Max-Cut** problem on a graph with 100 nodes using QAOA.\n", + "The Max-Cut problem is a combinatorial optimization problem that is defined on a graph $G = (V, E)$, where $V$ is the set of vertices and $E$ is the set of edges. The goal is to partition the vertices into two sets, $S$ and $V \\setminus S$, such that the number of edges between the two sets is maximized.\n", + "In this example, we use a graph with 100 nodes that is based on a hardware coupling map." + ] + }, + { + "cell_type": "markdown", + "id": "b825afbf-10fd-4926-bd65-05272044f107", + "metadata": {}, + "source": [ + "## Step 1: Map classical inputs to a quantum problem\n", + "\n", + "### Graph → Hamiltonian\n", + "\n", + "First, map the problem onto a quantum circuit that is suited for the QAOA. Details on this process can be found in the [introductory QAOA tutorial.](/docs/tutorials/quantum-approximate-optimization-algorithm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c25db38-5f36-4a39-bdde-4f83616a6dde", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 30, - "id": "032bf312-4bf4-40f4-81f0-2ae8a719b98b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "layer fidelity 0.6544189744623636\n", - "\n", - "The corresponding CVaR aggregation value is: 0.38642994025160377\n", - "To mitigate the twirled noise, increase shots by a factor of 2.5877912031063173\n" - ] - } - ], - "source": [ - "num_2q_ops = transpiled_qaoa_circ.count_ops()[\"cz\"] # the two qubit gates on our backend are cz's.\n", - "\n", - "for el in backend.properties().general:\n", - " if el.name[:2] == \"lf\" and el.name[3:] == str(n): # pick out lf_100, lf of the best 100q chain\n", - " lf = el.value # layer fidelity\n", - " print(\"layer fidelity\", lf)\n", - " eplg = 1 - lf ** (1 / (n - 1)) # error per layered gate (EPLG)\n", - " fid_cz = 1 - eplg\n", - " gamma_cz = 1 / fid_cz**2\n", - " gamma_circ = gamma_cz ** num_2q_ops\n", - "\n", - "cvar_aggregation = 1 / np.sqrt(gamma_circ)\n", - "print(\"\")\n", - "print(\"The corresponding CVaR aggregation value is: \", cvar_aggregation)\n", - "print(\n", - " \"To mitigate the twirled noise, increase shots by a factor of\", np.sqrt(gamma_circ)\n", - ")" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Instantiate runtime to access backend\n", + "service = QiskitRuntimeService()\n", + "backend = service.least_busy(\n", + " min_num_qubits=100, operational=True, simulator=False\n", + ")\n", + "print(backend)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e04ce982-8294-4ebe-8dcc-f74205938800", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 1: -10.010143313246703\n", - "Iteration 2: -10.778378196890142\n", - "Iteration 3: -33.11648157016098\n", - "Iteration 4: 23.37766951385026\n", - "Iteration 5: -8.456279717029192\n", - "Iteration 6: -20.250216714385534\n", - "Iteration 7: -45.87610233094513\n", - "Iteration 8: -20.4522784936772\n", - "Iteration 9: -49.63202770645467\n", - "Iteration 10: -49.10586683562599\n", - "Iteration 11: -49.15188090417742\n", - "Iteration 12: -45.84209193245048\n", - "Iteration 13: -49.634028318131016\n", - "Iteration 14: -48.542306019104984\n", - "Iteration 15: -49.8580968258604\n", - "Iteration 16: -49.7000485034442\n", - " message: Return from COBYLA because the trust region radius reaches its lower bound.\n", - " success: True\n", - " status: 0\n", - " fun: -49.8580968258604\n", - " x: [ 4.262e+00 2.820e+00]\n", - " nfev: 16\n", - " maxcv: 0.0\n" - ] - } - ], - "source": [ - "iter_counts = 0\n", - "result_dict={}\n", - "init_params=[np.pi, np.pi/2]\n", - "\n", - "with Session(backend=backend) as session:\n", - " sampler=Sampler(mode=session)\n", - " sampler.options.default_shots = int(1000/cvar_aggregation)\n", - " sampler.options.dynamical_decoupling.enable=True\n", - " sampler.options.dynamical_decoupling.sequence_type='XY4'\n", - " sampler.options.twirling.enable_gates=True\n", - " sampler.options.twirling.enable_measure=True\n", - "\n", - " result = minimize(qaoa_sampler_cost_fun,\n", - " init_params,\n", - " args= (transpiled_qaoa_circ,\n", - " remapped_cost_operator,\n", - " sampler,\n", - " cvar_aggregation\n", - " ),\n", - " method='COBYLA',\n", - " tol=1e-2)\n", - "print(result)" + "data": { + "text/plain": [ + "True" ] - }, + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "backend.coupling_map.is_symmetric" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8b864d32-9483-45ea-831f-60488e330adb", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "1d190fa4-3bbe-412a-b296-6dddd3ad2b12", - "metadata": {}, - "source": [ - "## Step 4: Post-process and return result in desired classical format\n", - "\n" + "data": { + "text/plain": [ + "\"Output" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n = 100\n", + "graph_100 = rx.PyGraph()\n", + "graph_100.add_nodes_from((np.arange(0, n, 1)))\n", + "w = 1.0\n", + "elist = []\n", + "\n", + "for edge in backend.coupling_map:\n", + " if (edge[0] < n) and (edge[1] < n):\n", + " if (edge[1], edge[0], w) not in elist:\n", + " elist.append((edge[0], edge[1], w))\n", + "\n", + "graph_100.add_edges_from(elist)\n", + "draw_graph(graph_100, with_labels=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "e39c4e42-ce97-4a04-8879-da4d33a684bc", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 100, - "id": "761821cb-9a0c-4efb-806b-75513302d34a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "plt.figure(figsize=(12, 6))\n", - "plt.plot(\n", - " [result_dict[i][\"cvar_fval\"] for i in range(1, iter_counts + 1)],\n", - " label=\"CVaR\",\n", - ")\n", - "plt.plot(\n", - " [result_dict[i][\"fval\"] for i in range(1, iter_counts + 1)],\n", - " label=\"Standard\",\n", - ")\n", - "plt.legend()\n", - "plt.xlabel(\"Iteration\")\n", - "plt.ylabel(\"Cost\")\n", - "plt.show()" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost Function Hamiltonian: SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIZIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIZIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIZIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIZIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIZIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIZIIIIIIIIIIIIIIIII', 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1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j])\n" + ] + } + ], + "source": [ + "# Construct cost hamiltonian\n", + "\n", + "\n", + "def build_max_cut_paulis(graph: rx.PyGraph) -> list[tuple[str, float]]:\n", + " \"\"\"Convert the graph to Pauli list.\n", + "\n", + " This function does the inverse of `build_max_cut_graph`\n", + " \"\"\"\n", + " pauli_list = []\n", + " for edge in list(graph.edge_list()):\n", + " paulis = [\"I\"] * len(graph)\n", + " paulis[edge[0]], paulis[edge[1]] = \"Z\", \"Z\"\n", + "\n", + " weight = graph.get_edge_data(edge[0], edge[1])\n", + "\n", + " pauli_list.append((\"\".join(paulis)[::-1], weight))\n", + "\n", + " return pauli_list\n", + "\n", + "\n", + "max_cut_paulis = build_max_cut_paulis(graph_100)\n", + "\n", + "cost_hamiltonian = SparsePauliOp.from_list(max_cut_paulis)\n", + "print(\"Cost Function Hamiltonian:\", cost_hamiltonian)" + ] + }, + { + "cell_type": "markdown", + "id": "4e576068-53e7-4a06-a83b-87e95de141e9", + "metadata": {}, + "source": [ + "## Step 2: Optimize problem for quantum hardware execution\n", + "\n", + "### SWAP strategy with the SAT initial mapping\n", + "\n", + "We will demonstrate how to build and optimize QAOA circuits using the **SWAP strategy with SAT initial mapping**, a specifically designed transpiler pass for QAOA applied to quadratic problems.\n", + "\n", + "In this example, we choose a SWAP insertion strategy for blocks of commuting two-qubit gates, which applies layers of SWAP gates that are simultaneously executable on the coupling map. This strategy is presented in [\\[1\\]](#references) and is passed into `Commuting2qGateRouter` which is exposed as a standardized Qiskit transpiler pass (see [`Commuting2qGateRouter`](/docs/api/qiskit/qiskit.transpiler.passes.Commuting2qGateRouter)). We use a line swap strategy in this example." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "7c54402a-7696-4b0e-826a-6e0ac4c54395", + "metadata": {}, + "outputs": [], + "source": [ + "# Extract longest path with no repeated nodes\n", + "nodes = rx.longest_simple_path(graph_100)\n", + "\n", + "# Collect even edges and odd edges\n", + "even_edges = [\n", + " (nodes[i], nodes[i + 1])\n", + " if nodes[i] < nodes[i + 1]\n", + " else (nodes[i + 1], nodes[i])\n", + " for i in range(0, len(nodes) - 1, 2)\n", + "]\n", + "odd_edges = [\n", + " (nodes[i], nodes[i + 1])\n", + " if nodes[i] < nodes[i + 1]\n", + " else (nodes[i + 1], nodes[i])\n", + " for i in range(1, len(nodes) - 1, 2)\n", + "]\n", + "edge_list = [\n", + " (edge[0], edge[1]) if edge[0] < edge[1] else (edge[1], edge[0])\n", + " for edge in graph_100.edge_list()\n", + "]\n", + "\n", + "swap_strategy = SwapStrategy(CouplingMap(edge_list), (even_edges, odd_edges))" + ] + }, + { + "cell_type": "markdown", + "id": "ba53fd1d-0d68-43e3-9102-4c6e64f04de7", + "metadata": {}, + "source": [ + "#### Remap the graph using a SAT mapper\n", + "\n", + "Even when a circuit consists of commuting gates (this is the case for the QAOA circuit, but also for Trotterized simulations of Ising Hamiltonians), finding a good initial mapping is a challenging task. The SAT-based approach presented in [\\[2\\]](#references) enables the discovery of effective initial mappings for circuits with commuting gates, resulting in a significant reduction in the number of required SWAP layers. This approach has been demonstrated to scale to up to *500 qubits*, as illustrated in the paper.\n", + "\n", + "The following code demonstrates how to use the `SATMapper` from Matsuo et al. to remap the graph. This process allows the problem to be mapped to a more optimal initial state for a specified SWAP strategy, resulting in a significant reduction in the number of SWAP layers required to execute the circuit.\n", + "\n", + "In `SATMapper`, the problem of finding a good initial mapping is formulated as a SAT problem. A SAT solver is used to find such an initial mapping for the QAOA circuit. `python-sat` (`pysat` for short) is a Python library for a SAT solver, and we will use it to solve the SAT problem in this example." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "df9d861a-64e8-49ee-aed1-c41f437fa743", + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"A class to solve the SWAP gate insertion initial mapping problem\n", + "using the SAT approach from https://arxiv.org/abs/2212.05666.\n", + "\"\"\"\n", + "\n", + "\n", + "@dataclass\n", + "class SATResult:\n", + " \"\"\"A data class to hold the result of a SAT solver.\"\"\"\n", + "\n", + " satisfiable: bool # Satisfiable is True if the SAT model could be solved\n", + " # in a given time.\n", + " solution: dict # The solution to the SAT problem if it is satisfiable.\n", + " mapping: list # The mapping of nodes in the pattern graph to nodes in the\n", + " # target graph.\n", + " elapsed_time: float # The time it took to solve the SAT model.\n", + "\n", + "\n", + "class SATMapper:\n", + " r\"\"\"A class to introduce a SAT-approach to solve\n", + " the initial mapping problem in SWAP gate insertion for commuting gates.\n", + "\n", + " When this pass is run on a DAG it will look for the first instance of\n", + " :class:`.Commuting2qBlock` and use the program graph :math:`P` of this block\n", + " of gates to find a layout for a given swap strategy. This layout is found\n", + " with a binary search over the layers :math:`l` of the swap strategy. At each\n", + " considered layer a subgraph isomorphism problem formulated as a SAT is solved\n", + " by a SAT solver. Each instance is whether it is possible to embed the program\n", + " graph :math:`P` into the effective connectivity graph :math:`C_l` that is\n", + " achieved by applying :math:`l` layers of the swap strategy to the coupling map\n", + " :math:`C_0` of the backend. Since solving SAT problems can be hard, a\n", + " ``time_out`` fixes the maximum time allotted to the SAT solver for each\n", + " instance. If this time is exceeded the considered problem is deemed\n", + " unsatisfiable and the binary search proceeds to the next number of swap\n", + " layers :math:``l``.\n", + " \"\"\"\n", + "\n", + " def __init__(self, timeout: int = 60):\n", + " \"\"\"Initialize the SATMapping.\n", + "\n", + " Args:\n", + " timeout: The allowed time in seconds for each iteration of the SAT\n", + " solver. This variable defaults to 60 seconds.\n", + " \"\"\"\n", + " self.timeout = timeout\n", + "\n", + " def find_initial_mappings(\n", + " self,\n", + " program_graph: rx.Graph,\n", + " swap_strategy: SwapStrategy,\n", + " min_layers: int | None = None,\n", + " max_layers: int | None = None,\n", + " ) -> dict[int, SATResult]:\n", + " r\"\"\"Find an initial mapping for a given swap strategy. Perform a\n", + " binary search over the number of swap layers, and for each number\n", + " of swap layers solve a subgraph isomorphism problem formulated as\n", + " a SAT problem.\n", + "\n", + " Args:\n", + " program_graph (rx.Graph): The program graph with commuting gates, where\n", + " each edge represents a two-qubit gate.\n", + " swap_strategy (SwapStrategy): The swap strategy to use to find the\n", + " initial mapping.\n", + " min_layers (int): The minimum number of swap layers to consider.\n", + " Defaults to the maximum degree of the\n", + " program graph - 2.\n", + " max_layers (int): The maximum number of swap layers to consider.\n", + " Defaults to the number of qubits in the\n", + " swap strategy - 2.\n", + "\n", + " Returns:\n", + " dict[int, SATResult]: A dictionary containing the results of the SAT\n", + " solver for each number of swap layers.\n", + " \"\"\"\n", + " num_nodes_g1 = len(program_graph.nodes())\n", + " num_nodes_g2 = swap_strategy.distance_matrix.shape[0]\n", + " if num_nodes_g1 > num_nodes_g2:\n", + " return SATResult(False, [], [], 0)\n", + " if min_layers is None:\n", + " # use the maximum degree of the program graph - 2\n", + " # as the lower bound.\n", + " min_layers = max((d for _, d in program_graph.degree)) - 2\n", + " if max_layers is None:\n", + " max_layers = num_nodes_g2 - 1\n", + "\n", + " variable_pool = IDPool(start_from=1)\n", + " variables = np.array(\n", + " [\n", + " [variable_pool.id(f\"v_{i}_{j}\") for j in range(num_nodes_g2)]\n", + " for i in range(num_nodes_g1)\n", + " ],\n", + " dtype=int,\n", + " )\n", + " vid2mapping = {v: idx for idx, v in np.ndenumerate(variables)}\n", + " binary_search_results = {}\n", + "\n", + " def interrupt(solver):\n", + " # This function is called to interrupt the solver when the\n", + " # timeout is reached.\n", + " solver.interrupt()\n", + "\n", + " # Make a cnf (conjunctive normal form) for the one-to-one\n", + " # mapping constraint\n", + " cnf1 = []\n", + " for i in range(num_nodes_g1):\n", + " clause = variables[i, :].tolist()\n", + " cnf1.append(clause)\n", + " for k, m in combinations(clause, 2):\n", + " cnf1.append([-1 * k, -1 * m])\n", + " for j in range(num_nodes_g2):\n", + " clause = variables[:, j].tolist()\n", + " for k, m in combinations(clause, 2):\n", + " cnf1.append([-1 * k, -1 * m])\n", + "\n", + " # Perform a binary search over the number of swap layers to find the\n", + " # minimum number of swap layers that satisfies the subgraph isomorphism\n", + " # problem.\n", + " while min_layers < max_layers:\n", + " num_layers = (min_layers + max_layers) // 2\n", + "\n", + " # Create the connectivity matrix. Note that if the swap strategy\n", + " # cannot reach full connectivity then its distance matrix will have\n", + " # entries with -1. These entries must be treated as False.\n", + " d_matrix = swap_strategy.distance_matrix\n", + " connectivity_matrix = (\n", + " (-1 < d_matrix) & (d_matrix <= num_layers)\n", + " ).astype(int)\n", + " # Make a cnf for the adjacency constraint\n", + " cnf2 = []\n", + " for e_0, e_1 in list(program_graph.edge_list()):\n", + " clause_matrix = np.multiply(\n", + " connectivity_matrix, variables[e_1, :]\n", + " )\n", + " clause = np.concatenate(\n", + " (\n", + " [[-variables[e_0, i]] for i in range(num_nodes_g2)],\n", + " clause_matrix,\n", + " ),\n", + " axis=1,\n", + " )\n", + " # Remove 0s from each clause\n", + " cnf2.extend([c[c != 0].tolist() for c in clause])\n", + "\n", + " cnf = CNF(from_clauses=cnf1 + cnf2)\n", + "\n", + " with Solver(bootstrap_with=cnf, use_timer=True) as solver:\n", + " # Solve the SAT problem with a timeout.\n", + " # Timer is used to interrupt the solver when the\n", + " # timeout is reached.\n", + " timer = Timer(self.timeout, interrupt, [solver])\n", + " timer.start()\n", + " status = solver.solve_limited(expect_interrupt=True)\n", + " timer.cancel()\n", + " # Get the solution and the elapsed time.\n", + " sol = solver.get_model()\n", + " e_time = solver.time()\n", + "\n", + " print(\n", + " f\"Layers: {num_layers}, Status: {status}, Time: {e_time}\"\n", + " )\n", + " if status:\n", + " # If the SAT problem is satisfiable, convert the solution\n", + " # to a mapping.\n", + " mapping = [vid2mapping[idx] for idx in sol if idx > 0]\n", + " binary_search_results[num_layers] = SATResult(\n", + " status, sol, mapping, e_time\n", + " )\n", + " max_layers = num_layers\n", + " else:\n", + " # If the SAT problem is unsatisfiable, return the last\n", + " # satisfiable solution.\n", + " binary_search_results[num_layers] = SATResult(\n", + " status, sol, [], e_time\n", + " )\n", + " min_layers = num_layers + 1\n", + "\n", + " return binary_search_results\n", + "\n", + " def remap_graph_with_sat(\n", + " self, graph: rx.Graph, swap_strategy, max_layers\n", + " ):\n", + " \"\"\"Applies the SAT mapping.\n", + "\n", + " Args:\n", + " graph (nx.Graph): The graph to remap.\n", + " swap_strategy (SwapStrategy): The swap strategy to use\n", + " to find the initial mapping.\n", + "\n", + " Returns:\n", + " tuple: A tuple containing the remapped graph, the edge map, and the\n", + " number of layers of the swap strategy that was used to find the\n", + " initial mapping. If no solution is found then the tuple contains\n", + " None for each element. Note the returned edge map `{k: v}` means that\n", + " node `k` in the original graph gets mapped to node `v` in the\n", + " Pauli strings.\n", + " \"\"\"\n", + " num_nodes = len(graph.nodes())\n", + " results = self.find_initial_mappings(\n", + " graph, swap_strategy, 0, max_layers\n", + " )\n", + " solutions = [k for k, v in results.items() if v.satisfiable]\n", + "\n", + " if len(solutions):\n", + " min_k = min(solutions)\n", + " edge_map = dict(results[min_k].mapping)\n", + " # Create the remapped graph\n", + " remapped_graph = rx.PyGraph()\n", + " remapped_graph.add_nodes_from(range(num_nodes))\n", + " mapping = dict(results[min_k].mapping)\n", + " for i, graph_edge in enumerate(list(graph.edge_list())):\n", + " remapped_edge = tuple(mapping[node] for node in graph_edge)\n", + " remapped_graph.add_edge(*remapped_edge, graph.edges()[i])\n", + " return remapped_graph, edge_map, min_k\n", + " else:\n", + " return None, None, None" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "e689e09e-6ca7-4154-8602-d1d954ebe80b", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "38aadfcb-aec9-4dbb-a9d3-319239eae196", - "metadata": {}, - "source": [ - "The following retrieves the best solution from the sampled bitstrings\n", - "\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Layers: 0, Status: True, Time: 0.02229800000000015\n", + "Map from old to new nodes: {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20, 21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26, 27: 27, 28: 28, 29: 29, 30: 30, 31: 31, 32: 32, 33: 33, 34: 34, 35: 35, 36: 36, 37: 37, 38: 38, 39: 39, 40: 40, 41: 41, 42: 42, 43: 43, 44: 44, 45: 45, 46: 46, 47: 47, 48: 48, 49: 49, 50: 50, 51: 51, 52: 52, 53: 53, 54: 54, 55: 55, 56: 56, 57: 57, 58: 58, 59: 59, 60: 60, 61: 61, 62: 62, 63: 63, 64: 64, 65: 65, 66: 66, 67: 67, 68: 68, 69: 69, 70: 70, 71: 71, 72: 72, 73: 73, 74: 74, 75: 75, 76: 76, 77: 77, 78: 78, 79: 79, 80: 80, 81: 81, 82: 82, 83: 83, 84: 84, 85: 85, 86: 86, 87: 87, 88: 88, 89: 89, 90: 90, 91: 91, 92: 92, 93: 93, 94: 94, 95: 95, 96: 96, 97: 97, 98: 98, 99: 99}\n", + "Min SWAP layers: 0\n" + ] }, { - "cell_type": "code", - "execution_count": 101, - "id": "7e8af29e-c99b-41f2-b6dd-2be471e1af21", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "bitstring (int): 1056839274160662489278455764297, probability: 0.00038654812524159255, objective value: -71.0\n" - ] - } - ], - "source": [ - "# sort the result_dict[iter_counts]['evaluated] by the CVaR value\n", - "sorted_result_dict = [\n", - " (k, v)\n", - " for k, v in sorted(\n", - " result_dict[iter_counts][\"evaluated\"].items(),\n", - " key=lambda item: item[1][1],\n", - " )\n", - "]\n", - "print(\n", - " f\"bitstring (int): {sorted_result_dict[0][0]}, probability: {sorted_result_dict[0][1][0]}, objective value: {sorted_result_dict[0][1][1]}\"\n", - ")" + "data": { + "text/plain": [ + "\"Output" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sm = SATMapper(timeout=10)\n", + "remapped_graph, edge_map, min_swap_layers = sm.remap_graph_with_sat(\n", + " graph=graph_100, swap_strategy=swap_strategy, max_layers=1\n", + ")\n", + "print(\"Map from old to new nodes: \", edge_map)\n", + "print(\"Min SWAP layers:\", min_swap_layers)\n", + "draw_graph(remapped_graph, node_size=200, with_labels=True, width=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ecf6e8c3-65c2-4430-8dd3-d67b8842045d", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Consider the Hamiltonian $H_C$ for the **Max-Cut** problem. Let each vertex of the graph be associated with a qubit in state $|0\\rangle$ or $|1\\rangle$, where the value denotes the set the vertex is in. The goal of the problem is to maximize the number of edges $(v_1, v_2)$ for which $v_1 = |0\\rangle$ and $v_2 = |1\\rangle$, or vice-versa. If we associate the $Z$ operator with each qubit, where\n", - "\n", - "$$\n", - " Z|0\\rangle = |0\\rangle \\qquad Z|1\\rangle = -|1\\rangle\n", - "$$\n", - "\n", - "then an edge $(v_1, v_2)$ belongs to the cut if the eigenvalue of $(Z_1|v_1\\rangle) \\cdot (Z_2|v_2\\rangle) = -1$; in other words, the qubits associated with $v_1$ and $v_2$ are different. Similarly, $(v_1, v_2)$ does not belong to the cut if the eigenvalue of $(Z_1|v_1\\rangle) \\cdot (Z_2|v_2\\rangle) = 1$" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIZIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIII', 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'IIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIZIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IZIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII'],\n", + " coeffs=[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j])\n" + ] + } + ], + "source": [ + "remapped_max_cut_paulis = build_max_cut_paulis(remapped_graph)\n", + "# define a qiskit SparsePauliOp from the list of paulis\n", + "remapped_cost_operator = SparsePauliOp.from_list(remapped_max_cut_paulis)\n", + "print(remapped_cost_operator)" + ] + }, + { + "cell_type": "markdown", + "id": "5ae531be-80eb-4acd-b84a-7d466fd872e7", + "metadata": {}, + "source": [ + "#### Build a QAOA circuit with the SWAP strategy and the SAT mapping\n", + "\n", + "We only want to apply the SWAP strategies to the cost operator layer, so we start by creating the isolated block that we will later transform and append to the final QAOA circuit.\n", + "\n", + "For this, we can use the [`QAOAAnsatz`](/docs/api/qiskit/qiskit.circuit.library.QAOAAnsatz) class from Qiskit. We input an empty circuit to the `initial_state` and `mixer_operator` fields to make sure we are building an isolated cost operator layer.\n", + "We also define the edge\\_coloring map so that RZZ gates are positioned next to SWAP gates. This strategic placement allows us to exploit CX cancellations, optimizing the circuit for better performance.\n", + "This process is executed within the `create_qaoa_swap_circuit` function." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "57d5eb53-9cda-4c38-a00b-26ed4b533bcd", + "metadata": {}, + "outputs": [], + "source": [ + "def make_meas_map(circuit: QuantumCircuit) -> dict:\n", + " \"\"\"Return a mapping from qubit index (the key) to classical bit (the value).\n", + "\n", + " This allows us to account for the swapping order introduced by the SWAP strategy.\n", + " \"\"\"\n", + " creg = circuit.cregs[0]\n", + " qreg = circuit.qregs[0]\n", + "\n", + " meas_map = {}\n", + " for inst in circuit.data:\n", + " if inst.operation.name == \"measure\":\n", + " meas_map[qreg.index(inst.qubits[0])] = creg.index(inst.clbits[0])\n", + "\n", + " return meas_map\n", + "\n", + "\n", + "def apply_swap_strategy(\n", + " circuit: QuantumCircuit,\n", + " swap_strategy: SwapStrategy,\n", + " edge_coloring: dict[tuple[int, int], int] | None = None,\n", + ") -> QuantumCircuit:\n", + " \"\"\"Transpile with a SWAP strategy.\n", + "\n", + " Returns:\n", + " A quantum circuit transpiled with the given swap strategy.\n", + " \"\"\"\n", + "\n", + " pm_pre = PassManager(\n", + " [\n", + " FindCommutingPauliEvolutions(),\n", + " Commuting2qGateRouter(\n", + " swap_strategy,\n", + " edge_coloring,\n", + " ),\n", + " ]\n", + " )\n", + " return pm_pre.run(circuit)\n", + "\n", + "\n", + "def apply_qaoa_layers(\n", + " cost_layer: QuantumCircuit,\n", + " meas_map: dict,\n", + " num_layers: int,\n", + " gamma: list[float] | ParameterVector = None,\n", + " beta: list[float] | ParameterVector = None,\n", + " initial_state: QuantumCircuit = None,\n", + " mixer: QuantumCircuit = None,\n", + "):\n", + " \"\"\"Applies QAOA layers to construct circuit.\n", + "\n", + " First, the initial state is applied. If `initial_state` is None, we begin in the\n", + " initial superposition state. Next, we alternate between layers of the cost operator\n", + " and the mixer. The cost operator is alternatively applied in order and in reverse\n", + " instruction order. This allows us to apply the swap strategy on odd `p` layers\n", + " and undo the swap strategy on even `p` layers.\n", + " \"\"\"\n", + "\n", + " num_qubits = cost_layer.num_qubits\n", + " new_circuit = QuantumCircuit(num_qubits, num_qubits)\n", + "\n", + " if initial_state is not None:\n", + " new_circuit.append(initial_state, range(num_qubits))\n", + " else:\n", + " # all h state by default\n", + " new_circuit.h(range(num_qubits))\n", + "\n", + " if gamma is None or beta is None:\n", + " gamma = ParameterVector(\"γ'\", num_layers)\n", + " if mixer is None or mixer.num_parameters == 0:\n", + " beta = ParameterVector(\"β'\", num_layers)\n", + " else:\n", + " beta = ParameterVector(\"β'\", num_layers * mixer.num_parameters)\n", + "\n", + " if mixer is not None:\n", + " mixer_layer = mixer\n", + " else:\n", + " mixer_layer = QuantumCircuit(num_qubits)\n", + " mixer_layer.rx(-2 * beta[0], range(num_qubits))\n", + "\n", + " for layer in range(num_layers):\n", + " bind_dict = {cost_layer.parameters[0]: gamma[layer]}\n", + " cost_layer_ = cost_layer.assign_parameters(bind_dict)\n", + " bind_dict = {\n", + " mixer_layer.parameters[i]: beta[layer + i]\n", + " for i in range(mixer_layer.num_parameters)\n", + " }\n", + " layer_mixer = mixer_layer.assign_parameters(bind_dict)\n", + "\n", + " if layer % 2 == 0:\n", + " new_circuit.append(cost_layer_, range(num_qubits))\n", + " else:\n", + " new_circuit.append(cost_layer_.reverse_ops(), range(num_qubits))\n", + "\n", + " new_circuit.append(layer_mixer, range(num_qubits))\n", + "\n", + " for qidx, cidx in meas_map.items():\n", + " new_circuit.measure(qidx, cidx)\n", + "\n", + " return new_circuit\n", + "\n", + "\n", + "def create_qaoa_swap_circuit(\n", + " cost_operator: SparsePauliOp,\n", + " swap_strategy: SwapStrategy,\n", + " edge_coloring: dict = None,\n", + " theta: list[float] = None,\n", + " qaoa_layers: int = 1,\n", + " initial_state: QuantumCircuit = None,\n", + " mixer: QuantumCircuit = None,\n", + "):\n", + " \"\"\"Create the circuit for QAOA.\n", + "\n", + " Notes: This circuit construction for QAOA works for quadratic terms in `Z` and will be\n", + " extended to first-order terms in `Z`. Higher-orders are not supported.\n", + "\n", + " Args:\n", + " cost_operator: the cost operator.\n", + " swap_strategy: selected swap strategy\n", + " edge_coloring: A coloring of edges that should correspond to the coupling\n", + " map of the hardware. It defines the order in which we apply the Rzz\n", + " gates. This allows us to choose an ordering such that `Rzz` gates will\n", + " immediately precede SWAP gates to leverage CNOT cancellation.\n", + " theta: The QAOA angles.\n", + " qaoa_layers: The number of layers of the cost operator and the mixer operator.\n", + " initial_state: The initial state on which we apply layers of cost operator\n", + " and mixer.\n", + " mixer: The QAOA mixer. It will be applied as is onto the QAOA circuit. Therefore,\n", + " its output must have the same ordering of qubits as its input.\n", + " \"\"\"\n", + "\n", + " num_qubits = cost_operator.num_qubits\n", + "\n", + " if theta is not None:\n", + " gamma = theta[: len(theta) // 2]\n", + " beta = theta[len(theta) // 2 :]\n", + " qaoa_layers = len(theta) // 2\n", + " else:\n", + " gamma = beta = None\n", + "\n", + " # First, create the ansatz of 1 layer of QAOA without mixer\n", + " cost_layer = QAOAAnsatz(\n", + " cost_operator,\n", + " reps=1,\n", + " initial_state=QuantumCircuit(num_qubits),\n", + " mixer_operator=QuantumCircuit(num_qubits),\n", + " ).decompose()\n", + "\n", + " # This will allow us to recover the permutation of the measurements that the swaps introduce.\n", + " cost_layer.measure_all()\n", + "\n", + " # Now, apply the swap strategy for commuting gates\n", + " cost_layer = apply_swap_strategy(cost_layer, swap_strategy, edge_coloring)\n", + "\n", + " # Compute the measurement map (qubit to classical bit).\n", + " # We will apply this for odd layers where the swaps were inserted.\n", + " if qaoa_layers % 2 == 1:\n", + " meas_map = make_meas_map(cost_layer)\n", + " else:\n", + " meas_map = {idx: idx for idx in range(num_qubits)}\n", + "\n", + " cost_layer.remove_final_measurements()\n", + "\n", + " # Finally, introduce the mixer circuit and add measurements following measurement map\n", + " circuit = apply_qaoa_layers(\n", + " cost_layer, meas_map, qaoa_layers, gamma, beta, initial_state, mixer\n", + " )\n", + "\n", + " return circuit" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "7793ef92-ce59-4fd7-b43f-48d4e3427e3a", + "metadata": {}, + "outputs": [], + "source": [ + "# We can define the edge_coloring map so that RZZ gates are positioned right before SWAP gates to exploit CX cancellations\n", + "# We use greedy edge coloring from rustworkx to color the edges of the graph. This coloring is used to order the RZZ gates in the circuit.\n", + "\n", + "edge_coloring_idx = rx.graph_greedy_edge_color(graph_100)\n", + "edge_coloring = {\n", + " edge: edge_coloring_idx[idx]\n", + " for idx, edge in enumerate(list(graph_100.edge_list()))\n", + "}\n", + "edge_coloring = {tuple(sorted(k)): v for k, v in edge_coloring.items()}" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "82ae28b3-85eb-4487-8100-1e622e93cccf", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 102, - "id": "5ea9e6aa-4297-4687-b484-1695d415bad5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result bitstring (binary) : [1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1]\n", - "The value of the cut is: 91\n" - ] - } - ], - "source": [ - "from typing import Sequence\n", - "\n", - "def to_bitstring(integer, num_bits):\n", - " result = np.binary_repr(integer, width=num_bits)\n", - " return [int(digit) for digit in result]\n", - "\n", - "\n", - "def evaluate_sample(x: Sequence[int], graph: rx.PyGraph) -> float:\n", - " assert len(x) == len(\n", - " list(graph.nodes())\n", - " ), \"The length of x must coincide with the number of nodes in the graph.\"\n", - " return sum(\n", - " x[u] * (1 - x[v]) + x[v] * (1 - x[u]) # x[u] = x[v] if same cut, x[u] \\neq x[v] if different cuts\n", - " for u, v in list(graph.edge_list())\n", - " )\n", - "\n", - "\n", - "bitstring = to_bitstring(\n", - " sorted_result_dict[0][0], len(list(remapped_graph.nodes()))\n", - ")\n", - "bitstring = bitstring[::-1]\n", - "print(f\"Result bitstring (binary) : {bitstring}\")\n", - "\n", - "cut_value = evaluate_sample(bitstring, remapped_graph)\n", - "print(f\"The value of the cut is: {cut_value}\")" + "data": { + "text/plain": [ + "\"Output" ] - }, + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qaoa_circ = create_qaoa_swap_circuit(\n", + " remapped_cost_operator,\n", + " swap_strategy,\n", + " edge_coloring=edge_coloring,\n", + " qaoa_layers=1,\n", + ")\n", + "qaoa_circ.draw(output=\"mpl\", fold=False)" + ] + }, + { + "cell_type": "markdown", + "id": "e2afd1a7-0980-433b-a3a8-303d7e7718b1", + "metadata": {}, + "source": [ + "## Step 3: Execute using Qiskit primitives\n", + "\n", + "### Define a CVaR cost function\n", + "\n", + "This example shows how to use the Conditional Value at Risk (CVaR) cost function introduced in [\\[3\\]](#references) within the variational quantum optimization algorithms.\n", + "\n", + "The CVaR of a random variable $X$ for a confidence level $α ∈ (0, 1]$ is defined as\n", + "$CVaR_{\\alpha}(X) = \\mathbb{E} \\lbrack X | X \\leq F_X^{-1}(\\alpha) \\rbrack$\n", + "where $F_X^{-1}(p)$ is the inverse cumulative distribution function of $X$. In other words, CVaR is the expected value of the lower $\\alpha$-tail of the distribution of $X$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "172023f9-164e-43bd-bbc8-39d87628287e", + "metadata": {}, + "outputs": [], + "source": [ + "pass_manager = generate_preset_pass_manager(\n", + " backend=backend,\n", + " optimization_level=3,\n", + ")\n", + "\n", + "transpiled_qaoa_circ = pass_manager.run(qaoa_circ)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6794cf3-7fbe-46a5-bdc0-5faad1235365", + "metadata": {}, + "outputs": [], + "source": [ + "# Utility functions for the evaluation of the expectation value of a measured state\n", + "# In this code, for optimization, the measured state is converted into a bit string,\n", + "# and the sign of the value is determined by taking the exclusive OR of the bits\n", + "# corresponding to Pauli Z.\n", + "\n", + "_PARITY = np.array(\n", + " [-1 if bin(i).count(\"1\") % 2 else 1 for i in range(256)],\n", + " dtype=np.complex128,\n", + ")\n", + "\n", + "\n", + "def evaluate_sparse_pauli(state: int, observable: SparsePauliOp) -> complex:\n", + " \"\"\"Utility for the evaluation of the expectation value of a measured state.\n", + "\n", + " Args:\n", + " state (int): The measured state.\n", + " observable (SparsePauliOp): The observable to evaluate the expectation value for.\n", + "\n", + " Returns:\n", + " complex: The expectation value of the measured state.\n", + " \"\"\"\n", + " packed_uint8 = np.packbits(\n", + " observable.paulis.z, axis=1, bitorder=\"little\"\n", + " ) # convert observable to array with 8 bit integer\n", + " state_bytes = np.frombuffer(\n", + " state.to_bytes(packed_uint8.shape[1], \"little\"),\n", + " dtype=np.uint8, # convert bitstring to array with 8 bit integer\n", + " )\n", + " reduced = np.bitwise_xor.reduce(\n", + " packed_uint8 & state_bytes, axis=1\n", + " ) # take bitwise xor of the result of 'and' conditional on the above two, return 0 or 1\n", + " return np.sum(observable.coeffs * _PARITY[reduced])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c5e5f7a4-01f6-4a02-9114-3bb0e24be1a2", + "metadata": {}, + "outputs": [], + "source": [ + "def qaoa_sampler_cost_fun(\n", + " params, ansatz, hamiltonian, sampler, aggregation=None\n", + "):\n", + " \"\"\"Standard sampler-based QAOA cost function to be plugged into optimizer routines.\n", + "\n", + " Args:\n", + " params (np.ndarray): Parameters for the ansatz.\n", + " ansatz (QuantumCircuit): Ansatz circuit.\n", + " hamiltonian (SparsePauliOp): Hamiltonian to be minimized.\n", + " sampler (QAOASampler): Sampler to be used.\n", + " aggregation (Callable | float | None): Aggregation function to be applied to\n", + " the sampled results. If None, the sum of the expectation values is returned.\n", + " If float, the CVaR with the given alpha is used.\n", + " \"\"\"\n", + " # Run the circuit\n", + " job = sampler.run([(ansatz, params)])\n", + " sampler_result = job.result()\n", + " sampled_int_counts = sampler_result[\n", + " 0\n", + " ].data.c.get_int_counts() # bitstrings are stored as integers\n", + " shots = sum(sampled_int_counts.values())\n", + " int_count_distribution = {\n", + " key: val / shots for key, val in sampled_int_counts.items()\n", + " }\n", + "\n", + " # a dictionary containing: {state: (measurement probability, value)}\n", + " evaluated = {\n", + " state: (\n", + " probability,\n", + " np.real(evaluate_sparse_pauli(state, hamiltonian)),\n", + " )\n", + " for state, probability in int_count_distribution.items()\n", + " }\n", + "\n", + " # If aggregation is None, return the sum of the expectation values.\n", + " # If aggregation is a float, return the CVaR with the given alpha.\n", + " # Otherwise, use the aggregation function.\n", + " if aggregation is None:\n", + " result = sum(\n", + " probability * value for probability, value in evaluated.values()\n", + " )\n", + " elif isinstance(aggregation, float):\n", + " cvar_aggregation = _get_cvar_aggregation(aggregation)\n", + " result = cvar_aggregation(evaluated.values())\n", + " else:\n", + " result = aggregation(evaluated.values())\n", + "\n", + " global iter_counts, result_dict\n", + " iter_counts += 1\n", + " temp_dict = {}\n", + " temp_dict[\"params\"] = params.tolist()\n", + " temp_dict[\"cvar_fval\"] = result\n", + " temp_dict[\"fval\"] = sum(\n", + " probability * value for probability, value in evaluated.values()\n", + " )\n", + " temp_dict[\"distribution\"] = sampled_int_counts\n", + " temp_dict[\"evaluated\"] = evaluated\n", + " result_dict[iter_counts] = temp_dict\n", + " print(f\"Iteration {iter_counts}: {result}\")\n", + "\n", + " return result\n", + "\n", + "\n", + "def _get_cvar_aggregation(alpha: float | None) -> Callable:\n", + " \"\"\"Return the CVaR aggregation function with the given alpha.\n", + "\n", + " Args:\n", + " alpha (float | None): Alpha value for the CVaR aggregation. If None, 1 is used\n", + " by default.\n", + " Raises:\n", + " ValueError: If alpha is not in [0, 1].\n", + " \"\"\"\n", + " if alpha is None:\n", + " alpha = 1\n", + " elif not 0 <= alpha <= 1:\n", + " raise ValueError(f\"alpha must be in [0, 1], but {alpha} was given.\")\n", + "\n", + " def cvar_aggregation(\n", + " objective_dict: Iterable[tuple[float, float]],\n", + " ) -> float:\n", + " \"\"\"Return the CVaR of the given measurements.\n", + " Args:\n", + " objective_dict (Iterable[tuple[float, float]]): An iterable of tuples containing\n", + " the measured bit string and the objective value based on the bit string.\n", + "\n", + " \"\"\"\n", + " sorted_measurements = sorted(objective_dict, key=lambda x: x[1])\n", + " # accumulate the probabilities until alpha is reached\n", + " accumulated_percent = 0.0\n", + " cvar = 0.0\n", + " for probability, value in sorted_measurements:\n", + " cvar += value * min(probability, alpha - accumulated_percent)\n", + " accumulated_percent += probability\n", + " if accumulated_percent >= alpha:\n", + " break\n", + " return cvar / alpha\n", + "\n", + " return cvar_aggregation" + ] + }, + { + "cell_type": "markdown", + "id": "63fa2ab4-5354-4022-ab46-e9bbf73870de", + "metadata": {}, + "source": [ + "The CVaR can be used as an error mitigation technique as previously discussed [\\[4\\]](#references). In this example, we determine $\\alpha$ and the number of shots according to the circuit's error rate." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "032bf312-4bf4-40f4-81f0-2ae8a719b98b", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "c5879546-35ab-4876-bed9-262b85f130cc", - "metadata": {}, - "source": [ - "Finally, let's draw a graph based on the CVaR result.\n", - "We split the graph nodes into two sets based on the CVaR result.\n", - "The nodes in the first set are colored in gray, and the nodes in the second set are colored in purple.\n", - "The edges between the two sets are the edges that are cut by the partitioning.\n", - "\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "layer fidelity 0.6544189744623636\n", + "\n", + "The corresponding CVaR aggregation value is: 0.38642994025160377\n", + "To mitigate the twirled noise, increase shots by a factor of 2.5877912031063173\n" + ] + } + ], + "source": [ + "num_2q_ops = transpiled_qaoa_circ.count_ops()[\n", + " \"cz\"\n", + "] # the two qubit gates on our backend are cz's.\n", + "\n", + "for el in backend.properties().general:\n", + " if el.name[:2] == \"lf\" and el.name[3:] == str(\n", + " n\n", + " ): # pick out lf_100, lf of the best 100q chain\n", + " lf = el.value # layer fidelity\n", + " print(\"layer fidelity\", lf)\n", + " eplg = 1 - lf ** (1 / (n - 1)) # error per layered gate (EPLG)\n", + " fid_cz = 1 - eplg\n", + " gamma_cz = 1 / fid_cz**2\n", + " gamma_circ = gamma_cz**num_2q_ops\n", + "\n", + "cvar_aggregation = 1 / np.sqrt(gamma_circ)\n", + "print(\"\")\n", + "print(\"The corresponding CVaR aggregation value is: \", cvar_aggregation)\n", + "print(\n", + " \"To mitigate the twirled noise, increase shots by a factor of\",\n", + " np.sqrt(gamma_circ),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "382e8acd-d0d0-4302-99aa-b64e5dd31e17", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 103, - "id": "852dfeed-2871-4ca1-9754-15c95293198e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_result(G, x):\n", - " colors = [\"tab:grey\" if i == 0 else \"tab:purple\" for i in x]\n", - " pos, _default_axes = rx.spring_layout(G), plt.axes(frameon=True)\n", - " rx.visualization.mpl_draw(\n", - " G, node_color=colors, node_size=100, alpha=0.8, pos=pos\n", - " )\n", - "\n", - "\n", - "plot_result(graph_100, to_bitstring(sorted_result_dict[0][0], 100))" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1: -10.010143313246703\n", + "Iteration 2: -10.778378196890142\n", + "Iteration 3: -33.11648157016098\n", + "Iteration 4: 23.37766951385026\n", + "Iteration 5: -8.456279717029192\n", + "Iteration 6: -20.250216714385534\n", + "Iteration 7: -45.87610233094513\n", + "Iteration 8: -20.4522784936772\n", + "Iteration 9: -49.63202770645467\n", + "Iteration 10: -49.10586683562599\n", + "Iteration 11: -49.15188090417742\n", + "Iteration 12: -45.84209193245048\n", + "Iteration 13: -49.634028318131016\n", + "Iteration 14: -48.542306019104984\n", + "Iteration 15: -49.8580968258604\n", + "Iteration 16: -49.7000485034442\n", + " message: Return from COBYLA because the trust region radius reaches its lower bound.\n", + " success: True\n", + " status: 0\n", + " fun: -49.8580968258604\n", + " x: [ 4.262e+00 2.820e+00]\n", + " nfev: 16\n", + " maxcv: 0.0\n" + ] + } + ], + "source": [ + "iter_counts = 0\n", + "result_dict = {}\n", + "init_params = [np.pi, np.pi / 2]\n", + "\n", + "with Session(backend=backend) as session:\n", + " sampler = Sampler(mode=session)\n", + " sampler.options.default_shots = int(1000 / cvar_aggregation)\n", + " sampler.options.dynamical_decoupling.enable = True\n", + " sampler.options.dynamical_decoupling.sequence_type = \"XY4\"\n", + " sampler.options.twirling.enable_gates = True\n", + " sampler.options.twirling.enable_measure = True\n", + "\n", + " result = minimize(\n", + " qaoa_sampler_cost_fun,\n", + " init_params,\n", + " args=(\n", + " transpiled_qaoa_circ,\n", + " remapped_cost_operator,\n", + " sampler,\n", + " cvar_aggregation,\n", + " ),\n", + " method=\"COBYLA\",\n", + " tol=1e-2,\n", + " )\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "id": "1d190fa4-3bbe-412a-b296-6dddd3ad2b12", + "metadata": {}, + "source": [ + "## Step 4: Post-process and return result in desired classical format" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "761821cb-9a0c-4efb-806b-75513302d34a", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "82f5c13b-a141-4657-adfd-bb18e88ad9f2", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "\\[1] Weidenfeller, J., Valor, L. C., Gacon, J., Tornow, C., Bello, L., Woerner, S., & Egger, D. J. (2022). Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware. Quantum, 6, 870.\n", - "\n", - "\\[2] Matsuo, A., Yamashita, S., & Egger, D. J. (2023). A SAT approach to the initial mapping problem in SWAP gate insertion for commuting gates. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 106(11), 1424-1431.\n", - "\n", - "\\[3] Barkoutsos, P. K., Nannicini, G., Robert, A., Tavernelli, I., & Woerner, S. (2020). Improving variational quantum optimization using CVaR. Quantum, 4, 256.\n", - "\n", - "\\[4] Barron, S. V., Egger, D. J., Pelofske, E., Bärtschi, A., Eidenbenz, S., Lehmkuehler, M., & Woerner, S. (2023). Provable bounds for noise-free expectation values computed from noisy samples. arXiv preprint arXiv:2312.00733.\n", - "\n" + "data": { + "text/plain": [ + "\"Output" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(\n", + " [result_dict[i][\"cvar_fval\"] for i in range(1, iter_counts + 1)],\n", + " label=\"CVaR\",\n", + ")\n", + "plt.plot(\n", + " [result_dict[i][\"fval\"] for i in range(1, iter_counts + 1)],\n", + " label=\"Standard\",\n", + ")\n", + "plt.legend()\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"Cost\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "38aadfcb-aec9-4dbb-a9d3-319239eae196", + "metadata": {}, + "source": [ + "The following retrieves the best solution from the sampled bitstrings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e8af29e-c99b-41f2-b6dd-2be471e1af21", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "c048cd84-d6e8-4b8a-926e-a7f7724a86e2", - "metadata": {}, - "source": [ - "## Tutorial survey\n", - "\n", - "Please take one minute to provide feedback on this tutorial. Your insights will help us improve our content offerings and user experience.\n", - "\n", - "[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_cZwpScxyXVDpIeq)\n", - "\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "bitstring (int): 1056839274160662489278455764297, probability: 0.00038654812524159255, objective value: -71.0\n" + ] + } + ], + "source": [ + "# sort the result_dict[iter_counts]['evaluated'] by the CVaR value\n", + "sorted_result_dict = [\n", + " (k, v)\n", + " for k, v in sorted(\n", + " result_dict[iter_counts][\"evaluated\"].items(),\n", + " key=lambda item: item[1][1],\n", + " )\n", + "]\n", + "print(\n", + " f\"bitstring (int): {sorted_result_dict[0][0]}, probability: {sorted_result_dict[0][1][0]}, objective value: {sorted_result_dict[0][1][1]}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9cbeacc1-ff99-4b3d-b38d-b293a19642e2", + "metadata": {}, + "source": [ + "Consider the Hamiltonian $H_C$ for the **Max-Cut** problem. Let each vertex of the graph be associated with a qubit in state $|0\\rangle$ or $|1\\rangle$, where the value denotes the set the vertex is in. The goal of the problem is to maximize the number of edges $(v_1, v_2)$ for which $v_1 = |0\\rangle$ and $v_2 = |1\\rangle$, or vice-versa. If we associate the $Z$ operator with each qubit, where\n", + "\n", + "$$\n", + " Z|0\\rangle = |0\\rangle \\qquad Z|1\\rangle = -|1\\rangle\n", + "$$\n", + "\n", + "then an edge $(v_1, v_2)$ belongs to the cut if the eigenvalue of $(Z_1|v_1\\rangle) \\cdot (Z_2|v_2\\rangle) = -1$; in other words, the qubits associated with $v_1$ and $v_2$ are different. Similarly, $(v_1, v_2)$ does not belong to the cut if the eigenvalue of $(Z_1|v_1\\rangle) \\cdot (Z_2|v_2\\rangle) = 1$" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "5ea9e6aa-4297-4687-b484-1695d415bad5", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "d325feb7-b9c5-4e6a-b1ee-93e5f96213cb", - "metadata": {}, - "source": [ - "© IBM Corp. 2025\n", - "\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Result bitstring (binary) : [1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1]\n", + "The value of the cut is: 91\n" + ] + } + ], + "source": [ + "from typing import Sequence\n", + "\n", + "\n", + "def to_bitstring(integer, num_bits):\n", + " result = np.binary_repr(integer, width=num_bits)\n", + " return [int(digit) for digit in result]\n", + "\n", + "\n", + "def evaluate_sample(x: Sequence[int], graph: rx.PyGraph) -> float:\n", + " assert len(x) == len(\n", + " list(graph.nodes())\n", + " ), \"The length of x must coincide with the number of nodes in the graph.\"\n", + " return sum(\n", + " x[u] * (1 - x[v])\n", + " + x[v]\n", + " * (\n", + " 1 - x[u]\n", + " ) # x[u] = x[v] if same cut, x[u] \\neq x[v] if different cuts\n", + " for u, v in list(graph.edge_list())\n", + " )\n", + "\n", + "\n", + "bitstring = to_bitstring(\n", + " sorted_result_dict[0][0], len(list(remapped_graph.nodes()))\n", + ")\n", + "bitstring = bitstring[::-1]\n", + "print(f\"Result bitstring (binary) : {bitstring}\")\n", + "\n", + "cut_value = evaluate_sample(bitstring, remapped_graph)\n", + "print(f\"The value of the cut is: {cut_value}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c5879546-35ab-4876-bed9-262b85f130cc", + "metadata": {}, + "source": [ + "Finally, let's draw a graph based on the CVaR result.\n", + "We split the graph nodes into two sets based on the CVaR result.\n", + "The nodes in the first set are colored in gray, and the nodes in the second set are colored in purple.\n", + "The edges between the two sets are the edges that are cut by the partitioning." + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "852dfeed-2871-4ca1-9754-15c95293198e", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "a1b8767d", - "metadata": {}, - "source": [ - "© IBM Corp., 2017-2025" + "data": { + "text/plain": [ + "\"Output" ] + }, + "metadata": {}, + "output_type": "display_data" } - ], - "metadata": { - "description": "This notebook introduces advanced techniques to improve the performance of the Quantum Approximate Optimization Algorithm (QAOA) with a large number of qubits.", - "kernelspec": { - "display_name": "tut", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.3" - }, - "title": "Advanced techniques for QAOA" + ], + "source": [ + "def plot_result(G, x):\n", + " colors = [\"tab:grey\" if i == 0 else \"tab:purple\" for i in x]\n", + " pos, _default_axes = rx.spring_layout(G), plt.axes(frameon=True)\n", + " rx.visualization.mpl_draw(\n", + " G, node_color=colors, node_size=100, alpha=0.8, pos=pos\n", + " )\n", + "\n", + "\n", + "plot_result(graph_100, to_bitstring(sorted_result_dict[0][0], 100))" + ] + }, + { + "cell_type": "markdown", + "id": "82f5c13b-a141-4657-adfd-bb18e88ad9f2", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "\\[1] Weidenfeller, J., Valor, L. C., Gacon, J., Tornow, C., Bello, L., Woerner, S., & Egger, D. J. (2022). Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware. Quantum, 6, 870.\n", + "\n", + "\\[2] Matsuo, A., Yamashita, S., & Egger, D. J. (2023). A SAT approach to the initial mapping problem in SWAP gate insertion for commuting gates. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 106(11), 1424-1431.\n", + "\n", + "\\[3] Barkoutsos, P. K., Nannicini, G., Robert, A., Tavernelli, I., & Woerner, S. (2020). Improving variational quantum optimization using CVaR. Quantum, 4, 256.\n", + "\n", + "\\[4] Barron, S. V., Egger, D. J., Pelofske, E., Bärtschi, A., Eidenbenz, S., Lehmkuehler, M., & Woerner, S. (2023). Provable bounds for noise-free expectation values computed from noisy samples. arXiv preprint arXiv:2312.00733." + ] + }, + { + "cell_type": "markdown", + "id": "c048cd84-d6e8-4b8a-926e-a7f7724a86e2", + "metadata": {}, + "source": [ + "## Tutorial survey\n", + "\n", + "Please take one minute to provide feedback on this tutorial. Your insights will help us improve our content offerings and user experience.\n", + "\n", + "[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_cZwpScxyXVDpIeq)" + ] + }, + { + "cell_type": "markdown", + "id": "d325feb7-b9c5-4e6a-b1ee-93e5f96213cb", + "metadata": {}, + "source": [ + "© IBM Corp. 2025" + ] + }, + { + "cell_type": "markdown", + "id": "a1b8767d", + "metadata": {}, + "source": [ + "© IBM Corp., 2017-2025" + ] + } + ], + "metadata": { + "description": "This notebook introduces advanced techniques to improve the performance of the Quantum Approximate Optimization Algorithm (QAOA) with a large number of qubits.", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" }, - "nbformat": 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docs/tutorials/advanced-techniques-for-qaoa.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 0e144520cba..15307ab77c3 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -7,7 +7,7 @@ "source": [ "# Advanced techniques for QAOA\n", "\n", - "*Usage estimate: 3 minutes on IBM Kingston (NOTE: This is an estimate only. Your runtime may vary.)*" + "*Usage estimate: 3 minutes on a Heron r2 (NOTE: This is an estimate only. Your runtime may vary.)*" ] }, { From 46de5857d060e092ecc2bad81278d8ae0cf5f3a1 Mon Sep 17 00:00:00 2001 From: Ibrahim Shehzad <75153717+ibrahim-shehzad@users.noreply.github.com> Date: Wed, 26 Nov 2025 14:50:13 -0500 Subject: [PATCH 05/28] update comment on reference. Co-authored-by: abbycross --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 15307ab77c3..4e6c50d006e 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -18,7 +18,7 @@ "## Background\n", "\n", "This notebook introduces advanced techniques to improve the performance of the **Quantum Approximate Optimization Algorithm (QAOA)** with a large number of qubits.\n", - "Please see [Solve utility-scale quantum optimization problems](/docs/tutorials/quantum-approximate-optimization-algorithm) for an introduction to QAOA.\n", + "See the [Solve utility-scale quantum optimization problems](/docs/tutorials/quantum-approximate-optimization-algorithm) tutorial for an introduction to QAOA.\n", "\n", "The advanced techniques in this notebook include:\n", "\n", From ba2ef431caaf73411336755c6a13823bdd944c70 Mon Sep 17 00:00:00 2001 From: Ibrahim Shehzad <75153717+ibrahim-shehzad@users.noreply.github.com> Date: Wed, 26 Nov 2025 14:50:40 -0500 Subject: [PATCH 06/28] update background blurb Co-authored-by: abbycross --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 4e6c50d006e..3ce7a23d7b3 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -22,9 +22,9 @@ "\n", "The advanced techniques in this notebook include:\n", "\n", - "* **SWAP strategy with SAT initial mapping**: This is a specifically designed transpiler pass for QAOA that uses a SWAP strategy and a SAT solver together to improve the selection of which physical qubits on the QPU to use. The SWAP strategy exploits the commutativity of the QAOA operators to reorder gates so that layers of SWAP gates can be simultaneously executed, thus reducing the depth of the circuit [\\[1\\]](#references). The SAT solver is used to find an initial mapping that minimizes the number of SWAP operations needed to map the qubits in the circuit to the physical qubits on the device [\\[2\\]](#references) .\n", - "* **CVaR cost function**: Typically the expected value of the cost Hamiltonian is used as the cost function for QAOA, but as was shown in [\\[3\\]](#references) , focusing on the tail of the distribution, rather than the expected value, can improve the performance of QAOA for combinatorial optimization problems. The CVaR accomplishes this. For a given set of shots with corresponding objective values of the considered optimization problem, the Conditional Value at Risk (CVaR) with confidence level $\\alpha \\in [0, 1]$ is defined as the average of the $\\alpha$ best shots [\\[3\\]](#references).\n", - " Thus, $\\alpha = 1$ corresponds to the standard expected value, while $\\alpha=0$ corresponds to the minimum of the given shots, and $\\alpha \\in (0, 1)$ is a tradeoff between focusing on better shots, while still applying some averaging to smooth out the optimization landscape. Additionally, the CVaR can be used as an error mitigation technique to improve the quality of the objective value estimation [\\[4\\]](#references)." + "* **SWAP strategy with SAT initial mapping**: This is a specifically designed transpiler pass for QAOA that uses a SWAP strategy and a SAT solver together to improve the selection of which physical qubits on the QPU to use. The SWAP strategy exploits the commutativity of the QAOA operators to reorder gates so that layers of SWAP gates can be simultaneously executed, thus reducing the depth of the circuit [\\[1\\]](#references). The SAT solver is used to find an initial mapping that minimizes the number of SWAP operations needed to map the qubits in the circuit to the physical qubits on the device [\\[2\\]](#references) .\n", + "* **CVaR cost function**: Typically the expected value of the cost Hamiltonian is used as the cost function for QAOA, but as was shown in [\\[3\\]](#references) , focusing on the tail of the distribution, rather than the expected value, can improve the performance of QAOA for combinatorial optimization problems. The CVaR accomplishes this. For a given set of shots with corresponding objective values of the considered optimization problem, the Conditional Value at Risk (CVaR) with confidence level $\\alpha \\in [0, 1]$ is defined as the average of the $\\alpha$ best shots [\\[3\\]](#references).\n", + "Thus, $\\alpha = 1$ corresponds to the standard expected value, while $\\alpha=0$ corresponds to the minimum of the given shots, and $\\alpha \\in (0, 1)$ is a tradeoff between focusing on better shots, while still applying some averaging to smooth out the optimization landscape. Additionally, the CVaR can be used as an error mitigation technique to improve the quality of the objective value estimation [\\[4\\]](#references)." ] }, { From 8994c75b9485e68a334370079b1751d021b53561 Mon Sep 17 00:00:00 2001 From: Ibrahim Shehzad <75153717+ibrahim-shehzad@users.noreply.github.com> Date: Wed, 26 Nov 2025 14:51:19 -0500 Subject: [PATCH 07/28] update requirements blurb. Co-authored-by: abbycross --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 3ce7a23d7b3..d1115d5c9c1 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -36,10 +36,10 @@ "\n", "Before starting this tutorial, be sure you have the following installed:\n", "\n", - "* Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", - "* Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\n", - "* Rustworkx graph library (`pip install rustworkx`)\n", - "* Python SAT (`pip install python-sat`)" + "* Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", + "* Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\n", + "* Rustworkx graph library (`pip install rustworkx`)\n", + "* Python SAT (`pip install python-sat`)" ] }, { From 36a466f43831343b17d96b29d688a7f2652c971c Mon Sep 17 00:00:00 2001 From: Ibrahim Shehzad <75153717+ibrahim-shehzad@users.noreply.github.com> Date: Wed, 26 Nov 2025 14:51:59 -0500 Subject: [PATCH 08/28] fix markdown typo Co-authored-by: abbycross --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index d1115d5c9c1..44aae6954e4 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -104,7 +104,7 @@ "source": [ "## Step 1: Map classical inputs to a quantum problem\n", "\n", - "### Graph → Hamiltonian\n", + "### Graph → Hamiltonian\n", "\n", "First, map the problem onto a quantum circuit that is suited for the QAOA. Details on this process can be found in the [introductory QAOA tutorial.](/docs/tutorials/quantum-approximate-optimization-algorithm)" ] From 83785e58d1ecf498ea9dd636d8979fe5430f3f5c Mon Sep 17 00:00:00 2001 From: Ibrahim Shehzad <75153717+ibrahim-shehzad@users.noreply.github.com> Date: Wed, 26 Nov 2025 14:52:29 -0500 Subject: [PATCH 09/28] Update markdown in step 1. Co-authored-by: abbycross --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 44aae6954e4..2b5882a181c 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -90,7 +90,7 @@ "id": "8b77b0c9-f5a6-476e-86b8-069ba14f9ab3", "metadata": {}, "source": [ - "### Max-Cut Problem\n", + "### Max-Cut problem\n", "\n", "Let's consider solving the **Max-Cut** problem on a graph with 100 nodes using QAOA.\n", "The Max-Cut problem is a combinatorial optimization problem that is defined on a graph $G = (V, E)$, where $V$ is the set of vertices and $E$ is the set of edges. The goal is to partition the vertices into two sets, $S$ and $V \\setminus S$, such that the number of edges between the two sets is maximized.\n", From c90dd4d82f04182f51cb9e9143641e20968f6716 Mon Sep 17 00:00:00 2001 From: Ibrahim Shehzad <75153717+ibrahim-shehzad@users.noreply.github.com> Date: Wed, 26 Nov 2025 14:53:00 -0500 Subject: [PATCH 10/28] Correct comment punctuation. Co-authored-by: abbycross --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 2b5882a181c..50c4de90403 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -250,7 +250,7 @@ "\n", "We will demonstrate how to build and optimize QAOA circuits using the **SWAP strategy with SAT initial mapping**, a specifically designed transpiler pass for QAOA applied to quadratic problems.\n", "\n", - "In this example, we choose a SWAP insertion strategy for blocks of commuting two-qubit gates, which applies layers of SWAP gates that are simultaneously executable on the coupling map. This strategy is presented in [\\[1\\]](#references) and is passed into `Commuting2qGateRouter` which is exposed as a standardized Qiskit transpiler pass (see [`Commuting2qGateRouter`](/docs/api/qiskit/qiskit.transpiler.passes.Commuting2qGateRouter)). We use a line swap strategy in this example." + "In this example, we choose a SWAP insertion strategy for blocks of commuting two-qubit gates, which applies layers of SWAP gates that are simultaneously executable on the coupling map. This strategy is presented in [\\[1\\]](#references) and is passed into `Commuting2qGateRouter`, which is exposed as a standardized Qiskit transpiler pass (see [`Commuting2qGateRouter`](/docs/api/qiskit/qiskit.transpiler.passes.Commuting2qGateRouter)). We use a line swap strategy in this example." ] }, { From 9b5cf1abbdc5cfa6edc594e0c3d8dc24cc7d6864 Mon Sep 17 00:00:00 2001 From: Ibrahim Shehzad <75153717+ibrahim-shehzad@users.noreply.github.com> Date: Wed, 26 Nov 2025 14:53:32 -0500 Subject: [PATCH 11/28] Correct spacing in comment Co-authored-by: abbycross --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 50c4de90403..efc5ef644bc 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -600,7 +600,7 @@ "We only want to apply the SWAP strategies to the cost operator layer, so we start by creating the isolated block that we will later transform and append to the final QAOA circuit.\n", "\n", "For this, we can use the [`QAOAAnsatz`](/docs/api/qiskit/qiskit.circuit.library.QAOAAnsatz) class from Qiskit. We input an empty circuit to the `initial_state` and `mixer_operator` fields to make sure we are building an isolated cost operator layer.\n", - "We also define the edge\\_coloring map so that RZZ gates are positioned next to SWAP gates. This strategic placement allows us to exploit CX cancellations, optimizing the circuit for better performance.\n", + "We also define the edge_coloring map so that RZZ gates are positioned next to SWAP gates. This strategic placement allows us to exploit CX cancellations, optimizing the circuit for better performance.\n", "This process is executed within the `create_qaoa_swap_circuit` function." ] }, From 40b98d6674cae415b1a76f6b0b5efe337deaca06 Mon Sep 17 00:00:00 2001 From: Ibrahim Shehzad <75153717+ibrahim-shehzad@users.noreply.github.com> Date: Wed, 26 Nov 2025 14:53:57 -0500 Subject: [PATCH 12/28] Update comment Co-authored-by: abbycross --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index efc5ef644bc..1627954ff6d 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -750,7 +750,7 @@ " else:\n", " gamma = beta = None\n", "\n", - " # First, create the ansatz of 1 layer of QAOA without mixer\n", + " # First, create the ansatz of one layer of QAOA without mixer\n", " cost_layer = QAOAAnsatz(\n", " cost_operator,\n", " reps=1,\n", From 643933e877614908352e9a412b795fa3aa76c2b5 Mon Sep 17 00:00:00 2001 From: Ibrahim Shehzad <75153717+ibrahim-shehzad@users.noreply.github.com> Date: Wed, 26 Nov 2025 14:54:18 -0500 Subject: [PATCH 13/28] Edit comment Co-authored-by: abbycross --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 1627954ff6d..3f5aa410162 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -1202,7 +1202,7 @@ "id": "9cbeacc1-ff99-4b3d-b38d-b293a19642e2", "metadata": {}, "source": [ - "Consider the Hamiltonian $H_C$ for the **Max-Cut** problem. Let each vertex of the graph be associated with a qubit in state $|0\\rangle$ or $|1\\rangle$, where the value denotes the set the vertex is in. The goal of the problem is to maximize the number of edges $(v_1, v_2)$ for which $v_1 = |0\\rangle$ and $v_2 = |1\\rangle$, or vice-versa. If we associate the $Z$ operator with each qubit, where\n", + "Consider the Hamiltonian $H_C$ for the **Max-Cut** problem. Let each vertex of the graph be associated with a qubit in state $|0\\rangle$ or $|1\\rangle$, where the value denotes the set the vertex is in. The goal of the problem is to maximize the number of edges $(v_1, v_2)$ for which $v_1 = |0\\rangle$ and $v_2 = |1\\rangle$, or vice versa. If we associate the $Z$ operator with each qubit, where\n", "\n", "$$\n", " Z|0\\rangle = |0\\rangle \\qquad Z|1\\rangle = -|1\\rangle\n", From 7568a912b4eb09f3883a78a23affeae6fafe663b Mon Sep 17 00:00:00 2001 From: abbycross Date: Wed, 26 Nov 2025 14:55:56 -0500 Subject: [PATCH 14/28] revert change to package-lock.json --- package-lock.json | 1 - 1 file changed, 1 deletion(-) diff --git a/package-lock.json b/package-lock.json index 8e0d62e945e..77165d2d067 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1054,7 +1054,6 @@ "version": "8.12.1", "resolved": "https://registry.npmjs.org/acorn/-/acorn-8.12.1.tgz", "integrity": "sha512-tcpGyI9zbizT9JbV6oYE477V6mTlXvvi0T0G3SNIYE2apm/G5huBa1+K89VGeovbg+jycCrfhl3ADxErOuO6Jg==", - "peer": true, "bin": { "acorn": "bin/acorn" }, From 73cec223a40f07fbc899926fbd3681f3afd496d1 Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Tue, 2 Dec 2025 13:26:51 -0500 Subject: [PATCH 15/28] test commit --- docs/guides/tools-intro.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/guides/tools-intro.mdx b/docs/guides/tools-intro.mdx index 6b0b382a8b6..0adc10b43b2 100644 --- a/docs/guides/tools-intro.mdx +++ b/docs/guides/tools-intro.mdx @@ -3,7 +3,7 @@ title: Introduction to Qiskit description: What is Qiskit? This document provides an introduction to the Qiskit stack. --- -{/* cspell:ignore Benchpress */} +{/* cspell:ignore Benchpress */} # Introduction to Qiskit From 8a61dd9f676851c5deb1f4249f15785b1dcf37a6 Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Tue, 2 Dec 2025 13:31:23 -0500 Subject: [PATCH 16/28] undo test commit --- docs/guides/tools-intro.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/guides/tools-intro.mdx b/docs/guides/tools-intro.mdx index 0adc10b43b2..6b0b382a8b6 100644 --- a/docs/guides/tools-intro.mdx +++ b/docs/guides/tools-intro.mdx @@ -3,7 +3,7 @@ title: Introduction to Qiskit description: What is Qiskit? This document provides an introduction to the Qiskit stack. --- -{/* cspell:ignore Benchpress */} +{/* cspell:ignore Benchpress */} # Introduction to Qiskit From b1598b4664518f2e00fdcde2aad1ae5665207d65 Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Tue, 2 Dec 2025 14:01:59 -0500 Subject: [PATCH 17/28] Bring in Mirko's changes Co-Authored-By: Mirko Amico <31739486+miamico@users.noreply.github.com> --- .../advanced-techniques-for-qaoa.ipynb | 145 +++++++++++------- 1 file changed, 86 insertions(+), 59 deletions(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 3f5aa410162..6ef371af1c2 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -7,7 +7,7 @@ "source": [ "# Advanced techniques for QAOA\n", "\n", - "*Usage estimate: 3 minutes on a Heron r2 (NOTE: This is an estimate only. Your runtime may vary.)*" + "*Usage estimate: 3 minutes on a Heron r2 processor (NOTE: This is an estimate only. Your runtime may vary.)*" ] }, { @@ -36,8 +36,8 @@ "\n", "Before starting this tutorial, be sure you have the following installed:\n", "\n", - "* Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", - "* Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\n", + "* Qiskit SDK v2.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", + "* Qiskit Runtime v0.43 or later (`pip install qiskit-ibm-runtime`)\n", "* Rustworkx graph library (`pip install rustworkx`)\n", "* Python SAT (`pip install python-sat`)" ] @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 91, "id": "d019ea68-61e0-4341-84c7-e612ca10dde7", "metadata": {}, "outputs": [], @@ -119,7 +119,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 96, "id": "e04ce982-8294-4ebe-8dcc-f74205938800", "metadata": {}, "outputs": [ @@ -144,7 +144,7 @@ "True" ] }, - "execution_count": 18, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -155,14 +155,15 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 97, "id": "8b864d32-9483-45ea-831f-60488e330adb", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "

" ] }, "metadata": {}, @@ -187,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 98, "id": "e39c4e42-ce97-4a04-8879-da4d33a684bc", "metadata": {}, "outputs": [ @@ -255,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 99, "id": "7c54402a-7696-4b0e-826a-6e0ac4c54395", "metadata": {}, "outputs": [], @@ -300,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 100, "id": "df9d861a-64e8-49ee-aed1-c41f437fa743", "metadata": {}, "outputs": [], @@ -523,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 101, "id": "e689e09e-6ca7-4154-8602-d1d954ebe80b", "metadata": {}, "outputs": [ @@ -531,15 +532,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Layers: 0, Status: True, Time: 0.02229800000000015\n", + "Layers: 0, Status: True, Time: 0.022812999999999306\n", "Map from old to new nodes: {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20, 21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26, 27: 27, 28: 28, 29: 29, 30: 30, 31: 31, 32: 32, 33: 33, 34: 34, 35: 35, 36: 36, 37: 37, 38: 38, 39: 39, 40: 40, 41: 41, 42: 42, 43: 43, 44: 44, 45: 45, 46: 46, 47: 47, 48: 48, 49: 49, 50: 50, 51: 51, 52: 52, 53: 53, 54: 54, 55: 55, 56: 56, 57: 57, 58: 58, 59: 59, 60: 60, 61: 61, 62: 62, 63: 63, 64: 64, 65: 65, 66: 66, 67: 67, 68: 68, 69: 69, 70: 70, 71: 71, 72: 72, 73: 73, 74: 74, 75: 75, 76: 76, 77: 77, 78: 78, 79: 79, 80: 80, 81: 81, 82: 82, 83: 83, 84: 84, 85: 85, 86: 86, 87: 87, 88: 88, 89: 89, 90: 90, 91: 91, 92: 92, 93: 93, 94: 94, 95: 95, 96: 96, 97: 97, 98: 98, 99: 99}\n", "Min SWAP layers: 0\n" ] }, { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
" ] }, "metadata": {}, @@ -558,7 +560,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 102, "id": "ecf6e8c3-65c2-4430-8dd3-d67b8842045d", "metadata": {}, "outputs": [ @@ -606,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 103, "id": "57d5eb53-9cda-4c38-a00b-26ed4b533bcd", "metadata": {}, "outputs": [], @@ -783,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 104, "id": "7793ef92-ce59-4fd7-b43f-48d4e3427e3a", "metadata": {}, "outputs": [], @@ -801,17 +803,26 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 105, "id": "82ae28b3-85eb-4487-8100-1e622e93cccf", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mirko/Workspace/documentation/.venv/lib/python3.13/site-packages/qiskit/circuit/quantumcircuit.py:4625: UserWarning: Trying to add QuantumRegister to a QuantumCircuit having a layout\n", + " circ.add_register(qreg)\n" + ] + }, { "data": { + "image/png": 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qPj7++OPx7LPPZpNtctVVV2VBSFr8KFUKBKB9pA/4KagbM2ZM9vtmm22WVeZOiY6yFOaqdOwqsqU/1KaExx//+Mcs+C9LYa6lP9CkQDglfO67775SFeZKhVFPOumkOOecc2LOnDmlK8yVkh3rrLNOlNF3v/vdLNl16623ZotMtdxYVhZL3zicEv/z58+PM844I8oiFT9uOfHRsv1TMiAlw8sgzXm/+c1vss+cqR+Sc889N/tc+q1vfasUhbna43N8kdufYuX0laQ+KKJKffCrX/1qid8/97nPZfmZtO8U4eRYpfbvuuuu2VeLdJNNan+KF8v2mSjFinvssUfssssuhTpBlrf9RY0XK7X/v//7v+Ovf/1rvPjii9kCpUWMFyv1wdInQu+6667sWFiUvEml9qd4MV1M3hIjp+1/5plnxle/+tUoQ/t/+ctfZse+o48+enHe4NFHH81ixSJcKJUnN1T0eDBPHxQ5JszT/iLHg3naX/R4sJoccRHjwWraX9R4ME8fFDkmzNP+IseDedpf9HgwTx8UOSasdJ6s6LFgnj4ociyYp/1FjgXztL/osWA158uLGAtW0/6ixoJ5+qDIsWCe9hc5FszT/qLHgnn6oMixYN7rhsoQE66o/UWPByu1v+jxYJ4+KENMmOfawaLGg9X0QZFjwhW1v+jxYKX2Fz0ezNMHZYgJV9T+MsSDK7qGugyx4IraX4ZYsFIflCEeXFH7yxAL5rmPouixYJ4+KHIsuKL2lyEWXFH7yxILrqgPyhALrqj9ZYgF89xTVuSYME/7ixwT5ml/0ePBPH1Q5JiwmvtKixgTVtP+osaDefqgyDFhnvYXPSbM0wdFjgnztL/IMWGl++uLHAfm7YMix4J52l/0WDBPHxQ5FqxmnY0ixoLVtL+osWCePihyLJin/UWPBSu1v8hxYN4+KHIsmHetoaLHhHn6oMgxYZ72FzkmzNP+oseD1aw5VsSYsJr2FzEmzNP+oseDefqgyDFhnvYXPSbM0wdFjgnzrL1Y5HgwT/uLHAvm7YMix4N52l/keLCa9VeLGAtW2wdFjAfztL/I8WCe9hc5FszbB0WOB+fkaH+9YsEu7fYv0SmlHSkNqBRQtzjkkEOyixVS1c+W5wwdOnRxUa4kBd/rrbde/OlPf2rI+wYoqjTnpnn4g9LvEyZMWKYiL8WXbixOlYhX5iRXW6TqwynATfvFvvvuG2XyzW9+M5qamkpXnK5Fmu9SrJm+Dj300Hj44YejLG688cYs0fXZz3421l133dh0003j/PPPz+LzMkqVqo844oisL8oibf807/3973/Pfn/55ZezSt2pOn0ZpAujFi5cGL169Vri8d69e2fVy9Pfyy7P53jK5+233y5tzJgKyaeTQ2WLF9MJksceeyy+//3vRxmlY0VKiKcYYZ999sluuipTvLjTTjvFD3/4w1h//fWzfkgnBN58880oq//6r/+KbbfddvHFE2WIF5955pnsooD0uXnq1Klxww03lCZeTCfQWosVU+4sfZUhN1S2eLDs+bG87S9qPJin/UWPB5fXB2WJB5fX/jLFg631QZliwjzzQJHjwdbaX7Z4sLU+KEtM2Np5srLFgmU+V1hN+4saC+Zpf9FjweX1QVliweW1v0yxYGt9UKZYMM88UORYsLX2ly0WbK0PyhILrui6oTLEhGW/bqra9hcxHqymD4oYE1ZqfxniwUp9UPSYcEXtL0M8WM0cUNR4cEV9UIaYcEXtL0M8uKJrqMsQC5b5GvJa+6Bo8WA17S9iLFip/WWIBSv1QdFjwRW1vwyxYDVzQFFjwRX1QRliwRW1vwyxYJ57yoocE5b9nrpa21+keLCWPihSTJi3/UWNCfO2v8jxYJ4+KHJMWMscULSYME8fFDkmzNP+MsSEy7u/vshx4PKUcY2BattfpFiwlj4oUiyYt/1FjQXztr/IsWCePihyLFjLHFC0WLBS+4scB+btg6LHgnnWGip6TFj29ZZqbX9RYsJa2l+0eDBvHxQ1Jszb/qLGhHnaX/R4sJZ5oEgxYZ72Fz0mzNMHRY4J86y9WOR40NqTtfdBUeLBWtpfpHgwb/uLGgtW0wdFjQfztL/I8WAtc0CRYsG8fVDkeHB+jvbXKxbs1m7/Ep3SlClTYvfdd1/isZaKj+lvLd9bqwKZHmt5DkDRTX59WsyYNWeZxxcsXLj4+/OvTFzm9xbdu3WNoetXvuCntTn3g/NyuqmgEWbMnB2T33iz5vYn660zMPr0XjJ4WVmkYPPlV6fEwkWLam5//1X6xDprLlldt5Jvf/vbWaLjE5/4RDTaP6e/HW+9M2OZx/P2QVNEDBsyaIlE3vK88847MXjw4Jg3b1624OLXvva1OO+886KR5s2bH+Mnvb7M49WMgbUHrBar9l+l4v/1P//zP9kHvJTgTDeWdxavTn4j5sydt8RjrbV3eX3Qq2eP2GDQ+1W1VyR90P/FL34Rm2++ebbYZrpQYs8994w//vGPi6sWN8I7786I1998u01jYMPBa0ePHt1X+P+89NJLWQXyY489Nm655ZaYPHlyfOpTn8q+X3311dEoaV98acLkaG5D+1dfdZVYc43Vcv+fY8eOzSqyp+rTncHUf06Pd2csexF/3j7o2qVLbLTBuhX361SBO33g32677bLnLliwIEt4pcUmGmnW7Dkxceq0ZR6vZgwMWntArNKn9wr/n379+mWfUdN232abbWKDDTaIO+64I373u99lx4Vp06Y17CLql1+bEgsWvN++Wtq/Sp9eMWjtgXX5HN8R3nzr3Xjz7XeXebyaPhi2waDo2nXlrAE/f8GCeOW1qW1q/5prrBqrr9qv3d/br371qyxh+IMf/CA60sQp/4xZc+bWHAv07N4thqy3bH6pVumz0T//+c9sbjjrrLPikksuiY6UjgHpWNCWMZBioRQTtVVKiqfjRYobU0K4Xp+JXpwwKZqba2//av36xloDV2/ze9lkk03i5z//eWy99dYxd+7cuP7667MLa3/2s5/Fv/3bv0VHeX3aW/HOezOXeTxvH3Rpaso+E7U1xk/x4iuvvJJ9tkptnzlzZnbT2VFHHRUPPPBAh32GSJ8F0meCpVUzBtZdc43ot0qfdn1f6dj3+9//Pv7jP/4jOtqEiVNj7vwFNc+DfXr3jPXWWbPN7yN9Vkifmw877LDsZFGKF9OJsSuuuCI60vR33otp099p0xjYaP11o1u3rm16H2mxjUsvvTT233//2HLLLbOFedOckKTPTUOGDImOsHDhonjp1cltav+A1fvHgNX6tzk31Kh4cNLr02LmUvnRavaBvPnRzpofa1R+NE/76xEPdsb8aL3jwX+++Xa89W4b86MbDs5igrb2QSPiwbnz5seEtuZHB64eq/br2+b2NyoebGt+tHfPHrF+jvxonj5oREz49rsz4o065EernQfrFQ+m/OiLE9oWC6yxar8YuMaqbW5/o+LBKW+8Ge/NnF17frRrlyw3VK3W+qARMeHM2XNiUh3yo5XOkzUqFmyX/Gjf3jForQG5/q/OeK6wXfKjQwZl5wras/31yg3On78gXpnYtvzoWgNWi9VynCvN0/56x4Ltkh/t0T2GDF471/+1oj5oRCyYvPvezJg67a265EdX1P5GxYKLmpvjpbbmR/uvku0HeayoDxqVH6xnfjTvPFjP3ODsOXPjtSn/rEt+dEXtb1QsmIyfODXmtSE/2rd3rxi8Tr5zpSvqg0blB+uZH6103VAjYsLUnpQbau3x9s6PdtbrptqaH+3RvVtsmONcabXtr1c8+N7MWTHljbadK11v3TWjT6+e7dYH9YwJU340nSNYtKi5w/OjldrfqHiwnvnRSn3QiJhw7tx5MaGN50rz5kcrtb9R8eCrk16POfPm154f7dUj1l93rXadB+sZD7ZLfnS9dbLjQVv7oBExYT3zo5Xa36h4sK350W5d0/Wjg9p8DXWj8oPtkR8dvPbA6Nun10p5DXny8quTY8HCRR2eH622D+oVD057652Y/vZ7HZ4fzdv+eucH0+fh9Lm4o/OjldrfqFgweW3KGzF7ThvuJenRPTbIkR+t1AeNyg+2R350yKC1omeF/Gil9jcqFqxXfrSaObCesWDy+j+nxzttuJekS5em7FxppW1UqQ8alR9sl/zoWmtEv7592tT+RsWC9cyP5rmnrBEx4fS3341pb7XtXGm6n6pb164r5T116Tx5Ol/elvYPXL1/rFEhP1pL++sVD6bPA+lzQUfnR6vpg3rGhO2RH11/3TWjd4X8aJ72N+pekrbmR1ddpU+sXSE/mqf9jYoHU14o5Ydqzo82pc9ElfOjefqgETFhe+RH1xm4evSvkB+tdh6s670kk17PrqPt6Pxonj5oREzYHvnRoeutE90r5EfztL8RMeHCf91f36b86Gr9YuDqq7bp/vpGXjuY7qNI91N0dH60s64xkM4Tp/PFHZ0frbb99YoF2yM/2q9v71g35/Wjefqg3vnBdL1Ium5kadX0wcY519pZUfsblR+sV360UvsbFQsmr01+I2bP7fj8aKU+aFR+MF03mK4f7Oj8aDXzYD1jwSw/On5S29ba6b9KrJnz+tHltb+R1w62da2dvPnRSn3QqPxgun48XUfe0fnRPGsNNSombHN+tE+vLB6qpLOut5TuI0n3k3R0frSW9tcjJmyX/Ogaq2bXjbRX++sdD9YrP5qnDxoRE743Y1ZM+WfH50fztL8RMWGWH50wOYsJas6P9uubXT/Y1vY3Kh6sV3602nmwXjFhe6y1kyc/mqf9jYoJ25ofTdePp+vIK8nTB42ICd965734ZxvvJcmTH82z9mIj4sF65Uc789qTbc6Pduua3U9USS19UI94cMas2TH59battZOul0jXTbRX++sdD6ZzpWndqY68lyRP+9N6C43ID7Y1P5p3LfI8fdCIeLA91iLPkx/N0/5GxYNtzo/mWIu82jmwnvnB9liLPK0vkNYZaGsfNORekub65EfztL9esWBTc/okSCFsuOGGccYZZ8SXv/zlxY+NGDEi25nSYkYt0oS6yiqrxG9+85s47rjjsgWO/vGPf8TDDz+8xL+XKgGmC11//OMf17UdAI2QLhS5/NpblwkC8jpi/91i9x22rPi8Hj16ZEHdBxeXGzdu3OKDfaOqsKZCBD+68rfxxpvLnijPIy08/cmTjqy6EENrx65Guffhx+POB8fW9NqUDP7kyUfmumiyxU9+8pP4/Oc/nwX6BxxwQDRa+jD8wytvzBZdq8Vu228RHzpgyUTeim7gfvnll2PWrFlZ/PHFL34xS3Z09MUwK5JC4l9c/8d4/pXXanp9OjHy6dOPrbi4SAr004f89KHmkEMOyR77+te/niW90sWkjfT3Z16Ka35/T82v/+iRB8TITTeq6bWpEnmaH++8885olHShxA+uuCHenbHsYmN5bLLR+vGxYw+pmKzo2bNnDBgwIKu23L37+x+cUwXqFJenSuRrrFHdAtbt6Xd3PRR/fnxcTa9NJ4jPO210VRcOpxMi99xzT5YA6gyLLaWTY//1q98tc5I0r4P32jH23XXbis9LSa5Pf/rTcdFFF2UJgeeeey5LeJ1yyikNvYA4JYR/8qubW11cIY90cvhTpx4d3btVXlwkjf90/E/bPyX+UhJ0v/32y2KkVJV97bWru/Cwvfxp7FNx6z1/rum1aQR//KNH5Do50h6f4ztCOkH+/SuuX+YkaV47bLVJHHvY3u0aC6bjw8Ybb5wlxOsRC1x9893xj+dfqen1adHlz5x+bLbARnv2QUoWfvjDH46f/vSncfLJJ0dHevalV+PKG/5Y8+vT9k/joL3an04OpHgxLcTzhS98IXve//k//yc6SrqRLMXDrS0wkkcqRPLxj4yquhDD0n2QTgZtv/32ce6558aZZ56ZPXbllVdmz0kJ8o50+32PxP2PPFnTa9OFguk4UG0xjryfCU899dT485//HM8//3x0lLTI0n/892+XOUmaV4oDUjzQ1van4+L48eOzE0MtsWG6wDjlC9JFxukCk46QPqf99NpbW72JIo+04OanPza66kIMlcZAio++853vZCdF0omVjvTok8/GTX98oObXn3bcodnngra2/8EHH8xiga9+9avZohOTJk2K888/P/ss2ZELD6SLJn/w8xuWuXA0r/R58CMf2r+q2L619k+fPj27KOLGG2/M/q1BgwbFiSeemI2DdLHIzjvvHB3lN3/4n3j8Hy/U9Np0gvizpx+bq2B3pdxQo+LBdLHU5b++tebXf+iA3WK37SvnR6vNj9UrJkw5sR/98qZWbyrOI100fNaJ1eVH87S/nvHgPQ89Hnf9qcb8aJemOPuko3JdNJm3/fWOB/85/e249Bc3xvylCpLktfv2W8YRB+xW1Wta64NGxYPpM9HPf3N7vDB+yYsfqsmPps9ElS6erzVHXo948MmnX4xrb7m35tefeNQBsdUm1eVHl9cHjYgJU9H27//8hiwmqMWmwzaIU0cfXFUskGcM1DMevPnOP8Vf/vZ0Ta9NcfCnTxtdVZHO5bW/UfFgWnz5P6/+fTYf1OKQvXeKfXbZpqrXLK8PGhETpgvAfnLVza1ePJxH+jz8qVOOzlWIYUXnyRoVCyYPPvb3+MO9f6nptWnP/8RHj8hdqDXvucJ65gfTDRTpM1Gt+dEdR24aow/dq13bX89YMO37v7r5rhj3/PiaXp/yoll+NEdxujztr3csmDz9wvj475tqP1d53OH7xPZbjsj13OX1wUknndSw3GC6keyHv7gx2xdqkRbXOePDh+fKj1Z7vUA9YsHktv/5Szzw6N9rzo+e+7FjKt5MmacPGpUfTDcR/fiXv12mYHNe++22XRy05w65npt3DNQzFkzv6bJrbmm1YHEe6Tz5eR87Jld+dEXtb1QsmDzyxDPx2zserPn1px93aIzImR9dUR80Kj+YFhb5wc+vj1mzlyzSmNfWmw3L8qOV5LluqFEx4XW33ht/G1fbtUspH5BigUoFaaq9bqqe8eCL4yfFz677Q82vP+qgPWKXbTdv1/bXMx5MC6v86MqbshxZLdINVGee+KGKhRiq6YN6x4R3/+mvcfdDf605P3rOyUdXXIC7Uvsbea44Fea69Mra86N77LBVjNp/14rPq/X6yY6OCdP1Yj+/7rZ4ccKkml6fbqJM82ClQrV52t+oePBv416I6279n5pff9LRB8aWI4a26/avZzyYzpH+oA350c023iBOOaZyfjRPHzQqJkyxYIoJa5Hi4M+cNrriIvR52t+oeHDCxKnxX9fcUnN+9NB9do69d966zddQNyoWTPnRH//3zdm1M7VIC66ec/JRufKjea8hr2csmKScQMoN1CKN1TM/ekSuhbaq6YN6xoNpUYUftiE/utPWm8Yxh+TLj+Zpf71jwbTvp9zgMy9OqOn1aeHhz5x+XFWLLy/d/rQgeaNiwSTlhq/6be350RNG7RvbbjG8Q+4lqUd+MOVHUyzQ2mJjeWw8ZHCcfsJhVV8/unT7GxULJukcUTpXVIs0/5/3sdEVC3NVs/3rGQsmaQH2n/z3zTXnR/fffbs4cI98+dEV9UGjYsHU7suuviVenVxbfnTNNVaNc9P1ozkK1a6o/Y28djBdK5CuGajVvx1/WAwful7F5+W5p6wRMWEqUJjmwXRvXS222Xzj+PAR+1V8XrX31NUzJvz1LffGE0/Xlh9N95N+5t+Oyxbbas/21zMefOGViXHFb26r+fVHH7xH7LzNivOj1fZBPWPClB9NubF0j3UtNhi0dpx54hEV86OV2t+3b9+GxYR3PTg27nn48Zpem9p99ilHVVyAu9b7ausRD74x7a249Jc3ZQtR12LPHUfG4fvtUvF5efqgETFhyo9ecd0fWl14sz3zo9WOgXrGhI//4/n4zR/uq/n1Jx9zUGwxfMOKz8vTB42ICVNRou///PpWFx7NY/PhQ+Lkow9ql/vrGxUTpnuJ0j1FtUiL7KU1JiotQl/p/vpGXjuY7iW7rA350cP23SX22mlkVa+ptMZAPWPBNP+newpTUZKa86OnHFWxEEM17a9nLJjc/8gTcft9j9b02vT+zzrxQ9mik9VYUR/UOz+YinGka+fSAqy12HmbzeLog/dsU/sbea74/fzoHfHMi6/W9Pp+q/TJYoFKiy/Xss5Iva4d/Mdzr2TX0NYqfSZOn42r0VofNCo/2Nb86PANB8dpx1eXH600BuqdH0xrjKS1RmrR/V/50WoKc7XW/kZeOzhx6j+zeymWLtic1wF7bB8H7L59Va9prQ8aFQum/Oh//er38dqUZQty5LHmGqtl149WKsSQZ62hRsWEaa2ptOZUrc444fDYeMPB7b7eUr1iwlSgMM2Ds2vMj6ZzZOlcWXu3v54x4bW/vyeefOalml7bp3fP+Ozpx2UxQXu1v97x4PMvvxY/v/72ml9/zMF7xk7bbFbxeZX6oFExYYqDL73yppj2Vm350fRZIF0zUakQQ61rrtUjJrzzgcfi3j//reb86DmnHh2DKhQrztP+RsWDqUhruoa4ZaH9aqVrptK1U5VUOwbqFROm/OjPfv2HbC3iWqScULq3uFKh2jztb1RM+Nenno/rb6s9P3rK6INj840rF0nI0weNiAlTUaK0/mj6XostRmwYJx11YK776yutvZi2eyPiwRtuvz/G/v25mvOjKS+weo78aDVrT9YzP/jKa1Pipyk/WuPrR+23a+yx41a5nltNH9QrHmxrfjSdIzz75KNyrbWTt/31jgfv+8sT8cf7a8uPpnzIWScdWbEoUZ72p0I0jcgPpkLFP/zFDcsUbM4r3UuV7qnKo5Y1aDs6Hkz50V/eeEe2Bmct0rUiaR7skyM/Wqn9e+21V0Piwaeeezlbg7VWHzliv9g6R360mu1fz/xgygekvEDKD9RixND1srUH2yMWSOO8EfHg7+9+OB7+6z9qem3KiaXcWMqRtbX96RxyPWLB6qpnsNJJVf7SgPqg119//+LYlgqArT2n5XmNqhYLUG9pkaA9dqzuYp8P3kCz6/ZbtNu83AipiMYJR+xb8cLf5d1Ac/yofasuytXZ7L3LNtkF0LUuMFRNUa60mEwKbDtLUa5k4BqrxuH7Vr7wtzUp+D1k7/zBaQp+U6Jr5MiRWcB7wQUXxJe+9KVopBRwH3voXhULay3PcYftk+u1qRhq+mA3atSo6NatW/b1jW98I7tQIP18zTXXRKOM3GxY1Rf7tNh2i41rLsqV7LrrrlkCoJHS9juuyoIyi1/bu2eMPnTvXB+E01yfLoRouWg42WKLLRZ/SGykdHIr3RBXi8P327WqolzvvvtuXHvttVmhys5QlCtJCc08hbWWd4I476IKn/vc57KTQynJvdVWWy0+CXLxxRfHnDm1FUFoD+k4fsLh+9a0MEKKH044Yr9cRbmSVGn8rrvuihkzZsSrr76aFSnt3bt39O/fP9ZcM/8i7u0tFRFIcV0t9txpZNVFuTpbvJhugsmb2F5aOimWZ4GlzizNRenC52oLa7U45pDaX7s8v/71r+OEE06o28XzaRH1tEBGLTYfvmHuRWfzGjp0aHaMTIvRpvj5wgsvzE4Ud5R0gcPxh+9b03EpnSA+/vB9alpUYWnphEiaF88555zF8WJaiHLhwoXZzx88cd7e0qIA1RbWapEKUtX62jx22223Do8X000wB+1ZW7HsQWsPyBZWaJf3se662dcHbyqsR7yYPqelcVxtYa0k7TcpjqjltZUWZ7388suzEyP1uHh+x5GbZAum1XqCuNqiXMuTPh8fc8wx2TyQPjcfeuih8Z//+Z/Z58WOLOicLng9+pA9a35tiiPaI7ZPYz9dEDR79uxsv09f66//ft9utFHtnzvzSAXHU0xUi9T+aopyrSg31Kh4cNiQQdnCmbUYvuF6sct2+fKjnTU/lk7ynjBqv2wB1apfW0N+NE/76x0P7rPrNlXlOD9o/922r6ooV5721zseTDnOdFNwLdLiUqkgTTWW1weNigez/Ohhe1dVWGvpQhTVvLbaOaAe8WC60CctpF6L7bYcXnVRrkrHgnrHhOlCr+MObUN+9JC9qooF8oyBeseDh+2zc1U5zqUvmq2mKNeK2t+oeHCDwWvHvlUW1mqRFhutdlGFFfVBI2LCtCBCigWqWRhhidzqqPy51RWdJ2vkueTdd9gqO2dei7123jp3Ua7Oeq4w5Tg/dODuNd9AMyrHAkvVtL/esWBb86PpOJCnKFfe9tc7FmzJce4wcpOab6DZropFZ5fXB43MDaYFslJuqJZP9tXmR6udA+oRCyYpN1hrjvOQfXbKXZQrz7GgEfnBdCPogTkLay0tFSHZf7f8+dE8Y6DeseD7+dF9a1o4No39lBfImx9dUfsbFQsm6RxROldUi1232zx3Ua5KfdCo/GC6CabaRZL+/2v7xpE544g81w01KiZM+dFKhbVWmB/N8drOfN1UWhgkFV6vxYih62cLbbVn++sdD6b5L32uqSk/2r1bNofmufa0mj6od0yYrhlav4oc5welxYUqFeXK0/7vfe97DYsH0wJReRZGaM1aA1aPg/fOd5611nmgo2PCdDxP1w5WWji2NU3/yo/meW3e40Aj4sF07Wit14Cm60UqFeWqdvvXOx5MCwUeW2NBmVSE5Zic+dE8fdComDCdIxmwev4c5wcdsf+uFYty5W1/o+LBIeutE/vsUlthrZQX2zPnogqVrqFuVCz4fn5037rkRzvjNeRJWhijmhznB6Xrh2styrW8Pqh3PJhynEfsv1tNr03nR9J15O3Z/nrHgmkOTznOWgprJcceunfNr21pfyNzgy05zu23qu0a0HSetNb7UPLMA/XID7YlP5pem86113r96NLHgUbEgslBe+1QVY7zg9JniVqKci1v+9c7FmxZJCgtHluL9dZZM/bbtfbrRz/YB42KBdNn+uNH7dO2/GgNr126/Y28djDldmq9BnS37bfIVZQr7z1ljYgJ+/frG0fVmB9NecG8+dHOfE9dOleccr21SLnlSkW5qm1/vePBNIbTWK5F2nd22rpyfrTaPqhnTLg4P1rD8Ty9Ns2hefKjldrfyJgw3SOfjmm1SMfQSkW52jIH1CMeXGvg6nFoFffIf1CKoVIs1Z7HgXrHhO/nR/PlOJeW9poUC+R5bTVjoN4xYVpEfatNKuc4W7PDVpvkKsqVtw8aEROmz7Tps20t0vVCxxycLz+ap/2NiglTbmON1Woba0ccsFtVRbmWd399I68dTLmtvPfIL22jDdbNvehsZ11jIOU2U1GdWtbLSTnVD1eZW63U/nrHgi1FJmvNce6zyzZVF+Wq1Af1zg+mHOcRNeY407mVatfpaa39jYwF03tI57qqKay1TH60itdWMwfU69rBLTcZGtvVeI98Osda7X0oKzoWNCI/mGK549qUH63u/vpKY6AR+cF0zUO69qHW86zVFOVaXvsbee1g+jy4f5WFtVqka22qXadneX3QqFiwJT9aqbBWa9K1VmnNvjyvzbPWUKNiwnSPfFpEuha777BlrqJcnXm9pZTjrHWtnWryo9W0v94xYWpDnhxna9JnwkpFuaptf73jwXQNdLoWuhbp2usdc67TU6kPGhUTpmvgj681P9ry2hz50VrngHrEhOk4mCfH2Zp0D0alolzVHAcaEQ+mHGe198i3SPfgpLWK8qhmDNQzJszOdx6+T3ZvVK350UpFufK2v1ExYbpHPl03UosdR26aqyhX3j5oREyYcpyjD63t+tF0L2Y6V5g3x1Vp7cVGxYMpL1JNjnPp86x5inJ15rUn03WD6R7pWtfp2W2H/Peh5O2DesaDi9cTr8Na5HnbX+94MK2RUG2Os8U+u26bqyhXpfan9jUqP5hynGmtjFrXMU9rdORVyzzQ0fFgy/Wjac2UWqTcWJ6iXHmPA42IB9M1oOl8aS1SbjRPUa5qtn+984NpraR0vrzW16YceXvFAo2KBw/de6dchbVak86R5H1tpfbXKxZcuStoUNHuu++eDbQ0mbT44x//GH369Iltt9128XNSJdAXXnhh8XOefvrpeO2112KPPWpLFAKsjGq5iSQ7uVzFDTRpzr3jjjuWeCzNyykwWG+92k5ONfomksPacANNZ1LrTSTVniBOVWdTte3bbrutUyw63NabSLITxG24gSZJcUojT4629SaStChL3hPEqdL0U089FU888cTir7TIUAr008+HH354NFJKbla7yE56flqcpy0ef/zxxR92GqnWm0jy3kCTpKrT6QPtByvPP/fcc9n3DTes7cREe3n/RpDqT5LWUsjlV7/6VcybNy9OO+206ExquYkk67fD850gTlLic+nndu3aNZqbm7OvRqr1JpK8J4iXlj6XpirkaSzccMMNcdRRR+Xux46Qxn66Mbram0jSCeJaC7nU+jm+o6Qb46u9iSQ7QZxzgaHOrtabSNJirWnR1vaUEsIf+9jH4pe//GXdLp5vuYmkmoXEF99Ac0j+E8S1SPtD+krzRWe8iSTFQnlPEFcyePDgZeLFtAhROlaknz/+8Y9HZ7uJJC3avXsbFhjqTPFiWiip2ptI2rJ4e6vvYc89s4vm3nnnnbrHi2mhrLRgVrXS4v1pEf/2lo5/6YTgmWeeGfWw+CaSKhfKScUbqjlBXGu8mHR0vJgWzKul0GIq4pH3BHFePXr0yHJlqS/SRfZ77bVXh19IU+tNJOkEcTULDFXKDTUyHqzlJpLsBHGVCwx11vxYWjg1LaBa0w00VZxcztP+RsSDWdHlUfluBPmgVMwrFfXqyO1fr3iwlptI3s+P7ldVv62oDxoZD9Z6E0kq6ldNoetaxkC94sH3byKpLj+aijpWmx+t1AeNiglrvYkk7w001Y6BeseDtd5Ekoq7piKv7dX+RsaD++dcSHyZfkvFWKrI6+UdA/WOCWu9iSTlBlOx5/Y4T9bIWLDWm0iquYGms58rTIWVqr2JJH2WzHsDTd72Nyo3mN1EUsMi5Okc0WY5b6CpZfvXKxas9SaSdANN3gWGKvVBI2PBttxEkmLIWgtd5xkD9YoFU340fSaqNj+aYuHdaizk0lofNDI/mG4iqTo/mvotFWOpYXGiFY2BeseCi28iqWER8n2ruIGmUvsbGQvWehNJuoHm0H1qK3S9ojHQiPxguokk3VRarXTtYN78aJ7rhhoVE2Y3kRxe/U0kKTead4Ghzn7dVPo8UO11kH2yG2jyxQJ529+oeHC9ddeM/XfbvqYbaNJc0JFjoB4xYTqWpdi+e5VFRTYYtHbsnbPQdaX2pxunGhkP7rLdFjF8w/WqzyunBYa6devQMVCPmHDV/qvUlh/dcWQMy1noOk/7GxUPpnkstb+aPF+SPgukRUfbe/s3Ih7cZNgG2XmCaqVzzP369mm3PmhUTJhyQim2r/bz7ebDh2SLD7f3GGhEPJjyo4PWHlB1v6UYqi3XPX5wjmtkfjDl+Q7eq/rrIA/ea6eaC113pmvIa82PptxwrYVcltcHjYoHU1Gmaq+DbGrD4kR5x0C98oMpz1dLfjS7B6fGQtcfbH+jc4NJKs5W7XWQKXY46uA92pQfrTQG6jVPpOsg99xpZN3zox9sXyNzgymmz/KjXarPj+66XW2FXJa3fRsRCybp+uFqF9npXmNeeXl90Mj8YLoOspZCi+kenHTtUHuOgUbEgll+NF0H2au6/Gi6ZuyQKu7ByXNPWaNiwrSQeC2FFtPCNCm3uLLfU5e2/XGHV38vybZbbBxb5Sx0nbf9jYoH01heM2eer0U6p5D2nbyxQK1joB4xYZrL0pxWrTR3pjm0PdrfyJiwJT9abdHldOzMew9Ordu/XvHgrttvUdV1kP8/P7pf7vxonj5oVEyYYtq8C4l/UIqhN8pZ6LqaMdCIe0nS+gL9+vauodB1/hgqTx80KiZM94mnz7jVOubQvbLP1O09BuodE9aaH80KXVd5D87y7q9vZG4wSTmuaq+DzPKjVRZj6axrDGT50RruE0/34KxdZX50Re1vVCyY9rWU50vXBFe7RtH+u1cfQ1UzBuqVH0z3iVd7HWSaM9LcUW2/tdb+RucH07mudM6rWuncWrVrFFWz/et5DuFDB+xWdZ4vrTGT8oPVHj+W1weNzA8Oq/E+8aMP2qPqNYoqjYFG5Aez/OgR1edH0zUW1Z5jXl77G5kbbCm0WO11kN1rXLy90hhoRH4w5fmqLbTYco457xpFedYaalRMmOaxtM5K3jxfi3St3SF75b8HpzOvt5QtJF5locUkrTeVd62dvO1vREyYroGtZRHydM1tKnDZkdu/XvFguhY673WQLVJx0nSOOW8sUKkPGhkTpmNAtYUWk1FVrFFU6xioR0xYa3403XuR7sFor/Y3Mh5MRWVScZlq++2EKvqtmjFQ75iw1vxougcr7z04edrfqJgwW2vn4Px5vhap0P2o/fLHUNWMgXrHhCknUO06mkmKodI9me219mKj4sF0b2yaB5tqWKMo3ZO7sq89maR7pKu9DvL9tcirz49W6oNGxINpDdG0Hnst9+BUu4Z7tWOgHvFglh9Na2pXmx9dZ2DsX8M55tban+KdRuYHU7HZdK6oGl1qzI9WOwbqEQ+m6yDTsbBa6bq5WgpdL6/9jYwHUyxUfX60bxxZwz04lbZ/I/KDaU35VHy8lvxoWsu+vfqgUfFg9+7v50fTGlrVGDF0/ZrOMVcaAx0dC9ZeQYFOIVV2a6lUlwZRqmybDparrLJKbLzxxvHJT34y/uM//iM7eP77v/97vPTSS/GVr3wlzj333Ojb9/0dNi10tN1222VVQH/0ox9lO1i6sXOXXXaJvfeu/iJCgJXV+ydJ94sf//K3sfADyYgVSSfI083YeX32s5/Nqu2mCqTpQ+4jjzySzb3f//73ozNIF8A++9KrMWHS+5XRKxm+4eDsZvz2PHY1UstNJL+948HqThDnvIHmM5/5TFx22WVZULfJJpssrkifqrOuump1J2U68iaSH/z8+pg1e26u16TFONKiHHl97Wtfyz7spUqz8+fPjwceeCAuuuiiTnPhXLqJ5JkXN46/jXsx/wniKhZpTPHXllsu+WFrrbXWyoL+pR9v2E0kh+0TP7vuD7lfk24or+bEeopJR40alX2wf/fdd7PkX0oC/+53v4vOIN1E8sIrk+Kf09/O9fxU2TstzpTX5z//+awC89lnn531RUp8pMdOOeWUWH316hOLHXUTyd0P/TX/DTRVnCBukebC9OF37bXbv4BDe5wkvfTKG2PBgoX5TxCvnr+ITWr3JZdckh3z0omOlOz68pe/nFUjT8eDRks3kTzz4oR4ccKkdj9B3CLt8ykG2GyzzbKC0Gkh3lSV/Nvf/nY0WstJ0utu/Z8OOUHcXp/jO/omkvGTXo/3ZszKfYI4LdbZXu1Pj6Vi4S3PnT59evb3dKzcfPPqF7+p9SaSR554Jv8NNFXehFypD9Jng/PPPz9+/OMfZ3mZlpgx9cEaa9S+iEnemyHSBfT/dc0tuROwo9MNNFWcIK7U/u9973ux6aabxogRI7IxOXbs2LjgggviQx/6UF2Olekmkudefi2mvPFm7htoql2ksVIfLB0Xpj5I6hEvttxEctt9j1Q1Zqo5QVyp/V//+tdjp512ysbA3Llzs6T5z372s7j00kujo7WcJP3hz2+IufPmd8gJ4krtT3FiOg6k+PBb3/pWzJo1K8vVpvlgm23yFz2pVVow6+kXJmTxQO4TxFUWscmbF0gxY1qYqx43kC1xE8nBe8VVv70z/8JMVZ5Yr9T+FC+OGTMm2w/SCaGJEydm+YSRI0fGsGHVX9RbrbRw3kuvTo63352R6/mpeEcq4tFe7X/sscdi/Pjx2TmLN954IzsupL//6U9/inpIN5GkBRQffOzvuW+gqeYiuzy5oUbGgy03kfzkv2+uLj9axQniPH3QyJgwLaCa8qOvTn4j1/PTBRLV3ECTp/2NjAfThePpJpKb7/xT/hPrVdxAk6f9jYwHW24i+cHPb4hZc/LlRw+osohNpT7o3r17Q+PBdANJigOeeDpvfnT17IbivPKMgUbGg+/fRLJ3XPGb23I9v+lf+dFqilXn6YNGxoTpJpIXxk+KadP//8VaK7LdliNy30BT7XmSRsSDLTeR3PPw47men4q6HlNFfjRP+xsZD2a5rsP3jUt/eVPu/GhaWCQV+c0rTx80MiZMN5E889KEeGnC5Nz50VTkub3OkzUyFvxgfvQ3f7gv95hJRb6ryY9W6oNGxoItN5Gkc+UzZs7OfX69miI2ldrfyFjwgzeRPPrks7lvoKlmkcZK7W90brDlJpLLrv595L08NcWP1RT5XlEfNDoWbLmJJOVHp/5zeq7nb7XJ0KoWaaw0BhoZCyZpgaGD9twhbr/v0VzPT3FgtcWqK/VBI2PB/9fenUfbWdf34v8GURRkkFEIBEEGhQSEhCGBJCeJZCAEAUHscLV16PXeXvXeX//rWv2jq/b312/VWtvqbSuIVmuVyYqoiPOAWqljcSwqo/PALGT4rfcTdnKyOXufZ4cDO+f5vl5ruVDOBvPss/fzfL+f72dIfDTr2zddflV5uGV8dN3yM8rBI8RH2+aMjGMtGBm4+q3v/7B86/u3tXp9GiqM0qRxuusfd2ywV0Tyz9d+tH0BTeKjIwyrnu49GHd8MIOXb73t7tbx0SULTyzHjlBA0yZvaJxrwjScTBHJ5778zdYFNOePEB9tc/3jXA8m1pW1wN+/89qyeXO71UDO19sW0LS5/nGvBycWb42P3n53u/hoGmyNUkDT5j0Y55owTXbOXXFmef9HP9fq9bn/veS8idbx0TbX3x8feTLXg7mvZ22T+OiDbeOjZy9sGu611eY9GOeaMGvbW773w/L1b9/a6vU5Jx6lCLnN9Y9zPdjER9ctL5e970OtXp9VcO6bbeOjo+TPjms9eO7EGeX7iY/+6jetz9dPHGGITZv3YJxrwnlzDykrznxB+fhNX2n1+uQLjTKsus31j3M9uPtTMlhjZXnz268uGze1jI++cMlIQ76ny6Eed3wwDRe/9V+3lVtvaxcfTd7g2SPER6e7/nHnDmYgUfYE77v+kyN8ZlY0f52p92Cc68EmPrp2abkt8dEH2sVHJ848uRw5Qnx0uusfd3wwg8nSgPfLX9/ayGA6yR8fpUnjsOvfFWKDeaYnF/Afkj86Sny05bDqNp+BcccHVy89rXz3B3e0jo+m/miUJo3TXf+4cwcPO+TAcs7SReXDn2ofH71khPho21qica0FtzYhX1HedPmV5eFHtg8KGGbdxBnloBGGfE/3How7PpgzouSMJDbQxhGHHjRSk8bprn/cscHkAl64Zml51/tvbPX6NCIZNT7apqZsnGvCnBX/4Pa7y2/uvb/V689aOL9pTDOT1z/ONWGaaS9ZOL98/uZ28dHkTWb9OJPXP871YD7L2RP9/T+3j4/mO5Pvzky+B+NcE+ae9u1bbyt33P2z1vVHozRpbHP941wTplY6z7YP3Pj59vHR9WnMtNuMXf9Ya0nmzGnWNm+87Mry0G/bNfbL2imNCttq8x6Mc024NT76o/KN79zauv4oa+gnorZ6HGvC7G2yx7n8yg+PlD+aXJOZfA/GuSbMGUnio7/49T2tXn/aSc8rJ4wwxKbN9Y9zTZgYR2Idn7jpq61en0bFeRbOVH39uGODvVjXm6+4umza1K6WJGfFow75HvYejDs+ePaj8dGsidsO+T5r0YIZu/5xnxUnF/j8VUvKlR/6VOvPzEtGjI9O9x6Mu5YkPTPeeNn7yv0PPNR6/ZizlZm4/l0hPtgMG1xwXLn5G99t3Z8pZ2sz9fsfd2ywFx/9x3+5bqT4aM5YZ+o9GHd8cM2y08r3fnBH+cnPf9V+iM1ODPmertfMuOKDyX1IDsRHPv3vrV6fPkvJsZiptcC4Y4NP6dXXX35VeaRlfDTrx+TajGrQezDu+GByoLIn+u4Pbm9df5SBZjPZa2ica8LkwKWZ9Lv/7WMjxEdXNjl3M/kejHNNmGbiP7j9x+We+9rFR89etGCkITZtrn+ca8Lkwqap/E3/8Z+t649GiY+2uf5xrgeb+Oj6FeUt//z+srllr50L1y5tcq9n8j0Y55owOfGpJbnjx+3io88/Zl6Tez+T1z/ONWFyAdM75rqP3dTq9U/r9dppGR9tc/1jrSVp4qMTTf5o2/hoYoOjDPkepe/eONaE6SWZtcA3v/uDVq/PtacGayavf5xrwtTGZY/z9rbx0fTaWb9ipPhom/dgnGvC1Eim7+Ivf31vq9efvhNDbKbrvTjO9WDTS/KMk8unvvi1Jyw+Ot31j3MtmBrpxEf/9h3XtI6P7swQm+neg3GuB5eedlJTT/fDO7b+f7apP8r5+kxe/zjXgxk2ml6SV3340yN8Zla27kU+3fWPOz64tRf5svLXb7uy3P9gy/joklPKESMO+Z7uMzDO9WB6pqR3yn98s1189KD9923O12fy+se5HuwNG/zH91zX+p/J+Xp6mM90D+JxxQczfDzx0Z/+4tetz9dPGnHI93TvwTjXg4c/+6Cml+RHP7P13jOdPXciPjrd9T9Za8HxjgPlccsDMjeI/CdJB1k45b+/6lWvan6eaY433HBD+da3vlUWLlxY/uiP/qj5T26sPdnMX3fddWXevHll1apV5Zxzzmm+ZElmHTXoDzDbJQEyiZBtC2hGaTAUWdhde+21zX335JNPbhYAuSe/5jWvKbuCXhFJm4KArQfEo0/onu7ZNW6jBLlGPSB+05veVB566KFy4YUXlkMPPXTbf17/+teXXUWviKTtAJ804xhFCmfyeT/xxBObIaBvfetbm0X/X/3VX5VdRQ782jSUzgFxkgpGOSCeDZpJzS2DXGnGk6DYKPK9z0Y/G6HVq1c3gfEbb7yxbNiwoewKthaRtJvU3GtQOYrc+6+//vryla98pQlupLlU7glvectbyq4iSYApkGsjTRVGOSCOL3zhC+XrX//6kzqBfNQikrZJgHlenDZCAU0ksJfvwJ/8yZ80wd8cgKxdu7ZcccUVZVfQKyJp0zAlB8SXjHBAPPlZkABP7gMXXXRRmTt3bvO5yF93BU2Q63ntBu6lOeMoB8QztY9/wotI1i5rXUAzygFxm+u/6667tv385ptvLtdcc03z388999zyZMkar83AvV6DoVEOiNu8B1kzbtq0qVkzTV4z5vvyZOgVkbRdO6dZ70xef5ow5jAsz8n8/Te84Q3NwUAadj8ZRmmYkgPiURoMzZY9UYpI2g7c25kD4umuP8+J/M4XLFjQNOf8yEc+0hRe5e89GZqBe6uWtHptkiXTtH0mrz/f949//OPN+5AYQtaK+T5cffXVT0qstldE0qahdHNAvL79sOpRvgN33nln+eAHPziWNWOviKR1Ac2IB8TTXf+f/umfNslDOSxKYsill17arBs/8IEPjLzuejxFJHNaDvDJ8I6ZvP4civ/5n/95kxSQdXL+9+c///lmL/VkSQPFtgP3cqA8SgFNm9jQuNeDvSKS1gU0Ix4Qt3kPxrkm7BWRPLVtfHTdaAfEba5/3OvBFJGkoW4baTKWe8FMXv+414MpIklD5TbyDMgwt1HMhhhxiqTTWLvN9yXD/DLUbyavf9zrwRSRpLF62waVGeo4ijbvwTjXhL0ikjZnP1sLaNqtnUf9DoxzPZgikiQNtY6P7rXnjF7/uNeDGayRARttJCaQ5sOjaPMejHNN2CsiaRMfbYZVN+cJu83YOdm414K9IpIM2mkjw70TIxzFdO/BuOODvSKSNhIbbrt2bnv9414L9opIMnBrOnkm5ZmR78JMXf+414K9IpK2a7ysnUctoNnVz8sT78nAvTbxnpwRZu08yvpkuusf91qwV0TSduDezsRHp3sPxh0f7BWRtJFcgcUt186jfAfGuRbc2oR8WauG0vm+ZC0wSnx0uusf91pwchFJ27VzcodGMd17MO74YFNE0jI+mgKatS3XzqMY95owRSQZxN3GJetHK6BpY9zrwV4RSRunnNg+t6Ctca8Ht8ZHJ1rFR/d8xh7lxSPGR9sY95rwzFNOKMe1HLiXtXOajXVJ8iYvWH12q9ceOfeQZljxTBv3mjDX36ahdBMfPW+0+Ggb414PHnf0Ec33oI2lp59Ujm6ZWzCKca4Hn9aL97R4r9Ns9LxV7YdVtzXuNWGeg20H7mWAT3JnZtK414O9JjttB/gsbLl2bptDPe61YD77yRdoE+9p4qPJHx3h3jTd9Y97LRinzj+2yRtp26By1PjodO/BuNeDzcC9de3yRw875IDWa+e21z/utWAkLtJm4F4vPppnR1fqKCJ5g2my00bWDG1zC9q+B+NeC26L9+zWMj66+uyR1mjTXf+414Kx7PSTmrV+G7n+fUeIj7b5DoxzLRjJHz+vZXw0A3zOPHW0+Oh078G414K9/NHs+afz1J2Ij053/eNeC8aC5x3dnJe2sWrJwnJ4y9qrUWrKxrkmTD5czsvb1l61XTuPcv3jXhOuW35663rhxJLzns3k9Y97PZjPdGL/bdfOC44fLT7a5j0Y55ow97Ss8XKPaxUfXTtaLclsqCtN8+VjWw7cO2/Vkla1V6Nc/7jXg1nbtI2PNg0qTz9ppH9/m/dgnGvC/PsvWHN2q3rh5vuS2qsW35dRvwPjXBMe/9x5TS5IG9k7ta29GuU9GOeasJcP1+azlr3zeSvPnPHrH/eaMLGOxDzayHMgsZSZqq8fd2wwEuvKeXEbiaGd2nLt3PY9GPdaMN+xS57A+Oh01z/utWCknu6EY9vVC6+dOL117VXb92Dc8cGmXrhlff3cZx9YVrVcO8+mPhupq20zcK85TzhvtPjodNc/7rVgb+BezkDbWHzqCc3Z6qiGvQfjjg/m7PvSlvHR1ByN2munzXdg3PHBrPFGio+26E3V9vrHHRuM5MC0XeMlt6ZtbkHb92Dca8F8z9JMuE0+XHKr0qOvzfdllF5D414Tppl0236KL0xuwbPb5RaM8h6Mc02Y331yIttIjuWa5e2HVbe9/nGvCdNUPrmxbYdVt6m9GuX6x70ezFCBDBdoI/ki849rV3s1W3qOJd7zkpbxntReZe08yhqlzfWPe02Y4SJt+yluWJXaq31m9PrHvR4cpZ9i4mJLT1vwhHwHxrUmzHuc/qt7t8iH25n4aJvrH/eaMDVy6SXWRvKH29ZejfIejHNNuDXe0y4+mtqr5JGParrei+NeD6aXYNt+iqnBbdObapTrH3d8MNeeoYNt5Jx01F7kbd6Dca4Ht/Yin2gV78k6MPunUeOj013/uNeDi046vhk+2nbtnLyJLvVfTc+MDJ9tW3u1cvHo8dHp3oNxrwfTO6VNvfC2+OiIvcinu/5xrwfTTzH9J9tIT5705pnp78A444OJ92TgXpte5IkLpjfTqKZ7D8a9Hpw48wWt+ylm7dym9mqU63+y1oJztmxpOZIbACqxefPm8g//ct3QSc158P/vV1w8UuPd2eRLX/1Wufojnxn6mt/ZsLKcvBPBgNng3vseKG+87H3lgQd/O/SA+A8vWdfZIZb/et0nylf+83tDNwyv+4OLRhpMNpt8/0d3ln96zweHviZD/HYmWWo2eOSRjeXNV1w9dFJzDohf+/ILOzeYrOdjn/uP8tHPDp/U/OqXntcED7roZ7/8dfmby68qj2zcNPA1acaVAGoXbd6ypVz+3uvL935458DXpBnb/37lxSM13p1NvnrL98t7PvDxaRPHRx1MNls88OBD5Y2XXdmsCQbJweAf/c55T1oi25Pt2hs+W77wlVuGHhC/9uUXjdxYYra47c6flLe869/KsLBZgoejFpPOFhs3bSp//85ry10/+cXA16Q57+v/8OKRGu/OJp/50tfLBz/xhaGv+YOL147ceHe2+NVv7i1/fdmV5bcPPzLwNUkU+70LXtjJPVG++++85qPllu/9cOgBceICozbenS1y7e+4+oZpCy0yrLeLHvrtw+VNl1/VfBeGHRD/j99/0ciDyWaL6z/xhfLpL3196AFxrj9Jpl10109+Xv7uHdeWTZs3Dy0+35kCktkSH/2/7/5A+dGdPxlaQJM90Uw33t1VfPGrt5RrPvLZoa/53fNXNcUGXXTPfQ+Uv37b+8oDDw2Oj6bBVtZDXVwLRPbE2RsPkpjY6//wxZ1rvNvzvR/eUd72r9dP22wwQyq76OHER99+VfnZL38z8DUpJP5fiY/OcOPdXUVio4mRDpJv/qt/57ym+LSLEhv/m7dfVTYOiY+m+LxtQ6rZGB+97F+vb85KBknC9P95xSUz3nh3V5EzspyVTZc4niTbLrr/wYeatcC99z84tIDm1S9d39n46DUf+Uz54le/VW189Ed3/Li89d0fGBofXbH4BWVNy0YsszE+mj3h3T/9xdACmtf94Ys7Gx/99Be/Vq7/5BeHvuYPL17bNKXqol/++p7y15dfVR4eEh/NIJbfOX9VJ/dE+e4nNvit7/9oaHz0/7zi4pEa784m//ndH5Z3XnPDtIUWKb7tanw0ZwS/vue+oc05/8fvdTc+et3Hbyqf/fdvDPx5Ciz+5+9fMHLj3dniziY+ek3ZvHnwWiCf/1GHNc8WiQv/33f9W7ntrp8OLaDJOdEojXdnk+QKJGdgmJwTjtp4d7a45977m5yRB4fER3NO/PIXr+nkWiD+5d8+Vr72rf8a+PMUECZfYJTGu7PJd39wR7nsvcPjo8kXSd5IF2Ud/DdXXF1+PiQ+mv3w/3rZhSM1lphNbvjMl8vHPz88PvpHv7th5Ma7s8VPfv6rJod4WHw0cbG2w81nY3z0bf/6wfJfP7pr4GvScDdnpaM23p0t/uOb3y3v/eAnh74m8fHEybvo/geSP/q+ct+Q+GjOR16V+GhH1wJXfejT5d+//u2BP9/9KU8pr/2Di3aq8e5skFqy7AmGFV2vXHxKWb1stGZ7s0Xu/3/7jmvKj3/2y4GvyTl56olGbbw7W3zqi18tH/rkl4a+5hWXrNupxruzwS9+fU+TOzcsPnry85/bxEe7KPHRK676SPn2f9028DWJByQuMGrj3dnim9/5Qfnnaz869DXJm0v+XFfjo2982/vKb+69f+Brjjj04PKa3z9/pMa7s8kHPvb58rkvf3NofPSP/9uFIzfenS3u+PHPmlqKYfHRsxcteEKGNe8q8dG3/vP7y+13/2zga5I/n/vgKI13Z5Ob/uM/y/s/+rmhr/n9C84p848frfHubJH7X+6DuR8OkoZ0L7uou/HRd7//xvL1b9868OdZByd/NHkDXfSdW28vl7/vQ0Nfs27i9LL8jO7GR9/09qvKL351T7Xx0Y98+t/LJ276ysCf57v/3393w8iNd2eLn/zsl+XNV1zT5A8NkgbFbYf3zMb46D+957py6213D3xN8kaTPzpq493Z4svf+E658vpPDX1N+iukz0IXJS6anJH7HhgcH02j+ldcem5n46P5/edzMEju/697+UXl4I7GR2+9/e7yj8kfHfKa9NlJv50uemTjxvK3V1zTnJcNcuD+iY++eOTGu7PFJ7/w1fLhTw2Pj77yJeeO3Hh3tsg68E2XX9nUFQ2S5usv3bCydDU++vYrP9zsCwZJPWniAvt0ND76jW/fWt71/hunHcy2M4PJZoPkS2UtMCw+Ou+wQ8p//70NnY2P/tuNny+fv3lwfDTX/T9fdkGZe0g346O33/3T8pZ3vr/ZGwyy9LSTyvoRhzXPFps2bS5vedf7yx0Vx0c/d/M3ywdu/PzQ1/y3C1c3A5u7KPnzuQ8Oj48eWV520epOxkezFnj3+z9WvvGdwfHR1FG9/hUXN4Pbu+g7/3VbufzKDw99zbkrzizLWg71nW3SZyq9J5M3MGx40R+/7IImf6iLsh/KvmiQfPdf87sbypEdjY8mXyj5o3kmDnLGC57fDOPoaq+df3zPB8sPbh8cH9370V7k6UPaRckbTP7gMNkT78xgstng3vsfaNYCySOtNT76vg9+stz8ze8O/PlTEx/9gxeXg0YcTDZb3HrbXeUf/+W6ofHRDKt+4dkLS63x0fShTw5xV+OjqSNJPckwr7p0fTnmObvGUL2Z9vNf/qY5L09f+kFOOfGYZojZbNXNiBZTNtb/5ZAmogBsl+Zhl0wzqfnicyc6O5QrMmRjWIP5FNB0dShXL9hx0ZplQwtoUkzcxaD45AKZYQ3m1684s7NDuXrBjmEN5jPBt6tNFXqNhTOBe9Ck5vz9Szes6OxQrphY/IKmUGqQTPLu6lCuyPc7hz+D5P7Q1QZLkUBnnvXDGihduHZpZ4dyRQLeaSg47IC4q01nI+u8S4Y0Dsk6MYnTXW06G+dOnDG0wfyapad1tulszJt7SFkx5FmfA+KuBsUjB98JeA46AM86+CXrV3S26WycddqCoQ3mU0DT1aFc8ax99y7nv3DwsJm993pGc0De1T1RruuitUuHNlCabs80251w7HOGPuuzZ1q8sJtNFSKJkHnWzxlSQJM9U1ebzsbqaZ71K5ac0tmhXHHYIQcOLRA6aP99y7qJM0pXZZ2bZ/2wA/BL1i/v7FCuOP3k5zeDp4bumTo6lCv2eeaeQ5Ph8rt/cQXx0WENlM5beWZnh3LFsc85fGiD+SPnHlKWnXFy6arc/7InGhQfTQHNpeet6OxQrli5+NShDeaXnn5SZ4dyxcEH7Df0WZ89UwbVdjk+esm5y4cWCL147bLODuXqPeuHNZjPnqmrTWcjCeHDGivvUUN8dMWZQxsorV12eqfjoykMmThz8LP+sEMOKKvO6np8dPC+v4mPntft+GjOQoc1mE8xeVeHcsX+++1Tzh/yrE9OSYrqu7onynXlWT+sgVKuv6tDuSKFwsOe9cc+Z245s6NNZ9vER1NAc+n6bsdHM4x6WIP5lUuG75lmuzSMOOfsYfHR/cq65d0c0Nnb9+dZPywvKvmlXR3K1SuWPe6owfHRU048trNDuSKNgy5cffbAn+/5jD2aZ2VX1wLxotVnl32eOSw+urizQ7niuKMOH9pgvomPdrSpQi8vKs/6QcXCWQNkz9TVprO9ZoLDGswnPt7VoVyRdVCGzw2SpiobVnazAf32+OjE8PjoumWdHcrVe9bPP+6ooXumU+cfW7oq++FW8dEOrwVyFrr/foMbKOUe0dWhXJHG4sPOQvOM6HR8dPfh8dF89rNn6upQrl4zwWEN5hefekJnh3JFzkeGPeuTU5Kciq5q4qPrlg1toNTERzvadDYyaGZYg/nsmbradHZ7fHTFNDV3E51tOts7C03exLAGQ10dyhWHP/ugoc/6gw94VlmzvJsDOneIjw7Y9895ND7a1aazkXtc7nWD5B7Z1aFcse808dE8Iy/qeHw0z/qseQbJWqmrQ7ki+dPDnvVZK2fN3PX46KDPePZKaTjZ6fjoWacObTC//IyTOzuUKw45aP+hz/rETNZXEB8dlheV2FlXh3LFwvnHDW0wv+D4o5oYalclN/iidYN77WQdePG5yzsdH92wavHQBvPrlp/R2aFccfQRhza1AsP2TMmb6arUiFy6YeXAfX8++4mfdrXpbOQsPGfigyxZeGJnh3JFciGSEzFIcinO73x8dHmTGzPIBWuWdnYoVyx43tFNc+Fhe6bkVnVVcuKyHhzkaRXER5MbOayvXvrMdHUoV6Tf2sqzBj/rc068elk3B3Ruy4taPzw++pKOx0eTN5dc+UGSY9/VoVyRHjKJDw6S/XCX80dzXResObupmRlkwwuXdHYoV6RWatizPnmDqbnqqiYv6rzB8dFezV1Xh3L1nvXpLTdIai67OpQrUjO7ZtnpQ/dMw/qTdqUX+bTx0Q73Il+04PhywrFHDvx5epOmH3lXpbdszkIHyTrwkq7HR1+4ZGhfvfTg6OpQrkgPlbOHnIUecehBZcXiU0qX46NNX71B8dHduh8fXX7mC5qh5IOctXB+Z4dyxYH779vMHBiWUzKsP+ls0N2oFju47mM3lf/vH95TvvyN73hnAB5nEUmCxsOSarteRJID4hTdd92wIpIkFXf5gHj7hn/qQ9I03ejyAfGORSTPGji0qssHxNMVkaT5TpcPiLcXkUxM2WQnB8RpwtR1g4pIajgg7m34LxiQEJWk2mFNB7peRNL1A+Ke44YUkaQZY5oydlkzfCxDGqf4PXf9gLgnz8GpnnfbGgx1+IB4uiY7XS+g2dY8YsAhafaLXS6g6UkDnUEJUV0voIk0UBpURJJGg2nS3nWJi0yVEFXDAXHveTeoyU4OiIc1HeiCbcPHptj7puFshnV03aAikm0Nhjp8QDxdEUmG9WRoT5dlv3PxgCKS7Be73GBoxyKSqQtGM7RrWNOBrheRpIAmw9u6blARSVNAs76C+OihgwtGM7wxQxy7bFgRSfaLwwZYdr2IpJr46JAikgzxzWCqLhtWRNLsF9d2d1h1myKSJMtlQF2XDSsiOXreoc1Q9xrioxnANWUBzfrux0eHFZFMnPmCoU0Hul5EkgG153Z4WPXkgtFBRSSXNPvFjsdH02RnQBFJCmiqiI+uWjLl8y77xYvP7XYD9l4RyaAmOymi63IBzbYmOwPio10voOlJfHyqIpKmgGbD8KFVXZDnXYYRTOWsRfPLMUd2t4BmW3z03OVlzymGj6XArob46EnPf+7A591Fa5YNbTrQBfndX7J+6oEkz3vuvHLayc8rXdcUjO6/7+B8mo7HR4847OCyYsnUz7vVSxcNbTrQBVtzg6Zurpv94rABll2R88CpnndNfDTDqjseHx32vDv95OeV5x8zuOlAV9YCOQ/ce69nTL1fXNP9/NE87/K7nko+G8OaDnRBvuM5D5zq9/zcIw8rSxbNL12Xe/1Uz7vdKxhWHbn21UunrpfInnjeYQeXLstaL+eBUw0fS9OBdRPdbTA0+Tzw+cfMm/JnyaXoenw0TXYuXLt0yp+luVCXGwz1nD+gyU4TH123vPNrgTzvzl409XlgGo8Ma8raBYn9ZU+UWGC/PAPSgKbrch441fMuMeMmPrp7t+Oj+YwPqpfIWflz5x1WasgfzT2vX+6NuUd23cknHDPweZczxDwruyxrnZwHTiUxgayVui45ATkrmTI+mmHVHY+Pzpt7yMDzwDVLT2tihF2WnKDEAKfKDcp+MY1pu+6sRQuaM/OpnhGJCwxrytoFyZUYNHAkOYWJndUQH00stF9iphlG0vU90QnHHFlOO6nu+Ghy56b6LefsZPHCE0vXrR7wvNtWb9jx+OhhBx8wsF4iNSYZWNL5+GhqJ6d43uUMfe3y7uePJidi0PMuuRRT5dN0SWoG8yycSnJpkkPadamXSA1pv9SaZnBZ19cCaS6dJtNTScxoqv1i9+KjyQ167O85NQTptdJ12RMnV3bK+Oh5FcRHD9hv4MCR5FZPtV/skuTIJzY2Ve1k9ovJse+6xEYHPe/Sc22q/WKXpPdsYuRTSU1peu91Xc4D02tjynrDxEc7vhbI8y5nZVNJ780a4qMvHRAfTa3loN6sXZLegum5VGt8NL22MphoKulJmV4jXZbfc3JEU0vfLzUU6T3Q9T3Ricc+pxlQNpVcf3owdFnWgU19/RQ/S6+pM0+tID66bFHTU6XfUyuJj6b36qDzwFVLFja9eGroRT5Vb7n0HBzUm7VLznjB8wc+73J+MlU+zWzS7W/wk2jTpk3lne98Z1m9enU56KCDyh577FHmzZtX1q5dW/7pn/6p+fm43H7XT8t3br29lC2l802TAWa+iOTIxxwQp9i6BoOKSNJ8u+sHxMOKSJoD4goKaLYVkfQ1lcvvPk03uh4M2XZIuuGxh6RputL1A+JhRSRpujOoOX0ni0j6Dkm3bpK7f0A8rIgkE9y7fkA8rIgkz4VBAfNaikguquCAeIcikr4mO2nCmGaMNcgzYKKviCSHYpdUUEATCXw3RSR9TXbSjLXrB8Q9aaCRNWGNBTSDmuzk+dg0GOr4AfGwIpImYN7xApphRSTNAfGa7h8Qb2uyM8UhaQ0HxJOb7PTf8zOcIUMaapAikhwU9x8Q19BgaFgRycqzul9AM6yIJHvlDOupQe75SRiaqsHQbD8gbitrof4ikgzrytCuGjRFJH1N5VJAU0ODoeHx0cVTJhTXUkSShOIMb6zBVEUkWxsMrawiPjqoiCTx8akSimspImkSiisYVj2siCRDnKdKKK6liCRDvDPMuwZTFZE08dEKhrEMG8C1Zvlp5ZBK4qM5K88gtv6E4lVndX9Y9aAikm3DqiuJj05VRJICmuM6XkAzrIgkDRcGDe/smqcPaLJzwRR75a5Kg5n+IpLjjjq8+R7UIAOZ+5vsbCugqeGsdEARSQpJD392twtoejKYvb+I5OADnlXWDhjeWUuTnayHuz6selh89NT5x5X5xx9VapCiyQzm6d8rv3hd94exRO5/zQCuvmtNXOSA/eqIj65cfOpj7vmpR1p6Wh3x0RRN9tcM5Iz0pRumHtjVzfjo8sfc89OAv5a6tNQMLDi+Lz66394Dm9N3zV57Th0fzd/Lz2qwfop7/vzjjmrOS2uQ73p/U7lmr1xLfDT5MVM0UFi3/Ixy8BQNF7po6RRNdrI2SOPZGuy/3z5lw6od7/n57CduPlXDhU7GR9cue8w9P3lzxx51eKlB7vn9TeX2eeZe5UUVxUfTVK7/jn/h6rPLPpXER3MmlljQZMcddUSTR12Duc8+sLywr6lccomqio+uX9Fc82Spo8h5YQ3yWc9nfrKcGaThYg1yr8s97zHDqtfXFR/Ns2+y1NMlb6QGORPrrxnI2iiNd6uIj6bB7BTx0fNXLWnWyjVYteTU5nk4WfZI/X0Xuir3/LUTO56JZY986YCGtF3T5Mesn3hM/eDEmSeXIyuJj5564rHNvmiyxMoGNafvmuQL5Z4/ZXy048OqJ/dUSf7YZMkpTuy8BrnnL5syPrq86vho+i7kDK0GqRnpPxNLw9lBwzu7Jvf81A5NltqiDPKuJT6a50D/PT85FMmlqEHOSftrBpJD0993oatSO5r80X7JpUpOVQ3SZLr/np9a49NPnnp4Z9fknp9m44/JJVqfWstK4qNNf7Ud93/JqU1ubQ2SK91/z0/PiQwwrcG+lcdHsxZIzUT6DPT3pc1gqhokf7q/ZiB75YvWdn9YdS8+mtyA/mvN8M4MqKtBaucyhGqy9J08q5b46EH7N+fFj6m1PG9l1fHRFelLO/eQUoPki/Tf89N/OLGRGqTfXO75/S5Zt7zpTVqD81Ytfsw9P30XaomPHn3Eoc1Q2v698sWVxEfTSyU9VfrzY3JGkr5bNUj+cPosTJZ+axOLpx7e2TW556+fIj7a7JVriY+uyz2/rxf5ogXlmCPnltmu+5GdJ8E999xTzjnnnPKyl72sfPSjHy1Pe9rTysknn1w2b95cbrjhhvLqV7+63HvvvWP78934uZubv6bwo5YhEgAzekj6aBHJtgZDFSyABhWRLFlYTwHNVEUkNR0Q9yRRfnKTnZoOiCOFEmnEPvmwIE1XarHtkPTR+97WZhMTVRTQDCoiqamAZqoiktwP+hvzd10KJ3tFJM0B8XkrqjggHlREkuZ7acJXi94haS8I3BwQr6mjgGZQEUmacPYnU3e+iGTS4Ik0Ya2lgCby2Z/cWC0Hwy+tpICm55S+IpI0Y+4/LOiy3PcmN9lJM+7+waVVFJHst3eVBTSR4oHJRSQpoOkfXFpTEUnuhxnOUMMBcU8a600uImkOiCspoOkVkUwePHFERQU02w9JtxeRbC2gqeOAuCcNVicXkZy1cH4zrKmu+OiKbf+7xvhoGi1PLiLJnrA/mbrL0lgtDbcnF9BkaF8t+otItjWbqCg+2l9E0hTQHLxjMnVNRSQpoJl8ZlBLEUnvXKgXH80Q31r0F5FkeHOGONeiv4gkydQ5K6wqPnrWqTucC51/Tj0FNFMVkTQFNIvqi4/2ikgSF31JZfHR5ItMbqw2sfiUMu+wOoZVbysiWbe9yc6B+9dTQNOT5sOTz4USG6ulgGaqIpLERk+upIBmqiKSpoCmkmHVk5vsTD4XSkFFLQU0UxWR5BnQP7i0piKSJj66oa746ILnHV1OOXH7fS9n5c898rBSi6bJzrnbm+zst88zy/l9g0u7bt3y03e47124dmnZe6964qMpHJw8eOL5x8xrmmvUotdkdYf46PqJquKjyZubXDi4ZulpTYywFrnvTR7Me+jBBzQ5tFXFR9dsb7KT/51zs/5mE112/HPn7TB4Ik3WclZUi/yuEwvq7YH2rjA+mu98vvs9OSvOvaEWueevmTSYN8+ExQt3bMzfZXnmp+nktvhob1h1XzPeLkve/AnHbj8XWrHklHJERfHRrP0znKvnoP33fczg0q5L3vzk+94l65eXPZ++Y7OJLsu50NmTBvOmudBJFeWPbo2Pbh9CteejDYZqWgtkMO+8ww7ZIae6pj4VOReanDefWHF/Y/4uy2f94nXLtt338l3YGieoJz56Ul9jtZwZ5d5Yi5wJZvBET3IlNqyqLD46cUazBurJ2ihnyLXIudDkvPmsjTOcrar46PoVzV5oW5ygsvhoBk9Mjo8mp7rm+GgaEU/Oqa5hLZBYWO++18RHz6srPvr8Y47cYfBEYqWJmdYiucI7xEefuWfznahpT5Sc8cn3vVz/vhXFRxMbnjx44tjnzC1n9g0u7bI88/Pszxlp5Mw0a4Oa4qOnnXR8c0bek7PzDKqpRe57yZHoSe5EcihqksETO8RHz51ocmlqkbVwmg1P7jmRgWW1SI5cBk8kZy6SQ1fLsOqeNBtP7lBPBham10Ytct87tz8+2teYv/Px0XOXb7vv9epMe3GCGiRnfnJfkcTHM8C2qvjopF47qamY3HOiBqmdmXwulJ4T6UFUiwwfyhCintRW9Q8u7bL+IVTb4qMVrQVSQzn5XCg96Cb35O26nAtlT9CTGtva4qMZTNW7723rRV5RfDSDySbnzS8+9YRy3NHbe/LW1os8vRZq67WT2OBj4qN7b+3JW4P0VElvlck9efsHl3Y/Prp9BsNTK+xF3p83n+dgejDVYp/ER9dsj48efMCzHjO4dLaq51P8BHrlK19ZPvGJT5TDDz+8fPzjHy933nln+dKXvlTuuOOOcvfdd5e//Mu/LE996ngWTrff9dPynVtvbxZwSfwGYGcmNS/bdkA8+aCktiKSHJSsXV5XAU1/EUltB8SRRPmmeG633ZomG2m2UZscCKWIpDkgrqzBUORgJIUzkWY7NRXQTD4kTRFJDogzubs2vSKSpoBmw8qqCmgiv/teEUlTQFPRAXF/EUlzQLyqrgPiSAF1bz+d5os1FdBsb7KzskkOyQHx5KGttRWR5IA468GaDogje4HegVAComnGW5NeEUkaq2wtoNnedKsWORjIAUHeiySO13RAvK2I5NEm5DkYS1P22qSIJIUU+1RYQNNfRHJhZQfE/UUkGcpR0wFxz6JHi0hSQNOLkdQkTXZ6RSSrliysqoCmv4gkw5kmD22tRRrt9opI8kysaVh1fxFJEmYzrK02abidBgsZ0ldbAU3kbKTXhLwpoNmvngKa/iKSZmhrRQU0/UUkvUbENRXQ9IpIegObc14yeWhrbUUkGdrcOy+pSVNE8miTnXwWaiqg6R/ElOHtp55YTwFNfxFJjQU024pIHo2Prp2oq4Cmv8nO3GcfWFZNGkpQiwxkzGDG3SqOj6YB85xHhxKkgKA2vSKSfZ65V3nRpKZbNRaRJD66T43x0UeL54476ogdhhLUVkTSFNCsry8+OrmIJOclk4e21hQfzdlI1kFrlnWjgGYUxzxnbjlr4fytw6rXT1QaH82g+jlNvkj2RbXJnviIQw8qe+359CafvLr46AH7bRu+cP6qJWX/yuKjWwc2L2+++2kskwGFtUls9KTnHb09PlrRsOrYa1J8dHml8dEMIkhzsZyPTB7aWovkjveG0+azkOdBTbY22dn63U+jwclDCWqRe3+eAXkW5JlQW3y0yR1/dDht1gTJm6hJ02RnzdbmYsmXWbm4vvjoiWmys+D47Q2GKhpWva3JTpqQP5pPnvy52qxetqiJCSQ2UFuDoUgsqDecNjGixIpqsrXR6ETz3U/+9Okn1xcfbYYvHH1E8x5UGR/de69yQS8+unRROazG+Og52+Ojk5tu1eLYow4vSxae2DwLL6kwPtp89xMfnTOnWRNlbVSbZvjCsw9q1sRZG9cWHz34wGeVdY/21sh5SXJIarLbo/mj+e4fPe/QclaF8dHEQhYcf9TWGMn6CuOje26PjyZGllhZbRITTe5oYqSTh7bWIjHxXm+NNKRPTnFNkjP+0g0rmzOSnJXUGB9detqC5nOQWoKLK6yvz9lor7dGBpPk7LQmW5uQL2ueBzkznzy0tRbJkUiuRHImMsA9ORQ1efqk/NGzFs1vcmhqk94aaTo8ud9GTZI7/sJHhy9kT5iBdTXZNqj+qbs3OZSnTRraWov0FEjueHJomxhJZfHRxAV7A5tzXpKc6trk3pcc+uTSJ4e2Nhk+kiEkTf5ohlXXFh/tfffnzGnyyTPAujYZvpDnYdOPuML4aM5Ger01cj+YPLS1Bk2exPqJpqYy/TaWLJpfapPeGuk5uHuv105Fw6ojZyMZShjZE6ffRm0ylDL5Ywfuv29ZN1FffDR5g+mtEOk9WWN89NJHv/vJJ588tLUW6a2yLT5aYS/ynI2ct2pr/mjOSNJ7pyb5fafHUnrR5xnQyyevyYLjj26G1W/tRb6iM73I52zZsmXLuP8Qs9nNN99cFi1aVHbffffyla98pcyfP3ML5TdfcXW5974HH9e/44EHHyobN21qPrC1DRIBmEkPP/xIc0BS2yK4J8+SXPlTKksW68ly6ZGNG8vTxjRoc1fw8COPNOuJWr8DmzdvLps2b+7MJmBnvgMPP5LvQL2fgdwDshms7YC4x33QfXDTpk0lwYPaEsd3+A7kPlhZs8X+tUBiH7UlzfZYC1gLbNy4sczZbbfqiml7rAWsBTZt2tx8DmobQtC/J0rCTK2yFspBea17os2btzRr4tqKJyb77cOPVB0XEB8VH609PprYaPZF4qP1fgbER90HnZVuar4L4qP17onER52VOivd2MQExEfrvg9WvScSHxUfrT4+urn5HoiP1nsf3LhxU8mlyx+1Fqj1OyA+KmdEfFR8VHxUfFT+qPxRtSRqScRH1dTVHR/dVNKJQv5ovXEB8VHx0erzR8VH1dfXflYqf1TOSOX19Zu3bGnqCqvuM6LXTvM+yB+t+DtQ+VpAfb21QNYC4qOVrwUqvw+Kj8of3RoffUozpK/atYD80brr65v46JzqhrH06LVjLSA+ak8kPio+Kj6qF7n80brjAuKj1gK1rwW27KK1JHs/8xnltS+/aKf+WYO5Hqc/+7M/K294wxvKi170onLttdeWmfT//t27yj333T+j/04AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgF3ZPs/cq/zpH//eTv2zu8/4n6Yyt9xyS/PXxYsXPyET1x6PBx58qGzctKmZKPmMp+8xY38uAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAIAnyuOZ32Qw1+N0zz33NH/dd999y0x77csv2ul/9va7flr+7p3Xlt3mzCmvf8WLy4HPmvk/HwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADArsRgrsdpn332af76m9/8psy0N19xdbn3vgd36p994MGHmr8+5SlPKf/w7utm+E8GAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAPDE2PuZzyivfflFO/XPGsz1OJ144onl6quvLjfddFOZaRnKdc999z+uf8cjGzeWR+7bOGN/JgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAXZXBXI/ThRdeWP7iL/6iXH/99eWWW24pJ5xwwoxOXNsZDzz4UNm4aVN56u67l2c8fY8Z+/MAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADzRdnZ+U8zZsmXLlhn901To0ksvLe9973vLvHnzyjve8Y6yfPnybT/7yU9+Ui677LLyute9ruy1115P+J/l9rt+Wv7undeW3ebMKf/Pq19SDnzWvk/4/ycAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAwK7AYK4ZcM8995QXvehF5ZOf/GTzv+fOnVsOO+ywcvfdd5c777yzZPbZr371q7LffvuVJ9rl7/tQ+c6tt5eF848rl6yfeML//wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAdhW7jfsP0AX77LNPufHGG8vb3va2MjExUR544IHyta99rey2225lzZo1zd/fe++9n/A/x+13/bQZyrXbnDllxZJTnvD/PwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAXcmcLVu2bBn3H4KZ8cM7flyuveGzZe4hB5ZL1k94WwEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAqhjM1TGbt2wpjzz8SNljj6eN+48CAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAPCkMpgLAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAIBO2G3cfwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJgJBnMBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANAJBnMBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANAJBnMBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANAJBnMBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANAJBnMBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANAJBnMBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANAJBnMBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANAJBnMBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANAJBnMBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANAJBnMBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANAJBnMBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANAJBnMBAAAAAAA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"text/plain": [ - "\"Output" + "
" ] }, - "execution_count": 26, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } @@ -844,7 +855,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 106, "id": "172023f9-164e-43bd-bbc8-39d87628287e", "metadata": {}, "outputs": [], @@ -859,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 107, "id": "e6794cf3-7fbe-46a5-bdc0-5faad1235365", "metadata": {}, "outputs": [], @@ -900,7 +911,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 108, "id": "c5e5f7a4-01f6-4a02-9114-3bb0e24be1a2", "metadata": {}, "outputs": [], @@ -1015,7 +1026,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 109, "id": "032bf312-4bf4-40f4-81f0-2ae8a719b98b", "metadata": {}, "outputs": [ @@ -1023,10 +1034,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "layer fidelity 0.6544189744623636\n", + "layer fidelity 0.5454643821399414\n", "\n", - "The corresponding CVaR aggregation value is: 0.38642994025160377\n", - "To mitigate the twirled noise, increase shots by a factor of 2.5877912031063173\n" + "The corresponding CVaR aggregation value is: 0.2568730767702702\n", + "To mitigate the twirled noise, increase shots by a factor of 3.8929731857197782\n" ] } ], @@ -1057,7 +1068,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 110, "id": "382e8acd-d0d0-4302-99aa-b64e5dd31e17", "metadata": {}, "outputs": [ @@ -1065,28 +1076,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: -10.010143313246703\n", - "Iteration 2: -10.778378196890142\n", - "Iteration 3: -33.11648157016098\n", - "Iteration 4: 23.37766951385026\n", - "Iteration 5: -8.456279717029192\n", - "Iteration 6: -20.250216714385534\n", - "Iteration 7: -45.87610233094513\n", - "Iteration 8: -20.4522784936772\n", - "Iteration 9: -49.63202770645467\n", - "Iteration 10: -49.10586683562599\n", - "Iteration 11: -49.15188090417742\n", - "Iteration 12: -45.84209193245048\n", - "Iteration 13: -49.634028318131016\n", - "Iteration 14: -48.542306019104984\n", - "Iteration 15: -49.8580968258604\n", - "Iteration 16: -49.7000485034442\n", + "Iteration 1: -13.227556797094595\n", + "Iteration 2: -13.181545294899571\n", + "Iteration 3: -13.149537293372594\n", + "Iteration 4: -3.305576300816324\n", + "Iteration 5: -12.647411769418035\n", + "Iteration 6: -13.443610807401718\n", + "Iteration 7: -12.475368761210511\n", + "Iteration 8: -15.905726329447413\n", + "Iteration 9: -18.011752834505565\n", + "Iteration 10: -14.125781339945583\n", + "Iteration 11: -19.693673319331744\n", + "Iteration 12: -21.175543794613695\n", + "Iteration 13: -21.805701324676196\n", + "Iteration 14: -22.121280244318488\n", + "Iteration 15: -20.02575633517435\n", + "Iteration 16: -22.399349757584158\n", + "Iteration 17: -22.569392265696226\n", + "Iteration 18: -21.877719328111898\n", + "Iteration 19: -22.79144777628963\n", + "Iteration 20: -22.437359259397432\n", + "Iteration 21: -23.021505287264777\n", + "Iteration 22: -22.69742427180412\n", + "Iteration 23: -23.12553129222746\n", + "Iteration 24: -22.893473281156922\n", " message: Return from COBYLA because the trust region radius reaches its lower bound.\n", " success: True\n", " status: 0\n", - " fun: -49.8580968258604\n", - " x: [ 4.262e+00 2.820e+00]\n", - " nfev: 16\n", + " fun: -23.12553129222746\n", + " x: [ 2.766e+00 1.080e+00]\n", + " nfev: 24\n", " maxcv: 0.0\n" ] } @@ -1129,14 +1148,15 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 111, "id": "761821cb-9a0c-4efb-806b-75513302d34a", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
" ] }, "metadata": {}, @@ -1171,7 +1191,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, "id": "7e8af29e-c99b-41f2-b6dd-2be471e1af21", "metadata": {}, "outputs": [ @@ -1179,7 +1199,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "bitstring (int): 1056839274160662489278455764297, probability: 0.00038654812524159255, objective value: -71.0\n" + "bitstring (int): 283561207335785714592526814041, probability: 0.00025693730729701953, objective value: -43.0\n" ] } ], @@ -1213,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 113, "id": "5ea9e6aa-4297-4687-b484-1695d415bad5", "metadata": {}, "outputs": [ @@ -1221,8 +1241,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Result bitstring (binary) : [1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1]\n", - "The value of the cut is: 91\n" + "Result bitstring (binary) : [1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0]\n", + "The value of the cut is: 77\n" ] } ], @@ -1272,14 +1292,15 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 124, "id": "852dfeed-2871-4ca1-9754-15c95293198e", "metadata": {}, "outputs": [ { "data": { + "image/png": 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q8fvXYcmmXzAodhzGDZgp1/Hv2tqb71YozKxA4qqjOJZUJJbMPESaMRHFOZXISilGwrIjiOzji97jOsPFw7wtMx0jG2qtMXvE/8HTwxN2DjYY0HMk3vzhCSzdMgc3TH7Qoo9DVXktrBuscccDt8InpCmxUVBQMA+VGVC4aEF3PrYQLl26FL///rsEf3YTMP3MtLsmwmPLXmpqqkF0yJ31hg0bZEft5+cn6WZzCOriCU9/ZyxbvUJ2p32OewuY4pIhV+Fgxj58Nu91DOutFzLSe8DJ3gV33Hm7gQhojon8mxoR0BAdHY1du3ZJ+j88PLxFoRyDPksKfI2m+OGrOWB1xHjuAsHjJ+qP/yT2HdqB31Z9jp6R/TFz5E1yHWv0hPFsBc3EaMuGbTh2KAeujl6SYRg7cJqQIWLV9vnYfzgBBSW5qK6pgpuTF3p37Y/n/vMoIro1N3WipiPEMxrODq5CBAhmUSKCuwmxokaDz23Ox8HavgG6ygakbs9RZEBB4SShyIDCRQ0a8bCdjgr75ORkCbIkCDQi0kAywLkFxuDvFAuOHj3arHKfYOqbIro3v9gMZ0c3RHWKNXs/9uvfddlTWLzxZ6zaPk+8fkP9umL6qGsxcFxTosF0PgM1/7bWOUBowkXqHJgVsASWApgZoP7AHLr1jMKSP1chMzsDIUEngnBiymZpLQzy1TsipmUk4cdlH4i24JoJ9xp8CyrKqlFSVoyDeaWIrvUT8kQTIzsbR0R5DUF3L0fklKVjzc55yC3RmxgRGfmHEeQbit7Rg+Fg54ScggxsSlyFa2btxIJlc+EX3LTsUVlWjXoXKzg4NV2i7G3txachp/AYQgOjLPo4+HmEYHrx9YibEKpmKygonAQUGVC4qMF68aOPPoq1a9eK6M+chz9thXnRwKDLWjNT7XQfbAkxAwPw+nPvInlrDupqG2DvaD59HuIXjjtm/l1vRVxaCzt7G4y5vmuzNDlHI3P4EfUDxjbItCTmcTFrYCqINAbJAgWPYWFhZm+/7c5bsWLZGnz256sY0XfScQHhTiSn78KAHqNF8FhUmo9vF78jpIV6gt0Ht8pjeexV5TrAoRrfvzof73/8jpgeFRYW4e4pL8CpwQfOHvaGiY+aiRH/hnFaX0NoUDS+W/RffPbmHDz55l1NbvN08cfR3FRY2TTtaDiac1D+rWkwLPo4xP+Jrxa8icGTu2HiFeYzNgoKCiegyIDCRY8BAwZIkNy2bZsEeYryjHfdGhjAuDNnmYACvRkzZogVcEtgdmDIZV3kJwmBrroG9k62UlNvmoZvRG1VnRAGR1dbjJgVjU7dmrc13nLLLTIT4eeff5bsBUWOLGFQRMjyAHUAlkobFEvyJz0VLDkQ0nPh4w8+xSvPvImNictRXVcpQ5UmDZ6F0f2m6Z+vLA/VNZWGLgLj10Dc9+C9mPPqHDE7+vTTT1GYV4zKwnp4dbIzvGZjEyNL8Hb3FT1BZnqe6Ak8A04IOofFTcA3v32Muas/x6i+0/Saifj5KK0skdt1dbWt+jj86+vHMOf3nxQZUFA4CSgyoHDRgwFqxIgREtgpwmMfPnf92hhgLS3PC+vtrM+z5t5SOt4YrPkPvawLAiM9pBc+70gZaqrqyC6kF54/eQwkCVH9/dFtSJDFCYAM1l999RWeeuopIS8kJzwOthty4JJ2TOZKG6tXrxahoCXDIQ2jJw+Br8s72L7osByfo4ttE+ISGdIdr933TdPWvQqdTDEcNCPS4C/w97//Xbob7rjpXizZ/gOmu17ToomRZEWqy9HQWI/84hwRJvL8d/aJQeqOXAyYom8Z5GvuEzUUh3ofxeakZdiRtF6uD/GPwKg+U7E6fr7heVvycQj1j8aB1H0n9R4qKHR0KDKg0CHAYMdSwJdffil9+AxiTMUz9c7b2LZHoSDT9Eyzn/LzW1vJqN+IOF8UZFTISOOq0lrU6Rqk7u0Z6Cy3Obm2bBrE42HQf+WVV0RMyIwGywI0SCJ50eYpGJc2KHSkayEJDjsoLLUdGqPHCH2GYcfSdFSW1MDWwRb2jjZNSIFkSqrqoautk7JG/ynh6DY0sMnzDB0wEhP6zcLaxIWtmhjRwfDVL0+UC9xdvXHNpHulTXDj4t34fN6/cCzjmMxiGBp6BYbETML4IdORW5gJRwcnBPp0xtLNc+Sxvp6Brfo40FI5tzq31XOhoKCgyIDCRQxpDaxvhLWNlQQ5ZgQ48Oef//wnZs6cKYFXE9ydzOjfkwH/jm8nV7mcCihyZG89hY4cqKQJBrWyBY+PJQNer9XkNf8AljxIIKZNm9ZqWcP4OHuODIF3sCsObM7C0aQiGavM5zW+D5X8Xfr6o+uQQARGNH/uyrJauDv7iNCwV9TAFk2MnBxccfulf4euvlZaGPembZdxyVZWjairaYCnpzd6xvYUrYRNkR/y9jXAycEB4cEnWjtTju4REuHnGdSijwNJWFllCXwClBuhgsLJQGUGFC4qMD1/ODEPKfG5KM2vEjJgY2MND38nJOdsh7eHr0z1I0gCWrP3PZvQWvLYxkiiwmBPTwHjtkcSAmoDWALIz8+X42UZgGUPZgyYzeAURrZNWtIJtISgLh5yoUnQocQ8lBfWoLamDvYOtnDzcZRshquX5XkFK1ctx58bv8Gj1/0L3p6+LZoYUT+geRjQnIn//vi3l+Aw0RkxneJw7z+eM2ROSguqMC9tJ2qr6w0dBex4yMg9hKnDrzW8Vks+Dscy05FZlIbbr73hlM+JgkJHhCIDChcF6nT12PnXEaRszxXrWljpa/lM33OgTc7hUliVBuKy3g9h39oc9B7bqUl//7kGa/5syWPQ5K6e4sCCggJs3bpVBIHaqGISgHnz5knpgi2RvA+zBywJfPDBB838CE4Xbt6O6D1W31Z4Kli4dL609Wnp+tZMjIzBOr+bi6cE+a6hfYSAaEhN349fN/0XfvaR8PL2xJGcg2KAFBPWG8OPezVY8nGgF8L6hKXwDfDGbbfddsqvSUGhI0KRAYULHrt27sELT7yBfQf2oK5eBx9PfwyOHStBg2noHUlrse9QArLzj6K6thpeS/xwyfhpeOqN++DgeG4n72ngqGIG9ssvv1wCOnf5Wr3eeFQxMwJsh7zhhhtkRDHLBBQRsnOAJkTMEJxPlFYUw8rGStwGbe1sWjQxMge+X5XVFfAKdIa1rVWTllDfEDdsXrcM1TXV8PHyw6QhszAibrLMRrDk47By+zzJBnWLiMV/PnlRWRErKJwkFBlQuKCxYf1G3HnL3+DlGIRxAy6Fk6MTCkpzUVquryEXluZh/rrv0CWkB0b0nQxHe2ckHUrE13M+RnpmCr6Y877BLvdcghqByspKqfcbEwHjUcXUCtBxkC2GJAIEr6f4kWSAtsXnmwxERIQj7cAa5BZkIdQl9MQ4ZyMTo1odhxpZNbNq3nNwmxCBYJ9wxAwKbCJepG7g868/RWp8LjbOTUVDfYPMNrDkvEgfh9umPyEaBmc3O0y8radyH1RQOAUoMqBwwYIB9e+PPokw7+64fsoDsHdovst3c/bAw9e8KrtHDYN7jsUvyz7Fmo0rsOmvRAy/pM85PnKgS5cuEuzZ6jh27Fizo4r5OzUDNPYxBokBzYdo2Xu+ceONN2Lt6nX45q9/Y3CP8XBzc29mYpSZny6dAnQf9PMMlm7LY7mHsTN5IzycfTBm0FSExZoX+rEV08bWCpt+P4iKEool9S2aWolH2kJ1DcdbOQEPXycxc1IzCRQUTg2KDChcsFi8aDFysvJx6eT7hAhwB2prY99ESMf2Ml5MERs9ANuT1mHj8p0YNimuRa//swFmAIYMGSLB39KoYm26Iu9rCgoMSRby8vKEGJwv0Bfhm+++xrOPvo5Ne1agqqYCHi4+GN59BoZ1nyzCRCdbd/SMHIiDx/aLZwBLB/QBGNR9HIZ3n4KBE2Jg72h5KYqI84NvZzeZgpi8LQcVxTUS+KkL4U92i/gEuyBmcCAievvCwfn8lH4UFC5kKDKgcMFi1fJ1sIE9qhvL8J/vn0BBcbakovt0HY4Zw683GNKYQ3lliZQHaoqtUZhVAZ/gczvdLjMzU9oALY0qpmsiWwcJc22PzA7wdgoRzycZoFjPpswb1094GLnRZaiv0/s2iNdSPVBTWQcrKzuM6zEbjgPt4OCify2aG2PM4ADEjjqRtWlJ4NhnQqjcNyOlWNog63X10vro7ueMwAh3EYsqKCicHhQZULhgkZp0SHaZPyz7r6SkI4dchbTMJGza9ZfY6V476W9mH0eP+w27lsHbww9BHmE4tr/onJOB7du3Y8WKFfjb3/5mdlRxz549xU1QjreurtnjKSQkSaDPwPlqi/zww4+wZtlG5OcWSjaAUxsHdBmPRp2N3nzRGrCxtkIjGlFRWY635z6HytoyXDX6HikZxI4JQf/J4acUxG3tbRDWU3kHKCi0NRQZULhgQQEePeoH9ByPS0feaOhx59S6rXtXYeKgKwxOdcag335uYQZunv4orG1sUF2p34GfazLQ0qhiuvBp5QGtXGAMXkfh4el4C7RVW6Su3Ao9AofDtYsbMgsPYdWOP5BdlI5rxz8o9sWcPGhtZS3HuG7PfOjq9Ol9jj+eck8sfDu5nfPyjIKCgnmcv0ZrBYUzhK2NfuccFzWkyfU0uiGOZOsH+RhjTcJCbNu3GhMHX4luYXESnNiKdq5Bm2GKA83t+Ane5uvLQT7WQgxM76OVB2hUdF7aInOLMHPAPRg7YAaG952Aq8bfib5dR2D/oQToGqvg4mmH4qpc2Dg3ohx5SDyyHmP6T5f6fnWZDpWlOkUEFBTaERQZULhg4ePta+gYMIars363TTGbMWhaw+E4g2LHYdyAmYbr6cl/rhETEyPiP16MwVHF3C2zz57+AhEREdizZ0+TcgB9CEgmevXqZXFU8dnu4uDO39nOrYnwz3hSYWVllQwkcvVwwl/xP6NX1ABEhXUXnQanHyZvaUpwFBQUzi8UGVC4YNGzVw/5WVxW0MwIh3BxOpGC33doB35b9Tl6RvbHzJE3yXV6sRvgF9q82+Bs47777pOg/+2338q8hPj4eBlbnJycjLi4OIMd8ejRoyXw837sPKAJ0bJly0R4eOmllxr8B84losN7iMnQgq1fS9tgcXmB+AoYTypkCYeaBrYZpmenYPLQ2U3q/llpJSjKbkrWFBQUzh+UZkDhgsVVN8zEd999gy27VyM6NNZw/fZ9q8WlLjKkm/yelpGEH5d9IMN0rplwr6HOXlOpg5uvE0K6tj7lr60xePBgvPTSS/jkk09EP0DPAXYXMPgPHaovcxD0GLj22mtFVLh8+XLxH+BkxVGjRsnP84FAp2iMibsMG/cvtjipsLKyAvaOdpi76kdxDfR290NxWb7cZmNnjfraBhzZWwivQPOjnBUUFM4tFBlQuGAR26sHJo6fgiXLFuH7JZDgfygzCbtTt2JM/xlieFNUmo9vF78jTekUF+4+uFUey1Q1jWomxA6RQUbnA1dffbXsnukXQDEg3QXNoXPnzrjpJn02gzbF1BOQNFBTcD5QVa6Dp6t+UmFs5IBmkwoH9xyPqqpqpOTES7fHmH4zmjxe0wxWlZ2fTggFBYXmUGRA4YLGv95+CbjLEZsT12DfoXh4ufli2ojrZDdKFJXlSZuh1kVAUC5I0aCtnTWiR1OxP/K8HDtT/DNmzMD8+fORkZEhxIA6AdMOAbYbsr2Q5QISBmYOaPZzvrA5fi0WbPoGT9z8H3i4ejebVBgbOQg1deXYmrQKl42+WSYLmoKth/XnQbipoKBgHooMKFzQ8PBxwSv/fQrLv7oMFSU1MgLXeBphZEh3vHafngRoc+6ry2vh5uOEibf2gGfAuVfjG4Ptg1dccYWYDe3fv1/EeQSDPjUFGhHg7xQVck4B/QjOJ9ZsXYJA71ADETCdVJhTeAzJOTvg6eot55/zIYiyyhL5WVFVhuKKfNjaB52X41dQUGgORQYULnjQh/6SO2Kx+ockFGdXyq7TztFGdv6GgFrbIKI3GtxQMDj62q5w93VCewCzAZxPQHviAwcOICkpCaWlpUICeBt1AzQhYrngfPgKmKKythyNjQ0yPMjaqMRiPKmQos6Ckhy88e2jzR7/x5qvJTMzdvbCc3rcCgoKlqHIgMJFAe7wZzzQB8cOFEnbWnZaCarLdSgsLIKrmyucnB0Q1ssH0QMDEBLt2SSItRfQkrhPnz5yac/oFhuFJfNWIjP7GDqFhBquN55UOGnwLFRUlzV5HDMGf235DcN6XILI0K7oNqDTeTh6BQUFc1BkQOGiAcsDtKrlpTS/CuXFNfjb3fdh9nVXYfrlk8XfvqOivr4BGUlFyDtaBl11PaxsrODobIfO3b3hHXxqiv5bbr0ZK5atxucLX8eIPpPg7OTabFIhL6ZwctCXZAK9wzHzqilwcjn3bZEKCgrmociAwkUJlgB4yas+DCuX6vNGBLQRuyQq52OQDrMjKdtzZNpfWX6VlFCMkbjyKAIi3BEzMABhveh42PoxUrz45Zdf4rlH/4UNiX+hpq4SXu5+kg0Y3W9ay+eivhFOLnaIGdTcJlpBQeH8QZEBhYsWnOrHlj224/Hf2uCfs4262nrpoU/eli0TEVkfp3bB2cMeUf38EdnXDy4eZ3dXTN3Be+98gLXLN6O6qhpebn4Y3GscRva9RG7/9PdXpQ2T5IBtlmz3c3CyxfjJY/DBh++3+vyDhvXDl99/ipXf7kdNRR2c3O1bJBL8G4GuXfDqvV9h3I3dO3SWRkGhPcKqkXRdQeEiAX3709PTxcKXPzkqmIp9DgSiAI8WvuHh4RZ7+s90nO++DZnYuy5TOhvYw0iDHckIcFdc1wCOI3BwtEF4b18MmBoOB+e2JyibN2/Gg/c/BOdGP8QE9YObmysKy3PleKYMm20gAwWluZg85CpDlwVJTEzvcNz19JVmvRfqdHqSU5BRLqUGzhngdUf3F6Iwr1TIlqu7k/41HxduMitCPwf+bVdvBxFu+oc1Hc6koKBw/qHIgMJFg7S0NKxZswaFhYUSiNi3T1CBz9+ZHWCQ8vb2Fge/Ll26tNnf3rd3H178+xtI3L0LdfU6+Hj6Y3DsWAzvPakZYSgpKcG7c55CbUMF3n7vTUyfOaXNjoOtiZdddhlc64Mwrd/tcPN0MlueIBmgwO+Ra18zXKerrUdtZR36TgpFnwknhIEkNinbcuRCHQYDO8+nNnGw0aoB6RkH0SmkM+ysHA2ZEN6H5RGvQGfEDA5ERG/fs0J+FBQUzhyqTKBwUYDGPW+99Za4+TEIeXp6om/fvhg4cKDcvmHDBqSkpKCoqEjMe5gpmDRpEp5++ml4eZ2ZHTE9Au669T542AZgbL9L4ezsJLvu0vKiZvdlYF6750/UN+qgq21A4oqjmDSpDvZObfNVXLJkCXKy8jBp+J1wcXOErr4GtmAK33z3BNsBdXU6MQays7dBXU09DmzOhltYPT7/8n/Yunk7jqXlwMXeC727DMaYAdOk66FpqYFagAb5G3YONhg0cAj++X8vywwCZgMCwtzPi15CQUHh5KHIgMIFj19//RWPPfaYjPTljp/+/cXFxSgrO9HaxjHAAQEB4ufP27OysvDHH39g165dmDdvniHAnc5O/B9PPI3Ont0we8J9cHK2b/H+2QXHZKAPpyb+tXUuinIqpbRgvBM/E2zZsgVWdXYoqSzCnF8+QkFxNuztHNCn63DMGH49bG1P7MzzS3Lw7Kd3or6+Dq7OHhjYYzTG9r0UWVnZuP7aR+Dp5YFozyHo2sceOaVHsHbXfOSVHcVNUx8xPIe7q7eh1MDMAssC3eIiJBNwMmJEBQWF9gFFBhQuaDDl/txzz4kOgF7/lrQAV155ZZPfORmQZj7cSXMS4JQpU05/J56Zi9vG3w1HJzvU6qpha2N5J75g/XcyOZG+/gyVNtZWovSPHRUiO+kzRVrqYVRV1GLOmg8xsOdoRA65CmmZSdi06y+xZb520t/kft4e/uIOGOjTCbq6WpnZsGr7fOQX58DbOQgFecW4acITcKz3gYun/fGSQCMSDqxHZXW5zCMgHO2d0LfrcMPfp7FTbVkd9q3LQOxo5SOgoHChQJEBhQsa33zzjbj1zZ49W4hAbW2taAVac+pjcKO9LwWHqampp/3316/dCNTZoVJXhjd/+HuLO/FdqVtlnO8j175umOBn62CDiuIaHNlXiMg+fjhTlJWWQ6erkc6BaSOuQT1qER3ZDbV1VYjftx4TB10BX89AzBp3R5PHMaDPXfUFtu1bje5hA0RM2FhlDydfO4M2wM3FQ0yFbG1sLZcaHGyEEOzflIXuw4JFTKigoND+ociAwgWNFStWSNq/srISH3/8sYgH+XtsbCwmTJhgEBESrG1rU/94P2YEGOjOpLMgJSlNCMVPK98Twx1LO3HuvhdvbD7Ol4p8dhoc3VeIiDhfGWXMY9QufF38SZ2D9m/T241vO5yWjrpGHTpF+uGQbj0aj//nF+mEmr3l2JO5AcM9ponQzxQj+0wRMqC1As7f8BWmjLpKsgDpWSlS3hjWeyLs7RxbLDWM6XMpyotIcAoQEWeZ4PC4ScTKy0lgdPK+Ub8RERFxztpAFRQU9FBkQOGCBQPg0aNHJchTN8DU/5gxY3DkyBFs375dgg2V9cb1/f/+97+G393c3DB9+nQJRHwuZ+cTQ4sY4Jll4G38qf2bwVq7jj9zc/Ogq6vBgJ4TcOnIGw0T/Bgct+5dZdiJr96xwOw4X6KmthaLF/yFx96+QV5La6C+wfjC4+YMA3oquHu7ory6GI1OlWiEC6xgI+UIZycXIQV5VYdwWLcJHtYh8LOJkp2+Bm3wkKujB0b2uBRbU5fivZ+fMdw+pv+luGTILMPvLZUaZg6+A6nxuWbJQF5enrR+cjATz7vWeUDw3xR3ksxxHgP/raCgcPahyIDCBQsGZgZlDvTp37+/dAcQnOrHYJ6QkCCCQrYSEgyc1157rdw/JydHhgLpA3ourrnmGtmhaoGf2YOTQV52CRobgLioIU2uj4seKmTgSHYqrK1tsDZhEWaOusnsOF/ugiPDIzFo1pPNAjx/Ggd+jj02LoE8//zzWLBggRwz9RO8MMB++/03sLezxy3X3ymv5evvv0BxSRFWrV6JjZs2yGOtYQs7KyfZzV859nbDdEFbOMLFwR2+7sHoGdEfnu7eOJyThNU7/oS7iweG9poo92up1DAgejzc8hybGSG9+uqr4oPA42XHB983TmLUwOuXL18upI2vIyQkRLpC2PXBso6CgsLZgSIDChcspI/9eIqfXQLG4K6SZCAjI8NABnhfpqCJ6OhoER1+/fXXQiIoIGTgZWBmwOVPpq15MXeddv1D9zyObVvj4erUdAfr6qz/vaqmAsu3zoWHi1cL43wL0XtgFCZeEXvK54Djj0l+1q9fL8GTBGnpkmXyM9A/CK6ubtDpahEYEIyGxkZMGHMJbHnOrIFjGUeQdvAQojvFyrlcFT+fFgLSBbA88SfcMvlxWMNeyFNssJ90aMxf8x26BMXCx8vfbHlFKzUcytqHLqExhutJAO6++245jwz+NILi85GAaSCBY4aH7xmHNTFzo2VoeD9FBhQUzh4UGVC4YMHAwoDB+j9T5MbQUv4sFVhCp06d5HF0KnznnXckQJ0q+g/qh+3b4lFYXAB/72DD9aUVxfLTxcm9xXG+89Z8LX79P8/4A6eD3r17S6kkLCxMzgXT7OmHj2Drti2oqa3B7r07kZF5DIWF+Rg+eCT69OqHjMyjWLpiEWpqqlFdW4XsksPY+Psy0QX0ix6FIwUHEOQbjpioHvJ8tbpaVFZUIqYsDvvS45G4Px6dfLvA0dEBzs4ucq55ITnQSg2VNRUiJtTKM0888QR8fHwwdepUOU5z2LZtm7yWG2+8EcHBwUJQSAL4Hvn6+p7W+VFQUDg5KDKgcMGCAYgugocPHxZPAQYbDdqO01gHYA4sEzDtbkomThaXzZqGz//3BbbtXYNukb0M12/ft1rKA5Eh3eDp6mN5nG/sZIQGRqH74NPzGWAnxcGDByVToan+ff184OLsKrqFdRtXw83NHSOGjkaf3v3ldjdXd/j5+GFH4nbYOzhgZfx8hHhFYuKA2egRPBjvL3wCbvY+qKmqhaOzg5Qb7D3t4eXtCUcHBwnUQT5BkongeScZ08hZpa4E9Q0NcLR1lkFRWvslbaGvuuoqIVzmOj5YyiAZiImJkefn78wK8P78G3v37sXQoUNP6xwpKCi0DkUGFC5YMPjNmjVLOgp27twpaX8N/J3BJjQ0VIIP72uqUN+3b5+k0wcPHmwIpKcKpuinT52O33+fh+8XA5Gduokr3+7UrRjTf0ar43wDPMIwZfokuHu3TFosQXsN2m6bAZS1+e49uuHSSbPEGlg6Foxen7u7Bzp3CkP60cOYMW2mlBCsMzrD0doNeVWH4d/ZA9lH85BbkINQ5xMkJTFlswgOOwdGSPukq6uLtE7qBZgVkgFYsm0hdLU6+DqH4I9VX2Nnga8YIXGXz+P85JNPzHZ8FBQUSNBnKWDRokXYvXu3vBb+PmLECPl9wIABqstAQeEsQZEBhQsarPezvszgz4DD4M9uAirVhw0bJkGSYsEffvhBdAVa9oCOhImJidLKdv/995/RMbz21ouoL3XE2s3Lse9wPLzcfDFtxHXSRtjaOF9HFzsxHDpdMCtCIqORGc5nYIshA62brxPKCqpRp6s73kJpbSAFB1KTJMUfEhyG+sZauPk4ICYmDBNunI7L9w/GrTfdjq+W/AvD4ibAxckNSek7kZy+S9onSW7SMvbjx2UfIi56CHw8AmQew9607Tian4IhPccjJqY7sp1LxKo5Pj7eYBltqeNDyy5s3bpVhJKaCdTGjRvFIZJZB762rl27nva5UlBQsAxFBhQuaHCH+eKLL4pKnbvk5ORkaUfjjlNTqZMQcAfPwKntOJl+pkr98ccfP2NhmqOTA17/6Gn89cVlMtGPw3hs7S2b7XB6oTbOd8z13eAZcHpZAYKB3zjdznQ6a/ckPrZ21nBws0JJVilcnTzEO4CeBuwqyM3LQVzPvrButKYNIroPD8SEGXEyWKhfv374+ruv8dT9r2Jj4nLU1FXCy90PkwbPwuh+0+TveLr5iovivkM7UFZZLBkDf69gzBh+M3qHD8fAyRHoNWaC3JfzIWj/zImRljo+mL0h+PP22283tBRSC0H/CBIH4zZRBQWFtoUiAwoXPJgZePTRR6UljYGRu33jAEndgLbTZC1a620fMmRIm9Whnd3tcckdsVj70wFkHSxBTZVOBv/QYlgbYcwxwRz9y8Z/Nx9HGefrF2peTHc6YCDlMCZ2TGizFuzsbeHq6QgfHzfUVtXJMaTtSZGOgd69e8HDzxlVNY0IjPQQIqChT5/e+Pbnz/DXF3tRVaaDkzs1CUBNYxnqG3RwcLXG5RNvhIOVK6yt9EJBOg9yXHFknC96jjwhpmR5hue7pY4PrYRDUaext4CHh4dcRzKhEQYFBYW2hyIDChc8GGi4Q6YAjcGdtWvCuCefJIA1a40cDB8+XJT4p6sVMAdHVztMvK0nctJLkbo9B+l7ClBTWXd83C/dBq0RFOWBmEGBCO3h3SazCPhaWG8nNN8Elgg0MMhqSnwHZ1s4AEg9dAB+/r6IiAmV89JY3SieBqbwCXHF+Jt7YPl3u5BbloZK2xzU2VTJjAI9rGQiomtjEOyrfWFv5Yzo/v4YenkXea0aSM5YFmip40ObHGlOyMnrKEBUroQKCmcPigwoXBBgQOWulmI4qX0bgZqAl156SQxsHnroISkVcBohgySDI0FSwKmFJACsO5sLfm0BZgECIzzk0u+SWpQWVMmO2dbWWnbXHn5ObUpAmAVgoOX5YZmEZRN6KFgCd+Ec48zUPEGCxCwCd9/G4HN+9NFH4l/Axzg7OSMqMgZxvfrDzsYBjQ0cWWyF6oZKbN49B6kHU1Cnq0fn+BD02NijiUlQVFSU6DPY+WCp44P3ZXnDeNKkBl7HYzyd1k8FBYWTgyIDCu0WrK0f3V+IlG05yDtSpq95yw7XDl36+qFLP3+4ettL4GGweOaZZ+QnRWqsT9ONT/MZYJaAjnetDTBqS7B0wMvZRPfu3UWtTwHeoUOHJBXf0g6amgItRU8CQbLEfxvvyEmubr75ZjEbogMg2zfz8/ORsCseJWXFuGT8VJSVVUo6f9GipcjOyZRsBM2dGOx5jo1NgthSOHfuXCkJaKZPph0fJDH8Oyxz8G9p2Qz+m8SEOgaSCgUFhbMDRQYU2h0YpFK252DXymMoK6yWrDSn33EnSgv7ypIa7FxxBHvXZaCsMQep+w/j3Q/fbFJr1rQDFzsojmQm4LvvvpOUv3GJwBS8nV0WDPA8NyRKbOsjGTDGwoULZX4ABXsM7sZeDRRg1jZWoqK2GBmph5GVnWEwCWJNn5kGCjcjIyMNj2FJhuUbdhYwK2Ku44MYPXq0iDzZ+cE2QoLCQWZxbrjhBoMOQkFBoe2hyIBCuwFb0GhZW1lai6qyWgn8LAswqX7vlc+KOY8GjsxdvW0htu5djfKaIjz84P+hd5/YDulhTyEkyyTMflB9bwkMtNRTMDhrg5YovgwKCmpyP6bzubPn8xkTAabpGcy17AoDtbFJEG8jueCOn9kZiv8IXv/ss8/ihRdeEAJgruOD8PPzk6C/cuVKaSkkAgMDheAovYCCwtmFIgMK7QokAb1CRqJzQATsHE58PNnLroHOel8vfAtHclIxoPtoeDsGwsqxDrZW+iDW0cgAyx/09GcfPwM5d9Kagt8Y3K3/4x//kIwAd/AsMbDn3/R+JAds+1u1apXczh35sWPHsGPHDmkTZG2ff8ecSRADOnf7DPokKRqYfbj33nvlOXhsljQbDP7XXXedZIfY9aGVCkh2WOJg1whJioKCQttCkQGFdoPyompUlukQHtQV/WMtt/yt37UUh7IO4J7Ln0bngC6iJWDpgCp94/R0RwIDON0Yly1bJoZKDPgMugzcDPbcuTMTwJ8M7kzDM2ibGzbExzGtz0zN559/brie9yc5YHDWxH/mTIJISkgS+DeYKSB4DCwDEMwckEhow5+MyQhJgFZuoI6AQkdmGUhC3njjDXns66+/3ur7XFtdh/LCGhFvssTk5GYHFw9FIhQULEGRAYV2g2NJRWhsaISdow1qaqthZ2sn/v7GYDDbmLhMRuvqiUA96urrZChO+p589Lsk7KyL9torWN/nKGb25O/Zs0fS8RQBEgy4vJ3dFDT8sTSzgbt7dg8w28DaPjsvGOw5/4A1f5YKeL024tmcSRC7ENauXYu77rqrSZaGxGPs2LFSVmBQ53FqAk8eH4kAoXVEUDTIv0VcfvnlQgqY2aBG4bHHHhNNg2lWozCrAqnxOUhLyBPPA46XlrZOW2sER3siZmAAQmI8m7Q+KigoKDKg0E6gq63HkX0FsnD/tuoz1NZWi6tdeHBXTB02G5389TvB3KJMlFYUIcinM+au+gI7DqyXskGAdydM6DsbaQmdETu6aZtcRwKDI4MtLwy89F9ggGeAZVBvrZuCwZ0tijRwYlpfC/IkEDLmeNUqXH311YYdvyWTIBIKcyZBPD4+F0kGMxj0RmDJgfdlpoCEhbdrY6eNwWzAN998g7feeguvvPKKZCWoEZHhR9V12PT7QfF2YAsqDZRIEDWyUK9rwJE9BTi6rxCe/k4YeU0MfIJVq6KCggaVGVBoF8g5VIq6qkbEdhmA7hF94ezoKoF/XcIifPL7K7j3imcR7BeG/JJsuf/6xCVwcnTF5aNvld9X7fgTP6x4B50i/Ds0GTAGU/CnKrxj8KQnAHf0xkGe4G6d/g1M4Wu+BOZMgph14K6/JT8F3sbShql4sTVQa/DUU0+J8PDll18WfcGtN9+Obz78FUkp+1DfqIOPpz8G9RyL4b0nobA0r8noaCYfWFayftMKbt6OuGr2lfjnP/95SsegoHAxQpEBhXaBmkodQny74IaoHoYg0iOiH2IjB+Ldn5/Gks2/4LYZj0On07sI1uiq8cDsl2Q8MNGlU3f865vHsGz9PNyFS8/ra7mQwQwChXv8aQpmGLR0f+fOnVs0CSIhOJutgOxEoKfCnXfeib/d9SDCA7pj/KCZcHRwREFpLkrLi+R+rk5uuHrC3U0eywzH3tSd2Je+Fb269T1rx6igcCFBkQGFdgGO2jUHX88AIQWciMc6NUfmEmGB0QYioA3OCQuIxuHMlHN2zBcjWEZgOp7iQTo4GjsGsnxAosasQUsmQew8oNjwbPs8sCRRWliB2PBBuO6S++Hi2lwHYW/niL5dhze7fseBDbC1doCH7sTYawWFjgxFBhTaBRycbI+LyAxTdg1g0KcuoLauRsbnEq7O+h52Yzg7uCOvNONcHfJFC3o9cMfN+jxV/Nzhp6amioiQvgQnYxJEvUFb2i6bw5IlS5CTnY87Jv5NiECtrhq2Nvat6iJKK4plBHOvyKHISCoTX4uOKjpVUNCgyIBCu4BvZzfpItBV14ndsDEKSnMkI2Bv64BA786wtrEVEaFp6re0sgi+fs2FZwqnBo4ZfuCBBzBv3jzJEFDxz104g7/xlEdzJkEULl5yySVNPAbOFtasXA/rBjtU1ZfhP98/gYLibNjbOaBP1+GYMfx6QxbJFLtSN6OxsQEDegyXjoO0nXmIHRVy1o9XQaE9Q5EBhXYB7sy8w22Rk1QN++NZAiIzPx37D+9E19BesuNzsHdE19DeSDq8UwSG/l76UbkZ2UdxLO8gbrz12vP8Si588NzfdNNNIg6kYyEzAca7fG0CJDUEvM+ll14q7w2vp45g6tSp58QYKC3lsBzDj8v/iwE9RiNyyFVIy0zCpl1/obqmEtdO+pvZx+1M3gQ3F0906dQDlaU6ZKUWnxUyQIJaXFwsHR08NzwnLJ1onRgKCu0J6lOp0G7w0/KPkJ1chvDgGLi7eUiw37pvNext7TF5yNWG+10y5CoczNiHz+a9jmG9J8miu37HUnh6euC+h5qKxRROD5zwOH36dCxYsEAEgUz9kxCQBGjBTQPPPy8kBGwZNNcWeDZQUVkBXV0tBvQcj0tH3ijXxXYZKCWlrXtXYeKgK+DrGdjkMfnF2cjIPYThcZfI8XLeBcdMtyXYJkktBTsvOPRJO1c8f2yD5BAtuj9qds0KCu0BigwotBtMmT4RX330M9btXAxdQw1cnd0RGzkA4wZcJkJCDQHeIbjrsqeweOPPWLl9Hhrr9d0EL73xzw5nRXw2QcMfbe4B7YXZZcBgxumBNADiDpdE4ccff5T7a9kDBlm2JZJA0BjobLXusSRAxEU1LUnERQ8VMnAkO7UZGUhI1pcz+sQMM1zXltIGaitWrFhh6LIwdlkkKaCnwrp168QjgfoLzokw5wKpoHCuociAQrvBtdddi1lXXo2V3+xHRkox7B1tmhjHGCPELxy3TX8cVWU6uc/wWVGIiPM7L8d9oYC791MR9XFXy1kAfAwDF4MW/QOoI2DXwPjx46WEMGPGjCapb2oMOJWQxkNnUzvADoYDSIGrU1M/BJJIoqqmotljEpM3wdcrCJ389aOUKysqsXt/Koq/34O+fftKZuN0g/Off/6JN998U7opeK7p4sjnpAiT4HmkKyTnM7BTg+eMBksc4MT7KSicTygyoNCuwMA+7ubu4iZ3eFc+CnPpYW8HJ1cHWFlbiWtMfV0jaqv0qV0XTwcMu6ILOnVTwkFT0JXv8O58pMbnoqygGnW1DbCzt4FngBOiBgQgtKc3bO0sBz6OMi4qKpLdPYM+9QEMaAx6HEzEgEb7YdNAxkDINkSWFDjV8Gyh36A4bNqwGQWF+fD31mtHtG4BwsWEJBzJOYiCkhxMGHSFoZ2Vgb+o6hjmf7BQ0vvsnGAan6+JdsicmHgy+offf/8d//d//ycEhfMU2HpJvYCxDwOFlswI8Dn79+8vegwSAxoncdCU6ShpBYVzCUUGFNodGLBGzY5BSC9nvP6PDzCw63jUVtmIc5yM0LWxgn+4mwwmCov1gb2j+hgbg9a7iSuPIHlrrkyBJDish0mB6soGZCTXSObF1dMB3YcFocfIEKmdm4LBivoApv01ImA8yphBkgHf3OMyMzOFCLC8oM0XaGtMv3QK/vfxZ9i2fw26dellOL7t+1bLTIvIkG7NsgJEnxh9RwQ7CTx8XPDS43+Hlc0TcqycmZCQkCDtkp988ons3mluxMwICQLLI6bOjAz4HNHM1zl79myzmQWWCBj4mQmg4FIDSy4cBvXbb78pMqBwXqFWUYV2i43xq7A3fw1e//zvqK+0hq66XgbOOLrYwjPA+az3sV9oYDB777/vY92KLZI54TyH6SOuQ0hAmOE+Ys/7/aOgxROHQjW+Czmfrp6OgBWa1Pi5g2XKnzvacePGNRtlTFJgjgwwK8DsAHe6TIuzLs77tjWY0p8x41L8+vNcfLfofUSFdsehzCTsTt2KMf1nGDwptGC8K3ULOgdGyTjshvoG1Ovq0WV4kIFMcogTL+yk4P3T0tKEGPBCTwP6LvAzR7MlEgPtwqwAswBXXnmlEAFmGEgijP0O2PXAoVGm9s2aMVNhYWGbnx8FhVOBIgMK5x11unqZWMhUNgcWMTPg7GGPRQuWSMrVx8/zfB9iu0dSUhJuu+122OpcMCR6CmztrbAtaTX+N/9V3Dfrefh5BZm15+VQn7raehRZHcaeQ1ua1PjpPjh48GCLo4zz8vJQUlLS7Fj27t0rwZ+zDCg65LFppkRtjVffeAHWNc5Y+tdi7E/fAW93X0wbcR1GxE1ucr/UY3tQXlmCsf0vlfIAjYa8g10sthQykHPXzstVV10l5IbZDo0cbNmyBXPmzJH7Msgz+JM4ffzxxxLYWSYgmaJtMm+jiJAeDCyvhISEiJ0z779hwwbJsAQGBgqJMGcDraBwLmDVqM0NVVA4xygvqpFxsynbc+TfxuDOLCM7HbHDQzH+ykHw8Dt7PvcXAx566CFs2RCPG0f8E16enrC1t5Ha+Zs/PIHozrG4YfKDLWoLvlzwb1TY5GDthtWGgMQxxF9//bXskI1HGbNlbuLEiVIGoEiQt2m7YAZCBkQOEmIgpHqeu2f++2yhvq4B6+ekyNhia1srODg33ZVr4FKnq6lHbVU9vAKdMO6mHmf0ueJrZVnh4YcfllIBswIsI7BcQAEl3RhZYmC2Rbv/H3/8IdMaNVBkOGvWLMkYcBqkNgBKQeFcQ2UGFM4LMpKLsO7nZCzd+DtW7/wDAT6d8H/XvWa4fcW2edidsh0/rc3HP16pQUR0Z0yaNh633XbbWfe8vxDB3WqIZzScHVyFCBDuLp6ICO4mBk01tdVi2GQO1fXlOJR1ACOHjGuyM6WrINvk7rvvPrOjjLnz13bG2uP27NkjP43FcOZGGbclOK545NXRcPd1xLr5u2FVbAtXN/15ICfgdoc6ivr6RtjZWyOslw+GzIyEi8eZGSPRT4GujDwfLANQV0D3Ru088Tq+L8xu8b7MAFBgyMxAeHg4ysvLsXnzZikzXH755WZLLgoK5wotm3grKJwFZBwowurvkpCTk4tN+xbLpDnT8n9WQTrCgiMxfvBMTB50HXxto/HzD78JGaCoTaEpKsuqUV9tJTMejEHDJprw5BQes3jKaM8Lq0ZEePbBsbRcCWAUtFHhzlKBuVHGOp1OdrqsnxsTCJYI+Bjj0cSnOkb5dGBtYw3/Hrb4edu/4NGtBj4hFDkeH4DVCDi52iNuXCfMeLAPxt/U/YyJgAZqCLS2SmYBjKERImZPmOmiKJGEgHbNzKawo+Daa6+Vjg2WYpQGRuF8QmUGFM4pSvOrsO6XZNRW12PV7t8QGhglC2VFddNRuMZpbe68qIoPy4jCosQvxbRF24Ep6OHlFoCj2QdhZSRkr6uvw9Gcg/rzbjLLwdSe193VE56OQfj7fS/iQN5mSXmzdZBpbFN/Am2UMd83YyLAoMfAxp2w9r4R2mAj7TFMqTNbwFQ+jYlMRXWni++++05aUK++6xIJutRDUHRqa2et96sw0zFxpuBr4Dmi74Lp6+AYZ2PfBWosTMslzBiQPNG/gedCQeF8QZEBhXMK6gMqy3TIrUjDnoPb8ODslzB/7bctPoaByMnNHs52nqiu0DXp3VbQY1T/ifj8xw8xd/XnGNV3mj6VHz8fpZV6gR9te83B2J7X2ckZM6bMxEvT/08EbqyFL1q0SAIVxW/mRhkbg1kB4x2xJojjLphCQnY7UEBH5b1GFEg6+LfY2x8REXHahj/MUnCwEjNHWlClEJWXsw12INB+mPoI45HPLANopIDtloSxjbMGZllIXmgBraBwvqDIgEKbO9dZAsVbqdtzYWXTiD/Xf4+BPUYj0Kdzi3+3srocDY31yC/OwbL4n2Wn1yOml3rXTDCi/yQc2JOOLQeWYUfSerkuxD8Co/pMxer4+bC3M7/rNLbntbO1Q2REGCIjI+W622+/HcuXLxe7YQoCGdTMjTLWghyDPevhmqaDZIBq/AMHDoiYTvMsIEHgT63WzuejSJE7bAoTLfkS8G9Q0Z+eni67bT6egZ9kgjMUSCQowjvXuOaaazB37lwpr5DQaKC4kK+Tr0cjBiRSLK1oINFiNmXEiBEWiRDJArs2qCngffg+mJZuFBTOFIoMKDQDBWFc9LnTY/pT65vmYs1dH1Xkp5PSTN9TIC1dO9PXorgsH7df+kSL9y+vKsGrX54oF7Bv/PLhd6KhuG3SyhcTqBUY1+cyTBg6HbmFmXB0cBKitXSzvv3N1KPfnD1vRUmNTIzUQAe+jz76CM8//7wEc34OzI0yJg4fPiy7X3oKaL+znMMgyEBG4sDdPx+naQgY3JkpYBDlzp6Bk738jz32mFgcGwdDEg12MeTm5srjjAkqXf1IEDhY6VxMSzQFXxPbLdkmyGPSugl4zLyer50XEgW+Xp5H/lsTEPJ133PPPc2elySB5IGiTGZWjLMp7Drg+SRxU1MQFdoCigwoNHNJ4+Ks9Y9z4eECxx0ca8K8rF+/XnqojRf2k9ULVNaWY8X23zFuwMxmnvKmcHJwxe2X/h26+lpk5qVjb9p21OqqUZKrBISm8O3sCqstVnC0c5GpjxpSju6Bu6s3/DxPCPrM2fOyvs732adTU3Mgmg2RAKxZs0ZfrnFyMpslYlB66qmn5N8M2L/88ot8nhiw/Pz85Dp+bjjvgH37pva8JB7c7W/btg0PPPCAfM4+++wzeQ5+FrWOBP7tm2++WYIhyw0ffvihIcPA2+gaSNLKAHs2hySZ4u2338bjjz8u3x1mQnjOqA9gRkUDWwjpT8AAz2wIj5f+Atzpv/POO3j11VelVEAyzvNNEsDXze+gaTbl0KFDQrj4WilIVC2JCmcKRQYUBFyA2EbGBYgLNHcsbIO66667DLszZgroKMcFnYsU68h/+9vfJE1qrq/bXJlg1Y7fpf1taK+JrX84bWwR1Vlff+4e3lf+/cEvL6JLbGcMuzJKvXNGCO/lix1L08Vi18lVT9ASUzaLHmDq8GvNvj/G9ry1lTq4+zkhJLq5wRMDtYyJXr9e9BrcfTM4GZMC3s7PBFPZ/JwwgPFzwZKBVk7gfbgzZrmAz2HOnpe/v//++1L/JxHgqF/el5ko7TVoI5IZRI0zCLw/CQWFj0ePHj2rQ5JMwe/Ca6+9JuUKZkh4vKZEmb+zHEASzXPA88dj5Ot4+umnpbOARIedHCQMfD08f8bDjkgE+P3kedRGS5NUMXujtXoqKJwOFBlQkEWaOxEthck2J1MnNO7C/vrrLxlMw8WMqUkSA3qyMx3KMbetIa8wEztS1uHS0Tc0UbfX1etQ31AvVrmO9k5wdjRvXRsWGA03Jw9s38Oa+A3qnTPCnv278Oum9+CJcHh5eeBI7kHE71+HmLDeGN67eeeFsT2vl6ufiDq7Dg6UFj1TMGjRQZDkkHVwpuQ1ESdv09LXDHashzP9zfsxiBvbEGszDUgULNnz8nfezqDOjAB/18Ylm4KfUWYejMHnZVcBSYnW1XCuwPIAd/8k1TQW4mvgcWtaAI0w8Rwwc8DvEbMiBHUZd999t5RIqIEgaWCgNx12NH/+fHF0pGiR7ZvMoNAcikOS/ve//0mmQUHhdKDIgIKkGxMTE2Vx5UJGERiDBeuUGrgo33HHHZLy1cBFmhPsuJNhBoGLmDG4+DGdydQpCcb+LUehq9Phz3XfysUUb3z7KIbFXYIZI643+67w+eoa6qBrVOYspqCy3zvYFZtWL5Ndp4+XHyYNmSW2vBzaYwrNnndMvxmoLNXBO8gZXfo17Q4wBY1yeGF9nwGJgYoBj0GfwY27ehIGKuq/+uor+SxxzLHpTAONaFqy5+X9WVOnQp8Eg59FXk4m+8TXzgDJjAKPz7Tj4WyBQ7Q4D4K6lquuuhq5uTmGTBoJCj+7PH6ePwZykiZjgkPCQHEkzyGzHTyfpuDtJN4kChrRYdaG54uZPH4PaRCloHA6UGRAQRZkLlhMr3KxoYp82bJlTc4MU5la37QGbSfIBY81UC7qXMA13QF/ckHmIkhDlsHj4hAd0F/S/3YOJz56y7b8KlqA6SNugI+Hv/ybU3Ps7ZqKwXYmbUF1bQUGDe2j3jUTsGb86WcfI+dwKVZ+sx9V5bXSjmljZqdPxIT2xst3fyX+DW7eDhhzfTc4upyc/oNpegrjLIGfCaa/KTo0N9NAA8sDtOflblcDa+AcFESVPQMoP4dMvWvCvLFjxzZpczQF0+sEU+YMxmeTDHDQE88322WP7isUh0OCdgaBUZ7oNqA/xowZK6ZP/H6RNFkS+3EQEl8zhx2xU8DcsCOWPggSHWPwu0UtwurVq6U0Y9zeqKBwslBkoIODwZq7dy48XHjZMnYqCyi1BLx88cUXUrdkipgkgenPK664QlzWmMrViAQNh7h40gFOqzlvSFwqP3tG9pefmfnp+Hzev9A7ejD8PIPFSe5Y7mHE71+PwIAg3H7PLWflXFwMCAh3x8TbemLNjwdEsElwKp82wpgZfQ4mYosmf/ft5CpEwN237WY/cIfPkgB3+9zpajMNNm3aJNdrQ4ss2fPS+ZA7XmYH+NlhVoqGPQx4LAGQLFhKh2tDkvh8JLbsbjgbw39IArb+mYbCzAqZgGhjZyOjtbUsQfqefBzdVwB3Hyf0mxwmmo6WQD0GyQ/PHUWQ5oYdaWZP5rQI/C4xc8DXf67LIwoXBxQZOAtgujDvSJmIubS2L79QtyZtW+0FXKQZzKmAZo2WIqaTBRcn7v65WLN8wEWIizgXMEsLcMygQBzelY+aijo4Hhe6mcLDxRuxXQbi4LH90jNPPYGboxcGdh2Lf776qNkUqsIJMMDPfKgPjuwtxIGt2fJZ5DCixgbAyhriyNe5hzdiBgYgpKuXePu3JRjYWCKguFR7r4xnGrA9lUSA9rzc7VMNr4EE4NNPP5XARpdJElMGOu70+RzsMNB2wKZgAOXumqUI/i225vFvMJCSkPBvcVd9uuOUSXaZpeC5LC+skaBvbW1lsNL+x83vwMPVW0oaO9PWYfPulcgryob9Rw7o078XnnjmISkRmAMzatRQsOzG8hszKNqwIxIEdkZowklmCJhB0cASDEHhIkt+igwonA7aX3S6gFGUXYHU+Fwc3JGL6nKdLBYEFwwGPtZko/r7wyuw/fTJs8bKC3vCWYs8FWvYpUuXig8B+7u5eBkru1vaufadFIbtiw+LmyAnzN11ub4lTYOLkxsuH3Or4XeSqqKCYuzLXo8qm3w2sp3iq+x44JCeyL5+iOjji6LsSvEQqKttkEE9bj5OZ3UKJDNMDOKmnyUGdHoFUFzHAN+SPS/vwxS5cccCb6PHBYmrOQ0Bn5vXs4zAwEhCwL/B+/F6ZsCYeeBzkDBowfVkwUxXdGgsdi4/ItkVfnZ5fL+v+Qpebr5CBIjFm37C+p1L0LfrcAzpNQ5lpWXYnrgGN11/K77/6ZsmQ5w08HhJykkELA07ooETyRW7B5gN0ASEJEd8jbwviYOCwulAkYE2ANOE8UvS8clH/8Py7XPh7xWC/7vuVYMXOmuLqelJ+OCXl5BTfATefh644poZuP/++5vV4c81uEgyfUvl8qnMnOeiSsU4DWi4m+Ou5mTRc2SwnJOEv46goqQW9o42eu94k1Y1Bi9mWZh+HTCpC5L/WogHH3wQL7zwQpPdpIJl8Jx6B7nI5VyBwlPuxqnoN66RG8800IKWOXteTTDIujmV9FoXAsF6Op9Hs/DVMgIMiBQf8nHMSjBwMrBq3Ql8DG+nUJbPyZ31jTfeiEceeeSkLZB7xfbC4VUN6N4pwFDmOpyZDJ2uBn2i9SZMDQ312LxnpWS2rp5wt+Gz3D10AN6f9yT+nL/QLBnQXou5YUckAwz6JC90WOSUQzoeEjy/1FFwwqRWLlBQOB0oMnCGYFDbODcV8asPYG3iQgmqNrZWTVq0soqO4Nu/3pT69+TBs1FUUohvP/9J0n3vvffeef3kciGltwCDq3ELE4M7F1ZqCrhQMc1qvANjupclAdZk6clufHtr4ILVa0wneAU6Y9+GLGSnlQgpkGWM/yeD5hpha2uDgEh3dB8ahPDevhg47W1pYWRPNnd8N9yg2gvbI0gO6WDJrBHJrhagjGcaWLLnZUaAQlZmERjAmQJnsKZXAXfFTP0zAGp1c372vvzySyEHvI6P432ZXuffZ8A3bcmj3oDpdH73+LfoD3AyyD5UKtk/R+cTQXdnyiZ+oBEXoycDLGnV1dXC1flEKUvMnHy90NhghYoCnYEg0K+D3z0KePk6+XpNv0fGw44IluPuvPNOeW28jpoLng/aRvN1ncr3UEHBGIoMnCESVx5FyvZcrEj8FeHB5ifw0RLWycFF0uHso2fN0dPNByuW/iQ77HNpjmIKLrRcmJjyN+0gIOjwxpQqPeMJdg4sXLhQUq1MZ/L1ak5qp4pO3bzlUphVgbSdeSjJq4Kuqg52jrZw83FERJyv1L+1hZeLPbMCdGmjYxtd7ThM52RazhTOHRiA2fvO3St35wzipjMNLNnzMojz/Rw8eLA8ju81Az71LEzzM3hSHKhlBvh4ZiL4eD4Py1Va9oB/jwGTBMJcSx6HMP30009i9MOUfGug8JUdA452+s8buwR2p25BWGAUvN31Lbd2tvboHNAFO5LWITQgChHBMaiqqcTK7fPku+9S0xmPP/Y4du/ZLQGdYLqfpQ0Sb5JzYwGv8bAjDfw+GLf48tzyO8yOEmU8pHC6UGTgDEBdwL71mTiSl4J9h7ebncBXXVuF1KN7pd+bi4Gm7u4TPQJ/xc/B4oVLTokMaOlzxke9Qvz004JaKnbkyJGyozAW5tGEiAsua7ra4BkKmtgKRiHWzJkzZdHmQszHcjLd6eJU0th8veyl5mL4xhtvSIaABOFsKMYVTg8MtJwxwPeFpSSWC/gZMp1pYGrPS2JKUsm0N3UBFAOy04DBnTt4zQyLqXQONiIZYPDk55g7axIHfob5u1Ye4MVcSx4/R8wSMAX/888/47nnnmvxNdXXN+DY/kIRX2rfueSju2WQFoc8GePqCffgx2Uf4JflHxuu83b3x23T/47GSltU6xpEZ8NOCYptqZHg62WrJbNu/C4ZMg9Gw47Mgd9REi+SBb4e07ZDBYWThSIDZ4C0xDxUVdRi6fYfLU7gyy44KnXEEP/wJte7uDnC3yMEO7bubvXvUIiYlVKM5O058rOB/cxWelV4WKwvogb4N9lBtwYSAKqiuWBzEeVuiQspd2ta/ZQe8Qz0WpDnzmzOHP3QG+6iuNMieB/WZ/k853JXwtopCQFLBtRevPnmm02m6BHFOZVI3ZGLgmPloj2gqM7F0x4Rvf0Q3NXTYg++wpmDtW7uukkq2XGizTUwzuJo9ry8MKhRyMqgNmXKFLHcpaMef2pOhQx0vC+JBQV3/Eyy9s/PM7MHJAksdWnmPJrBkaWWPG3YFv9Ga9BV1UtWQGsf1OycrW1s0TtqcNPntXdEgHcIQgOj0KVTDzF3WrNjAeas/hg3T3oCzz7yvHQXGYPfs8svv1y6H9hayVkPpsOOCOoFqKFgeYAki+SBmY9p06bJ61fTDBVOF4oMnIFWIHlrNuKTV6OkvAATB//d7P3KKovlp5tzU893dhjwumPpB+W5NLGhKY7sK8COJekozq2S+zEbwMeyrl5TVY/9GzORsi1HFpdBMyLgE2K5bYopRy40XHC4gNAdjsNRuPBwd8S0Oxcac2l3pjC5+BAsKRCaopuLPRenc52i5A6SZQxasdIdkTVgplgzU4uxd20Gsg6WHB/Ao98J8qSRWLEkwb562u92GxrU5q11Cnrws6Gl7ZkBYAAnTIfukAiQhJKQMhPFlDk/k8w+0VWPGQISCeNgzs8pCQKfiwSAO2jtQjDbxc8H0VJLntafz6CrlRT4WedPZh2038uKKtHbZSb8A5iet0VNbTX2HU5ATOfYJvbZJP6fzf8XIoK7YeaomwzXR3Xqibd/fBIb9yzBrEbzhk0vv/yyfKdI1Jn657GaDjtiSYEiSGY0eD6YOSFZ4PfP+H4KCqcKRQZOE5VltcjJyMeaXfMxbqDlCXx1dXrBEF33TMGFrLqqBlXlOji7N09zJ23OwrYFh7Aq/k+sTvwDAT4heOTaE2Kn5CO7sStlM9KzD4rvv+fbPliw4E8ER+vT+hpYmyQB4MLKXRRbACm+M0498jrezoWPuzNTcR5nEmhT6biAa+UBpjZNbYjPJViDpssdJ93dcsstePLe13F4SzlWbp+PNbvmNTtncvx1DWLIs+a3vbjp3qsAhxr8+9//FnKk0LZgsKXxFDMF1Juwxs/Po9Z9QhJAEsl0OT9HxkSUny/217/11luSBWBGgLt5rUOBZIIX7ppZXiCBIEGgCJFaHF7PDpnWWvJIUuiJoAloeR0DMS8kEvy74WERcMkyEkMeitd3EZiUCNIyDyCn4BimDb+uyfUcIe3rGYyjeQelTGgOfF2vv/66aHEY8Pm3TEeFs6TIC183SRQJDI+XDoY8R3y8KpkpnA4UGThN0MGNbYTOji4tTuCztdXvZurqm7fe1TfUwdbGTgSFpmTgUGIetv55CCVlhdi4bxEc7JvPaU9M2STDZoJ9w+Hh7iXlgzU/JosDHcsGTDPSsY1GJlwEWaO97rrrZBdhCi56HC3LXQlVztyhsCartSuJVqGuzpAd4ELJ1CQX1vMNiseoKH/x0Xewed5BUXtv2r/Y7DkjmAlwdnfAsvifUFZcASs7poCbt7gptB0Y1FjTZp2c5QB+jkgEeH1LwYtBkYJVZn74WeRunY/XiAAJBmvm119/vWSFtBHLvGgGR7zOUkse2w35OeeoY5IJfg9IIsxlx/76Yi+OJRUBzvouAnt7R5mmaYzyqhKLLZN1Oh0cHRzg7ts0wBuDf5djo/n9YqlOy6bwtRtnU/hd5GsgSWf2gFoMDjl66KGHpGR2vluWFS48KDJwmsjIOob45LWYNuy6FifwaeUBrVxgjNLKYrnduA5JsL695c9DaKhrkC4F1h7NdSlcMuQqXDHmNtjY2OKrhW/JjoRe8yt+2IWEgvlYsXKF7Go4DY2e56Y1dVNwAaIDoaba5nQ6zbxFmzbHtCQV4txttacdiIOVKwaFTUN1VS0Wbf/a4jnTkF1wDNuSVmNMv0vx19a54taH5sP9FNoY/ByZm3NhDtzpspWUnQUkA/wc8jPJC3fEDIosezErwA4DSwZHJIuWWvI4iZNlCZKU1my46Z6ZcaAIJWXFIgqOix7SbH6Gr4e+q2ZX6mZ0DTvhNngs5xAKSnIwfPQQs5MhjcGgz4wGj4nOhMymUCirEQySFxIrZlM0h0aWCjj6md01FNi+++67zfQDZYXVSN9TgMqSGsmOUUPj4e+E8FjfdumOqnBuoT4Bp4kyIQCNWLDhOyza9L3FCXwTB14uU+Mycg83ERoxU5CZl44I/x5497230Kd/b0mncqd9eHc+qkprkV2Whj0Ht5ntUiA4Ic0UdEVLP5CFjJICPPnkk6JaPpWgzYDPQM8L66W8sLTAnQnJBBfV9mhsQqFgXU09CqqOYHfqVovnTMOC9d/JLIQunbth+Tbg6P5CWSCVfqD94F//+pd89p555hnDbp+BnReC5MBSF4CxwVFLLXm8nT4HJ2Nx3ambl7S8Lt+wUrQBfY57CzS5j38EojrHio02dQXRnWOF9G/YuQx2dva454HbTvr1M+gz4DO7wZIAzwW/n/w+mzNKYhfHxx9/LILae+65R8gBMx3UziRvycaR/YXyHSHo4yHf40aIJoluldTQePipjEJHhSIDp4luPbviwdueRFZqMRxd7S1O4HN0cEZU555ISN6AcQNmwsFenyJMOLABtboaUUjvT96L+Qv/kMWNTH9C1K1wt/fDgo3fW+xSsPiG2tvAx8sX90x7AsOvODNBH7MKWlthe4auth6p23MBa+DP9a2fs12pW5GenYJHrn0dxWX5It6sKq2RXV9oTzXxrT2Anhe8vPLKK00CuDEYzNjZQitt7viNy1/GBketteQxa6Y5ALYEEsV+l4Thne+3wMneDVGdYs3e76apD2NtwiIp4R04sgvWsEGofxTuf/B+dIs99e8kXwcJwMmQenYUkEB8/fXXWLx4sWhmOAeEA6r4Oaf2WJuhQBzKSMbCpT8h6+PDcHJyxozLp+LpFx5XZYYOCEUGThNMqV998wws+2Kf2Ona2tmYncBHTBo8Cx/PfQmf/PEKBvcYi+LyQqxLWISokJ544rkHRPDH1igqoRO27EXDQUckHF0jger2S5845YXDzt4WR3YXYthllrsULiZkphSL9/7Ow2tbPWe6ulos3vij+D7QKEbIAHeQjdRp5CsycI6g+WUwG8Pvj3HqnClxCuFodKX59FsCU+IcjMQ2WarpqQ8wNTiy1JJH/QEFitxRnywi4vzw8fufIn5xOqrLdLIRMC3z0Xho/MDLMK7/TFRX6tBY34huw4IweMa5manBkgqzjL9+sRRBLjFwc3MV0aLpDAVOB/1iwb/g5xWMGaOvR15+Hn756VdkZWfii28/OSfHqtB+oMjAGSCoiye8ApxRkFkOFw/LBkAhfuG4fcbfsXjTz1iw4Xs42DkhLnIErp5+ozwHwfoe+/27hvbBb+9uwurEPyWTYKlLoSVY21rJIssdsyXl8sUEllRo/rJi+++tnrPVOxaIpmNMv6ZDlaytrFBepIa8nG3QbZKDvNIS8qCrqWeW2jBKmTX5zj28RCfAstQ//vGPVktSFM6xJY/pcboXMtAz5W9qcGTakkedALtHeD9LmQdL6DkyBI4udti28JBodAjW3DVSwBZg6n64G+f1vSZ2Qq/RIeeMmFNP4GETgob9kWhsbICrh3OzGQpNnFEv0zujkqB5uvpg4cpvsXLZGoybNPqcHK9C+8DFHynOIvjlHnZlFJZ/uQ+VpbXSEWA6gU9DeHAM7r3yGfnCVZbUyhTDsdf2aLZAcCFZueN36V1uqUuhNTQef66OAHoJrNr5O5wdWj5nFHUyfcv+b61co4Exh8+jcHbAVs4t89MM3g+c30HTJylb01QrtUR/G6qQc6BadAInO6qaHTKsjbM0oKXTTUmE1pJHaAZHbGU0nZp4suAE0s7dvXFoV774jXAypK72+JRSKyu4+zkJuYns42e2bfhsgmvM/vWZQIMVXD2dzc5QMOeMynM2pM8YLN32M379Yb4iAx0MigycIfw6u2H0tTFY+1MyKoprZCdgOoGPkL7gmnrZMXBxGHVNV/h2aq7uzy3Iwo6U1rsUjI1OTMHFlbsUevx3BOQVZWFH8jpJdbZ0zpZvnQsPFy9EhnSX64iySn0rWHlVKcqq9da2atZB26Igoxwrv03CotW/NvN+4PtAsS3BUMqMFv+169ZNMhmQaXy2/bWEqjIdQhxjkV/tgJLMctQ2NsDKtgE2njWw9qyQf/P7RwEeMwd8f9ltMHny5JPSClgCv+sU3cUMDEBpQRVqKo9nAxxthAycL4fLwswK5B4pg72TjcUZCpacUe1s7RDoHYrdiftQp6s3lD8VLn50jGhxlsGa/8TbeyJh2RERFHICH10CtbQh+//pfMdZ8hSo9ZsUBu9g81781Q1lsLJGq10KM0Zcb/F4WIsN7emtdyrsANBZV9BbEH+u/Q4L1n1n8ZwVlxVIe5cWfIyxcPP3WLnnF8y8b2OrLZgKJw+2s636NgkZRzPNej+4OrkZRv0a3s/aeiSn78bBgh0tzu0oyavErlXHpF2Ovh+AA5xt7GRqYE25DnUVLrDK9kC9SwnqPHJh52wlJIAte2xFPNnRxa2B2b32pMI/mJAra4CDs73FGQqWnFEJT3cvpB1LEk+F8F7NPUkULk4oMtBG8Al2xYRbeogfPr+MR/YVorZSbzRk72yL0B7e6NLXH54BLS8aMV2j8eT/PY+UrTlwcLE1ZBhMuxQsgTsrkonogQHoKBg4vDfuvPoxlORUSflFg+k5q46sauY7kFN4DMs2/4YRvafgqjsnqRGwbYwdy9Jl17x6z1yz3g/2do7o23V4s8ex26a+2gZx3Qc2uZ62wbSg3r5lB4pzKxHkFY7JQ2YjLCTSUHJrbHRAcnIKqupLZKx4VsEReV9nXD5ZvAYudkOe0vxqIwtu8zMUWnRGtbOXrFp5kd5gTKFjQJGBNgaDff/J4XI5rcd7euKaWy/FH8UJklGgUMlSl0JW/hHsP5wg/+aOt7q2Ess2/Q4nVzuE5dQhtMcYdAR4eXth1vXTseHXVDi52RmU6ebOmSlYPqD7YPce3XDprMnt0kOhLcA0OdPpdLtkxoh+FA7OTQf3tDUYTI7uLURG4cEW/TJMUVpRjPTcA+gZNhhH9xTDL0SvHUhKSsLtt98Ob09f9AudhBrfWuxMW4evlvwb9816Hn5eejfMysoq5BZnYsmOb6UkcenoG6RDYe6ceSgqLcDHn36IixnMknBDQFiaodCSMyqJAJ1RWdZU6DhQZKAdwtXLET2GByFx5THoquss1v5pWvTXlt8Mv7MUsXrnH3D1doT/GhuMHdsxyAAR3tsXe9dnoiirEi4e9iet3KaGgzXV9TsXY//+kdKnfTGBr48mVge25qA4m26S+utJCIKiPRE9wB8hXb3OSn2bGbLqah2WbPvhlPwy6N5HFXyfqKFI2Z6DXmM7wc7eBh999JHYF1838jFUF1rLBMohfcfgzR+ewNItc3DD5Afl8ZWVFdievAIuTq4GpTy/G55uPli27Hts2rSpSafBxQZbB2s0HtfCWpqh0KIzaoXeGZVlTYWOA0UG2in6TAyTroOU7bmoqa7FnZc92WzX2r/7SLlwoasu18lugBmJ2FEh6GhgC+XY67th2ed7pUWwpc4ObafMcxYe1A0/fvEH/vfrG7jttttkYA2HNF3oIkJRlG/Mwq6VRyUjQNg52sDG2kqEesw6pe/Jx9F9BTLBcehlXRAU1bx+fCY4srcAO5JXo7i8ALdbmOppDjuTN8HNxRMxEbHSNpp7qFQIC9sCe3Xrh9pSGzi66kW67i6eMiEw6fBO2QWzS6SouBCZhYcwbuClBqU8yc/AnqPwV/wvmD930UVNBvh+NqJQPgOWZigEeney6IzKjGO3Tv1kU6LQcXBhr3gXMbh4DZsVDWv/EuTk5qC8uFomJbItq6Fef6HQij7jXDAdnGwx7IqoDkkEjEs0k27vCa9AFwmA7O6oq62XRVEDz1tVea3cRke5ITO7YMSMWJl8yGE3HINMO1emlS9U8PXGLz4sg66WbfoDL31/Jz5d/LwQJjpUcpfNz4ujmy027V+CVz99FKPGjsTIoWNl0A1HWbcFCvKKsDpx3in5ZeQXZyMj95AEKCrZSXRrquoMLYHl+TTxaWiicre3tZfsDvUf1CRk5KWDTlKmSnlHJ3sEeHZCwrZduJjBdkZbWxuUlOhnKPSM6N9shoKxMypJlLFWo7qmCv16DhP7ZYWOA0UG2jH4Zf560ZuoDz2E/peEw8XdXsoG3NFWV9ShvrYBXkEuGHJZF1z+WD9ED+g4osGWCMG0v/UW/wffzq5S92SGhQ6FvHBcNGvlvcd1xvT746Q1TFwb7exkDPIHH3yAtLQ0XHPNNWJXeyFi3/pM7FmbiYqaYmzctxgOJoGAYPD8asGbWJO4AN27xGHqwOsRGzgKDTXWKC8vP+NjYFBetOFnMbU5Fb+MhOSN8tM4ra35ZXQK6Yx9+/bC2s66yU72aM5B+TfbSjlau7K6DDbW1s2U8vpMghcyj+Ucb2G8OEEDJ99QVyQkbbY4Q0FzRq2qqRBn1C17VmLp5l8xb+03iAzqielXThTiqNBxoMoE7QBcOHlhq5NxKYC7VOK+R+6SGQGxo0NQnFMldWDWxLm78wp07hCWw6fb/52TXiodHhRV2dhZi7iSKWdLzoy0tP3pp5/w4osv4v/+7/9krDMnwbXWj84ASktpbbQsHSVpf3uuQfOrncuPSsloecKvCA3oYnZ64/pdS3Eo6wDuufxpdA7oItkEkiW/UHdEhOsHAZ0M+NzHjh0T+19Ou+RPkimOFs46XIgpg07NL4PKd1+vIBn4w6wAP9vaezV98kw8v+ElzN/wJcYMmC7HvCp+PkqPe0XQalpG/lrph/CYU8qT9Ol0teIJcK7NgM4V+Np7DAvGax9ugbOD5RkK5pxR+0QOx9Th1yBqQMsTHBUuPigycJ5QVlYmbVJ79+6VBUwjAwEBAdIHzeAyf/58PPXUU4ZhQUyNkvUrnBwYSAIjPORyqh0dnAn/22+/4a233kJ8fLwMzGGPujH4nh09elTGPTMIkgho455JCDj5kUNjOnViffbcJOHSEnKFLOaUp1mc3sjj3pi4TNLHJALcPXKHzYxJYUY5ctPLEBDhbjboM9AbXw4fPixmPgT9GSIjI+Xzy8sXn3yDFQm/YfWuuSfll3Ek56B0xUwYdIX8ztdBIsBdLjFt8kysmLMDm/Ytxc6UDXJdiH8ERvWZitXx86VNkeUBD3ePFpTydaKUv5gzA0RYLx+8/fr72L0mQ4iwg7N1q86oLDeSMI++rivcvJVeoKNBkYFzDKYxmX7mnHLOVWeQ0DICrIlyceWloKBA5rjPnDnzXB+iwvHd1axZs2SIDQnZzTffLPX0q6++Wm4rLi7GokWLMG/ePGzYsEH87TkghrdxYf3+++9x5MgRQwmC1rp8nylc0zI+bQ22SLJroNGqAX9usDy9MbcoU3brQT6dMXfVF9hxYL2UDQJ8OmFC3NVIWO0EpyNlhl2+paBPojNjxgwZAczffXx8DJktnp/YmL7Y9MdB0b9oKeeW/DKYFSCY1tYGGUX194fT8amgdPacMOBKjOo3FYVl2XB0cJLXR499wtczEAHeAaiqD7eolOd1FB3SJfBiBt8HiolJiPeszRDNkb2DrYhIjbOPzL5UlFbBqtFa2phZXgvtoSZ3dkQoMnAOwMX0k08+kUlpnKhGcHrasGHDEBMT02wHxp3otm3bZEHduHGjpK4fffTRZvdVOPtgkONEvP/+979444035P0gKVi5cqVkBTgch+1uJHXMBmhg4GeGYeTIkWKBywDK95vPd7ZQnF0pnRQ701qe3phfki0/1ycugZOjKy4ffav8vmrHn/h+xTsoLbsN64/9KGUOHm/Pnj0NQT8iIkI+u635MfC1XzprChyK98hoaBdPB3mMJe8Hfu458rdzYBR8PAJQU6mDjZ2VkAENrp4OErAayxplR6sh5egeuLt6w88z6KSU8v16Du0QA7xIBDhyOSDcXUhiZnKRCI5FgSETovg/K+QX5sHRrxaX3T0T3kHmnVEVLn5c/N+IdoCsrCypJ3NyGuerM1Cwpvrrr79iypQp6Nv3RNvPwoULpXTANOvAgQMle8CMQVHRibqrwrkFB9889thjYo3LcgF39nwvt2zZIiUABjJmfExBnQHH6PJ2ln34XjKwni1QdV9RWYYV8X+0qOBn37ncX1eNB2a/JJPqiC6duuPf3zyGg7mJ+HPeQgQG+5+xCVPfiaHIP1ouKWinFmr0qcf2oLyyBGP7XyodIPl5hSi1OgZ7jxPniylsOmsm/HXEUI5JTNks3QdTh19rKMUYK+V5HrShVDuS1strphNhR9HZ8Bx16uYtF9o3H95VgMrSGuhqGmR2AW2UV27bjZ9/+xF3PnPZ+T5chfMIRQbOAYYPH476+nps3bpVrFBJBhjov/zyS7lOIwOcurZ7925ceeWV6Nq1q1zH6WpEaGjouThUhRYwYsQIPP7441i/fj0KCwsNjnjLli2z+BgSAWoJXFxcpF2RRI/v/ZmCwbCkpESIJi/Z2dnITivFqs1b4OzYsoJfc58LC4w2EAHC080XYYExyMg/BF+/1nf/JwP/MHcMnxWF9XNSpKvj9kv/YdbgKCa0N17929eora4XcV9IjCcWLX4PCbcswdtvvy0ZiR07duDDHz+GTX6gdClkFR9C/P51iAnrjeG9JzVTyn889yVRyg/uMRbF5YVYu2MRYkJjccUNU9ERwcAfN765FbNP1JX4+vsv8eeff2L27Nnn5dgUzj8UGTgHYJqYQYAkQBuOwl0MU8dcyDWQGHDOOomAFkSYgqbYkI/nblTh/IHvB0s+9LnnwsldPzM9lsBsDksLJIIkA927d5cuA+oQWhuSw/c/Pz/fEOyNg772b2YajLMQgV5h2HVoOy4deWOLCn621xGuzs2FlVSfZxcega1R+96ZgsNuWKOnXXR5cY2kqMUAyVY/wrihgRa6daiva4SdgzW6DgnE4BmRGHHtl9LJccstt+C1114TQuzobI8th1egqKAU3h5+mDRklozhZVmgJaW8vY0j+nYZgYf+7yGDxbeCHvwMjx8/Hj///DMmTpwo5S9+tkg4uf5w1DP1IAoXNxQZOAegWJAdA6wpM51McsDrKNDS7G95HRd4BorVq1dLLZqCLdZeWWtmAOLPi33ISnsGW+e4G2dGgD+vvfZai/dlB0hYWJgIC1nm4WM0Hci4ceMksOXk5DQL8NqFt5FEaCCJCAwMFEJIYSl/8qJdx8/Jtm3bce0VN0vwW7TpB4sK/kmDrpTBNcaEwZBtqCiCr/8JIWBbTva87NF+MqvgwNZs5B0pQ52uTogB/xQHTFEf0KWfPzz99Z9xEmNmz55++mkhBY888oiUaNgJsPKbJBw7UCTttbYWiBV1Bfdc8U9R09NvIrKvH4ZOb9oRoqAnniQD/Bx+9tlnhnKLJoblusXPK0uX1JC01bRHhfYFRQbOASga5JeKQZ6WqtoXjRmASZMmGXaRvA9LBfwyjh07Vlg5AwhV6/wCpqeny+5S4fwgIyNDyBy7B1gy4G7fEqZNm9bkdy6kfB9J8qg/4HMZOyNSlKcFeOoKtH9rl5MhgTEx0Xj6sReQtDkbji1MvGQNvWtob7HwZWeBv1ew3C8r9xiO5R3EdTeenVQxnQ8ZkCP6+KKsoBrVFTrJBjBr4O7nJLebgueY7Z0kAWz3JIH++9//jrE3dsPG31Kxe8Mh2FjbwtXdRWrg2mumURGHMulqG2Bja4WuQ/XZBm2IVUfH888/jwULFshnkHomZgJICpYvXy7rD104mbnk+WbrLD+vXKPY+rxu3TpZmxQuLigycA5A8Ri/YKwVd+vWTX6nxwC/iPwCElrbFjUCbGMLCdHbCrO3nSNbWUKgd77C+QOzN+wm4EI4YMCAU348d/QkA2w1fOaZZwy7ei6wFCmeKZgdmM2Jl0UJqNPVG1ryzCn4LxlyFQ5m7MNn817HsN6T5LO4fsdSyUA88Mg9OJsQJ0BfJ7mcDPjdYQcHd6UUcLJl89///je8Y2ux+JPPMHvyXUAFUFFca5jWx4wDyUHXQQGI6h8A/3C3i3Yi5engiiuuQP/+/UUEm5mZKZ8/ZgCWLFkibbD8HBDcnBw4cEA+qyyTMcPJdtrLL7+8TT6zCu0HigycAzDgcyHi7o8Xbaf4448/4pdffpGaqNaWxgVdIwIEv3AkBImJiZJuVjh/0ASAl1xyieg4NHCR5HvMEgBr9yzpmAMXWH4OWH+99NJLz8oxung4IG58Z2xfdEi6C5hGN4cA7xCZ6Ld4489YuX0eGuv13QQvvvE0AgLbj601zyl1GiTJ/G4wK/Dtt9/ipptukqyBa4ANbnhigogTcw6VymtmzKcLZVAXz4vWZfBM0bt3b/EyYeBnoCdBpVaAa4xxx8uYMWMwdepUyUxyreLcCpIxttZOnjz5vL4GhbaFIgPnAAwOWgbAGMwSLF68WJTpTMkR5lLPfLxx/Vjh/EATVbF7wFwHATM4zP5QhGUOTLOSDGiOkmcLPUcGo6qsFnvXZaKyrhZ3XPokrG2a74opsrt12uMy64KCvuGzohHRW09Wzyd4jlkSY2cNiYApCaZxE90QN2/ebJgwyQl7asreyYPZSab/SQJ4IUh0+fk0JgPauqSBt3ODwmwBM11n+7OscO6gyMA5AHvR6eCm9UYb7yi19DN3izR5Md5xaqBYjV9YtlcpnD8wCNF8h++FsYhqzZo1ErAmTJggiyPfT95ubELE9561VuJs76j4GRswNVyMfhJXcISxvgTFgE83QKK+vhF1NfWyi/bwc5JhV8HRbTvC+FTB0hkHRdGhk+lolk+onaGa3aAFaGwU8S0DF2vdL7/8skycpBbHXBmAQY91bz4fCTUDGUWdFHd2ZCEczzXJrRbseW54HbOSzMC0BGa/uE6xhMC2aYWLA4oMnAMwDcdFiEFCE97wy8edDwOGVjqgOJCCQarWtcBPwRoNith10FIbm8LZB98fdnRQdW28Y+J7xvdJ84bgrpZ1Vb5nJAckfdxJMb1KQjFq1KhzM6xmeDCi+vnj0K58JG/NloFNdXWU7+tHZIf29BYTHw5uMtf7fy6heTZoO1MGan4/5syZI2U0rbWNt/Nc8jvC7wPPLckyB0s9+eSThjo2BW/c+fK7w++dpozXnoPvC1PlfI86WoeOuD3u2iUZFY1AaaWYkzHF4mP4/vD80h1VyywoXNhQZOAcgLsdsmim+7krYeDgF4k1O7b0aAsYAw3ZOQfkMAVHBk7BGRexBx988FwcqkIL4OLJAMK2P2YCLC2CrMNyN8tAxJ0pF09vb295rzkJ8VwK2YwnOHJ8s2HipbNtu+q3/+ijj+ScchYHzx+/EzTj+vjjj6ULh0Zc5urY7I2nAI7vyd/+9jcRFvK8s4RAseemTZuENNx1112Gx1Osy2wOOxSYMWALI99Xzpbgz4sdDPr8XBp/fjUfFK3VuTXw/dGeR5UKLg4oMnAOwPZBGshQucvFh6SA2QL2mxtPwqNe4MYbbxRxDrsHmD3gQsadEQOJwvkHF0u2ijL1rE0nZN3aGEyzUm2tgbtXEkBOMTzZxbatQQJAMV17FdTRXZC7f343NHLMshn723m+GcC1642zMtogKN6PxPmJJ56QDA1T4HxOcy1wLEPw+8VdsDZngQTizjvvxBdffHFWLaPbA3guucHQyiT8naUXZiMtiV9NwXPGDIPWBaVw4UORgXNEBnhhKo6zB5i21GyJTUGWzbYf7pK4oJE0cFdkXH9WOH/g+8B5Esz20ByIwYQZHHMjirlY8r0mGQgPD5fHdaT3kZ0ATP9TOMnzwPPEjgoGa600poE1aAYi02DEQE9SzE4O4y4bc2lrdnlQEc9Ax6wA7286N4K/kyRQvMssBO/Lv82MwA8//CCC3oudDPAzaFw2YdmF6w2nUJ4sjEd1a2AnR02FTiYhchCUk6tdh5kBcTGg46xM7QDsk77ssstkwaH4ieCXiYuk9gUj0+YXk9dzShzFZh2tptnewVIOh0zxvWQQYrZHWxi1RZYEQLNzZaChSVFH6cumNmLnzp1S1ze3c6TGgjt52jlrUxyZFSO5Mi6hkASwBECYE9Yag98htr0xm0ANAd8jc3Mj+JzarAiCf4//5mM0wnKxg4SLJIuvl59JljC1FuaTBc8hP+9Ojk44llSIlG05yEgpRkN9o95V0lo/CyFmcAAi4vwstrgqtB+od+gcg7Vkpv2ZluPAG6abqSPgoqS17VCoxt0JdzbKKKV9geUeCtXi4uJkaBGzNww8FLsxYGk7Jqa86SXBHWhLToUXC7ROAO7IGVjNdQJowZjp/K+//lruxwwB9THMgJFEMHPG6ZAEnR5ZkyZOxmNDm+VBAmBpbgSDIDUCfL/4/eLx8T1ctWqVvHckeBc7GMT5ueSodJ5fkjaWr05WCMjzxPejc1AEln6chJK8KnF8tLW3Mcy0YHagILMcm+aWI2HZEfQa0wk9RwSrTEE7hiIDZsAFi62ATJ9xweIiwx0DF5e2UPUz4DPYU0NAVS/FU0xf8noKzTpC8LhQPxdPPfWUBLdXX31VsgJ8r+hGyAs/J5qwsCOVA7ROAO44SWQZLCiQNe0EIBh0Wa9nSpoCQYrQmEVgep+P1SZ3EtocBhKM1rIq2tAmfp9amxtBw6c//vgD8+fPN1xH0SLLc+bKPRcjuP7wXNHMjGuPpRIBhZnUbBAs9/Dc8jvACoODtQsc7VykM4V8794rn0VoYJTcl8+ZkLoWm/esRH5RNuy+dECPnt3x9Mv/h7g+cef0tSqcHDrOinWSiz0XJn5B+MHnB1prv+G/2TLG27mj4OJ/pn3/XDCZJlX+ARcGaCrE958Kd3NT3EgAOhIJ0EAyy+8JzZYYtEmQaHVr2glgXK/XHBhJBqghoFcAyQS7Zph90cg3H0+QJLcEEjESAnYPtDY3gs/NjAS/x9RycHfMx7EdlLoOS9qEiwk8t/Q/+f7776UMyfNgiQyw80IDyz48X06OzggP6oYxA6YaMj8+HiecKxdv+gnrdy5B367DMaTXeFSUl2PLvlW44dpb8NOv3130uowLER1v5TIBdyIc2MHdCv/NXQ13JExxmmYAKGLi6FruYrjos8XpX//6V6sLFZ+TU9pS43ORlVqM2qp6qam5F8Qiqoszygqr4eatBn+0Z1CBztQ2gxUnSyqcAA1/GECZKdH0LeY6AUzr9QQJAAV+WkDh70zda2AKm90DrY3QJdGgFoGPb2luBO9HoSCPjYJDDSTkJC+///67tPhe7OD5pqiZO306oFoC9S5auyXPHbNAv82Zi5mD78aoAZPMljEbGuolIxDbZSCunnC34fruof3x7m//wB+/zVdkoB2iw5OBr776SnZ7rCPSSpaLFRe3b775RgYGsZ5PUPBHFs1dBVsCKRrjroVtZdxRWKq3ZaYUIeGvI8g/Vi6jV2nuoilsAzw7w67UAX+8nYDQ7t7oNzlMkYJzDNY2s1KKkXukDLpqrQffDp26ecE7yMVgYPPss88K+WPrp0JTcEfO7we/A8bBwbQTwFK9njtPZhYYkLVsHEFyTlEh22pbS98zk8dARcFtS3MjuNPl8dAt0hgk9OzkoYano4CdHdRIcINDvQb1FiRepuea54/vE8+ls5MLbBud4O7qgVpdDexs7WBt3bQrqr6BpK8Wrs4eTa739vEScWFZrpqx0h7R4ckAgznTmBTzcbHggsX6Ged6M3WopTNZt+QO59Zbb5X6IkHR09y5c4UksDZqnGmgSOpgcjrqK2wQ7B2BS4ZehUC/YMNieTTnIOKT1svPzLwjsmj+5+h3GHtDN/iEuJ7Hj0THANugDu7IRfKWbBQfF0BxoaI7H3/uXH4EgZEeiOzng+f+83fxDnjuueeUoNMMGEQ5K8CYEFvqBDBXr+e5pSsjs3PsCOD3i49luY4dNSTpLdWxNU8BEnQGNnPeAtrcCJIRwnRWiNb90VE0AxqYcaE1M6cVMqPCc2h8HrQha8z4UDSbsjUPaJyLP9Z9gV9WVsPKyhrhwV0xddhsdPLXd4bY2dqjc0AX7Ehah9CAKEQEx6CqplIGYjk6uCDCrT/qdQ2wOS42VGgf6PBkgLUrZgEIbTHjLoE1RdYuNVBMyNYbjQgQrH3ydy5uGhlgpoGLWP/YoQhu7IfS8hJsT1mJj/94Hvde+RwCfTrpny99F7btX4Mgn87w8fRHfnE2SvOrsOrbJFxyV6zKEJwF0Ofhk08+we7EPTh0IANWDTbw8wzGqH7T0Cv6xHjfJz+4SQRSzBrIrkhXAf9gLykd0X6VgUXhBFij//TTT6VltrVOAHP1eroFkggwC8dUP4kECcLo0aNFQGgaoE3r2HyP2J3D7y+f09gv33RuhDbwi6SdREMDd8bMblDUeDFCxqXXNcpoa6r+bWytDSSIXSB8T+6//345jxRPs3TDx5AEsKRCXQdLMPF//IAe4f0RG90Pzo6uyC3KxLqERfjk91dw7xXPItgvTJ736gn34MdlH+CX5R8bjsHb3R/3XP5POMJTOg38w/RjkhXaBzo8GaAokDUz490EvwRkyJoxChcJ/k4tgTHImHkd26q0HmVmGp775wv487+7UWVbK45vA3oNw7s/P401OxZg9kT9rPghseMwut80YdHz1n6DguJsOHs4oLSgClsXHML4m7qf44/CxQ+mnEuKShHq0geRccNgZdeAfYfj8cOyd3FZ7a0Y3HOs3M+4zlldVYO62gbUORdhW/JKQ7BTOAHW2LkzJwluqROgtXo9yfUDDzwg30mtF76lOja/pyJmc3KSjANbClkOoF7B0twI7e/xOJnp47/5HOzsIbE3dZO80FFRUoO0hDzxAagso/Mg1y3A3cdJPADiU9YIEWJ3DNdAtsPyYg4kx8GeEbh2wv1iZ030iOiH2MiBsr4t2fyL3MZMjaODo4zJZndBl049UF5ZIuvf98v+i5smPC622ArtCx2eDNCGlIuUsQqcKn+mNrWBMtoOx5xCmTsYkgFmDrRF6sCWbFSV1sLJzV4Ig69nIPy9QoRFazCtpxFs0aFzV2ZykfTucpqcQtth2NBhKE50Rc6hMjh72Mv5Hh43Ce/PeRbrE5cYyAAV0MbQ1dbj1+WfoapMp2a4mwGDNnfj3Mm31AnAoNNSvZ7CXE6FpNcAd6faYC/TDg3N2ZE7fta4OaeA+gOmsfk3ja2LzWHWrFkS/Jkd0DQCTJX/5z//kZ8XAxhsty48hMO78lFbXS+fdablSQRICDQPgOx8K1w99m/oEqlvCTwd+HoGCCnYm7YdxSXF0jmwYPuXiAjuhpmjbjLcL6pTT7z945PYsHcpZmJwG71ShbZChycD3PUbpyG5mHGHwbYbjSFro4bN2QdruxfWLgnWnjkhTibDHZ8hLzuYqlIEeLXessQxsxUltVLP7nfJxbEwtRdkHSxG3tFyOLraGkb58r33cPXGsdxDFh9nZd2ApKM7EOIfCU/3ljtHOiK0QTUkAS11AjC7Zq5eLyns44Scu3zaBLMHnhdm7fj9Y4DnbZqpE//N7yfT+hrZ4L85UZJCQoKEwNxOn99ZljZIYDgsTPubxmWDCxHPP/+86JXoAlhWUC0klsFf+6z/4+Z34OTggh1Ja7HvUAKyC46iqqoS3m4BKKssxlNv3AcHR8vGQ3weO0dbVJU3d5X0dPVBfX0damqrkVV0GDkFxzBt+HVN7sNNEctyx/JSZYCWQvtCh39HjOuZzAD88ssvsqhx0IxGErSdiVZvNIamVmaNlKpzV1tv5B11gqPzCVvTnckbUVpeiImDrmj1DdH7rFvhYEKeIgNtjJRtuWiobwD/q6iqRHVtJfYfThD9Ru9oyzuVpCOJqKmrQvdOA3F4dz5iBga29aFd0KCWZt26dZIe1loLzXUCaEHbXL2eLW5aSyCDda8ecXBHCI4dzsaeXftQmJmOqMHRcA9iR0KQpP1NbbpJ1tkux+8QCYFxZsG4y4GEgsfK7z4JCrMDd999ojR0oYKmSf369pfupaKsCtg72cla8vuar+Dl5iukN7vgGOav+w5dQnpgRJ/JcLR3xoFDifh6zsdIz0zBF3PeN5AHc+jc3Rv7N2UZSJmGgtIc2NrawcbaVr5X5kgfwXNu72IPn2Alkm5v6PBkgIFfWxw4DpULCHcTxpPRtBqktrMxBgkEdyAU3ixatAgetoEY0+UGdHbT6wtYGqAmIDQwGv26jjipN8Xaxho1lToJXPy3wpmjorgGR/YXws7BFgs3fIute1fJ9VRD94zsj0tHWm4ZJJljC1X3sAFI2ZqjyIAJGNyXLl0qO2xmCSx1AlBHYKlez+8bO3WKsiuQsj0HB3fkobpcJxk2h9pAdLLzRs1BN+hKneDgGQDrRvM7WH4XaRzEDAWPgWUJGhvp3+sTw3lIEkgoWM7gWOlnnnlGygQXcjcBS5TWRd4ocPZCTJydiAQPZyZDp6tBn+ihch83Zw88fM2rUs/XwPLYL8s+xZqNK7Bl5W4MnWB5jLNvlB1st1mLjsbOQZ8pzcxPx/7DO9E1VJ9J9XLX+7PsSt2MrmEnnovZt4KSHAwdPkN1ErRDdHgyQA91BnJ2BDAlSRtTzVvAuB+XuxDudEzBliouci+88ILeZCUxC2u/TxWVDlNvXy94S9j39ZPvP+mFRiPcFOyYtPAqnCZo7MR2JgcXWwyPuwS9ugxCaUURdh3cejxN3TzrQ1TXVuFAeiK6hsbBjUNw2IZosivq6GBApdiW7bkk08wAWOoEMK3X8zzy/gzeV142G+UF1Qj2icDkQbMR2inyeKnNQXrat+5Zhb1LdyDnvWNosKpD995RuPaGq5vZCPPfDIxsEWa2jt9vaoC4KyVh4feb1sca4ad47uGHH5YuESrqL1TU1zcgeWuOGJpp3QI7UzbJghIXoycDLk5ucjFFbPQAbE9ah41/JbRIBv7z/ivITimHt31nePt4I68oE1v3rYa9rT0mD7kaVeV1CPIORVTnWOxIWi9lg+jOsSitLMaGnctgZ2ePux+87SyeBYXTRYcnA9wdcFfAoH7VVVeJVsAcqHRmDZMaA5IDrSZKAkGhofY4Nw8X2c1XVVXgywX/QVVtBe6+/J9wd9HXVU8GJAF2Ru0/CmcO1k+1IO7vFSwXol+3Efh8/r/x1cK3cN+s55sF+T0Ht6GuToc+MUOP21I3Cqlge5aCHvzs006YYly2F2oug+YIk3G9npk27tzZ48622v6dJqExpAE7Dq7FV8v+Le+Hn5c+w1ZYmoeFm36Q9PaoflNhXW+H9Pz9ePmlVyXTQDJuCpICkgxjHYM58FjoLPnuu+9KNoOZhfaM6godstNKDIp81t8Dwt2Rd7RMupFomkWwhr87dQvCAqPg7d50g2MKqv1ZHqgutEJxTiU8A8xPSmV77e/587F1x3LxDqD5UGzkAIwbcJkICQ+XHoK1nTVumvow1iYswq7ULThwZBesYYNQvyjc98B96Bl3orNDof2gw5OBb7/9VogAd/VMJ2qtURo0ESHbp9g1QIMhpj6Z5mR/NMVRN910k2Fn4hXgDFv7Rnw2723kl+Tg9kufaJKSOxmwFzikq5fafbYhbO1s9BkXSRM3DVK0Tf1j9Zfi9aAFHw07kzfB0cEZ3cL6oL5Wb8aiSJp5xMTEyGeWJQPuxFmrZzreeNdu7GbH2/jdc3F0x5UDHhZxm5OrPQbFjcabPzyBpVvm4IbJD5pNb5PY9SsZjWU7v8OCPxfgjjvuaDXotwSWBmlk9NJLL8la0N688/l66WKauj0XhxLzUFPZtDWPhIAXpu+d3fXnO/noblRWl6NPTMv2ynX1ddiwaxm8PfwQ6BGGYweKLJKBa665Ri5H9hVg3c8pqK3SwdHF3pD21xwk2TI9fuBlGNt/JmoqdCKs7j4sCINm6I2JFNofOjwZYEsgSwBsZeJO3zStqZEBZgO4YCxfvlwmr1GsxMWHrVDGfbm2DtZYlPgljuSk4pYZjyAs8ORnhBPcdfIYYgaeGPqhcOZw9XKQIF6na4C9iQ6D1qmEJnzSUFpRjLSM/ZI9oDiqtqIG7n5OagxrK2JCpv3Znrt3794mOhutZs9UPdP4/N5wsmEnr2jY2zjD0UW/o3V38ZS2tKTDOyXN7GDv2Cy9La54bvYI94nFniOb5Lt7JmSAz/f000+LmPHRRx8VI7IznU56puCIYU3YSM0LMwKaTwAnBIYH69cWBtqU9CQs3vATsgtpK+yEvt2GoLSiBNY2tugd1XIb3/y13yC3MAM3T38U1jY2J+UBENrDB+NvtsWm3w+iOLcSdbo6WNk0wtbGHmgAKsqqgAZrcfN0dLVTI4wvAHR4MkDnNIJio5UrVwqztZTiZK2RmgLeh8In1hxpZWxsWPT2229L205MaG+UlpUi4cCGJs+h9bAXleYjIVl/W8bxtjbadZLxB4cE4abjNT6FtgEHQbkEACVH68XLQQNTqTsObICtrb14QRiDAqjGxgbZWbE8wIU4eoAiaa2B2TJqBmjQxHo9OwWYSWOJgKSahIGEgKiqqEFNYyPsHW2afOdYg+Z7k1N4zDAW1xTUE1RUl0qQ9HBv7ttxqqD4kCJCzp947LHH8L///U8Exucbg7qNg0O1n+y+bcUrwAp+Xic6WrIKj+DbZf+Bp3MAxsXNQllVMTYmLkNRRR6G9ZogToGWsCZhIbbtW42Jg69Et7A4MSniZ/1kYOdRB+/+Zcjaehi6PEdYV7vB3tYRaLBCeVkFbJzrERbniSETY+Djf/JlUoXzgw5PBjTQsIRpTRICpji582eQ17wFuKPRxqRqCx6zAqZiQ2Ya+IU9nL8XSek7DbO+m5GBsjz8teW3Jo9duulX+TnYeaDqIjgLWBL/PVITMxDZuTu83L1RVlkiZQCKoKYOv052oMbgbdR6RAZ3k90SXdci+7Rce1U4AQZSivhagpeLP45mp8LGzqpJ2pozOwiKPC2B99uWvAJujt7wdjKv9TlV8Hv91ltv4fbbb8eLL76Il19+WYKvrqZe2kozkouky6GxQb/jDYryQERvX0Odvq1RWVoLF10w+vQcYlDvm2Lp5jlSYrll0uNo1NFDwxollQVYmTgHfp5Ny17GiN+/Dks2/YJBseMwbsBMfadFI4SYtZap0IZTabMLONStU3gYUG+NtENp2Je0C0czj6DopyJ4/stTjKTOJHOjcPahyIARWCdkZwCVzkxzas6DWnqTXzJ2HzDFSeGhuYEoWqahrrYeq75LwrGkIjES4hfZeOcTGdIdr92nn4nA5y7OZ4uiHfpdEo64cepLczZw+ewZ+G/K59i0azlq6qvgaO+IYN9wTB56tTioGSOvKEsyNuzFZqMBywtdBwcaUtkKbYPBPcfh2+RPMHf15xjVd5p8F1bFz0dpZYncrjtewrGU3s4rzsTs0Q+gvMDy/U4VFAvTwOfJJ59El7AYxIWOlvHjlSW1+llWVnrVCXfQdPhLWHYEkXG+6DkqBK5ebTeKnFlCEg8KkhusdGDbvumEQHa7pB7dixFxk+Hi6oryohrAqhFVNWVy37IK/Xk0xb5DO/Dbqs+lrXbmyJtOlChtrFqdGcCsDzdMJALaGkgSZWXbANg2YF9KIvYn75dBbtrYak52pWU09RgK7ROKDJiA9U6qnTmQhjoCEgJ+mJlC5AeeA1ZOpkWQanNOIGRN7VBiPiqqayVjIOnQ46YedArjF541v9LyYuQ27sdNoy/+WernC1OmTMaQviOx7LO9qCitgbO7g0WDFQoJSdYo5qwur0VwtKeMmFZoWwzqMRbpB49hU9IyaUUjQvwjMKrPVKyOnw97O8dW09vRnXvLzr0tMXHiRKTuS8fB1dVYk/8h1u6ejwCfTvi/614z3IdljJXb52P7vnUo/bgYXp7euOG22XjgkXvMupWeKpiFIOGYv/FL/LrmI7MTAukiyLbYEP9wODjZSiahorIU6XkH4OsWjKz8Y82eNy0jSYYIRQR3xTUT7jWsZ1yLfDu7SmeCJbA9k1kBEiZqrcyVUznqmxbRPAc0cWPHCB9DH5brrrvO0I2l0L6gyIAFMPhTHX1GJ9feBiOujka3oUFIjc8RUlB1PMUouwtrKxG2RQ8MQLW9M+5/5DV8910nSbkpnB14Bbpg/C09sPr7JGlnq2+og5unc7OyDElALYkaW+e6eWPUNTHS7qnQ9l0e4/pejnFDpiO3MBOODk4I9OksqW/Nwra19Dbr3Lb2bduGy+cM0PVFmcsh/Lr+Qxm8Yxr3fl7+CXYf3IoB3UYh2C8caUcO4L2330d+QR5efu35M/r73CBkJJWgR1g/xMb0tzghkF4mhJuzp6wnLGVtOxAvu/Zgn3CUlLHMcqKDhlqlbxe/I7+zi4bHT9TXN0JXXYcpg4a3KJDl0CcSApZULfltGBu2GRu3MZtAUenQoUoP1R6hyMBZhoh9Qt3k0ndiGAoyylFbXSdfOKaceb2+Va2zkICPPvpIvizsd2ZmgraqnHvALyBrsMxOsJzBuewXslva+YRvJ1dMubsXvvnvPBSk6WBrE6avlx6f4iJ+7jbW8ApyQcygQEQP9JegpdD2cPN2QHZaI1wcXREefIJ8pxzdA3dX72Y1b9P0Nl06CXYWtCU2/3EQxTlV2HhgAcKCoiS4VlSXGW4/lpsmPfwkI8xOEEN6jsMfK93w8w9zcNMt1yGm6+lvJqordfBx6IzrLnnAIHg1nRB424zHxQODsLXR34dryv5jW+Hs4AZvt0AUledJBlKbk0KtUnVNpaHMguNUgfch2Y0Zy0BuPjvJYE5TqRUrVkj7KNc2pv3pPcD1qHWbdRspv7I123T4lML5h3pHziEoOKJ/gCVQnMORr1Q0072N41i1vl1Nt0BHNbJrtj31799f0nXKDe/UYesE/Lb2U0yaMBkjJ05E3pEysYDmoskFldmAoC4eqo3wLCMizg+pO/JEk8EyGpGYsln0GlOHX9uE8JpLb1eV6ceEh3RrO7U6LZEzUoqRWZyKPWnb8ODslzB/7bdN7nMoM1l+9o4+MdKaBL9v96HYvH8Zfv1pPp567rHTPgYKVsV8zMR4zHhCoH64k51BTElwY3H/1c/JoKLfNnwiswIKC4tgY2sl58vPvRP+ectH+jWFGYB6K+iqG+Ad7IpJt/WEi6fl7gl6QkRGRop7IzuumP6nm+R3330nXivUCLQE6gvYWZKWlnbGWVeFtociA+0AFCxy2tjWrVvF9IiiG7ZkjRs3ztDrzC8+WTW7FWiLzHQd569zLjzd18yJGRUsg+pmLkzX3XANOnXyR1T/89tT3lGRVZaGn9e9g05eXWUcOP05WAaICeuN4b0nGe5nLr0tM0Uq6xAZ54fD6V7SstgW4FyE2po6LN7yAwb2GC1lC1PU1et35DTXMQa/h0wybd+40+Lz87vMbF9OTo4E1Nzc3GaX0sIqjA+/DUHBAc3cLrUJgbV1NVIeILRyAcH7u/k6SYuhq6MHqst0MmirrkFnSO3bWtvD1toODY31yK1Ix5b18/DW7w2iA2Cg50X7t/aTmDx5smFKJc83NyOfffaZjI6mGVFL0HQUdG1VaH9QZKAd4KuvvsLOnTul9YYlAvZk83can7B0wPZFihhJGChgZEaAX9D09HT8+OOPQhB+//13lSE4SXAx5m6G6U1L9tMK5waBgQHwDnTFhvglqKuvgbeHPyYNmSXqeGPlvLn0NnfODGweqU5wDKiV4MQ0NoMsBWu8jcGZHUInm5Zm2YHjwxNSV6O4vAC3D/672fv5HdcypGclN7H6Tc9OkTLTsSOZWLxgGQqKmwd6EgDjCagMkiT9/J6zW4mvw88nADV7vWGF5uUpbUKgva0DAr07yXnKyD3cxFyIBlrZhYcRGzEIvUdEIutgKep19cc9BBphZQN4hdrCJaQBjY7uGFXVRQyieOFGw/jfPGb+ZInStJWagmvu8rkGaVnM1sD1TaH9QZGBdgA6G9LMiB4HFC7yQnc2Mu5NmzZJSw4XDKbijIMX57dTmcvSAokCfQ8UWgcH6tBpju1jCucX/Dx/+8vnWPdLCtIS8sRS11w/vWkrbkkBg1UZxl4Xiz6jI5Gfn481a9ZICY224tp0QgYnCtrod9CjRw/JprWE2qp6FBeXYNXOeRg/cCZcncwr32lP7enmi0Ubf5LsQIhfBI7mHsSyzXMkOFdUlOM/r78DnXW5BHpeWF/nmGbtd+3CjIi5ILr4q+3I3lfdRKhnPCGQj6FVdlTnnmJgRv2C5pWxZtti1DXUYdIll2DCLT3FwZCdBpzRQW0ATbhYtjwV/Pnnn6JhMgXXIJIbTdfUGpReoH1CkYF2AAb+H374Qb70JAIa4+YkOC5yBMmAuV0sxYQbNmyQiyIDJwdmBegVwYvC+QfFmsOu1LsMHtqZJ6p2e2fbZqJNfj901fUiwM0vO4bvVr6J137PlN00AyqzaswC0N1QC64kfty1Un/D7FpYWBimT58upkJautsY1C6siP8Nzg6uGNprosVj5s78lumP4oel7+P7Je8Zrps8dDZWbZ8PZ3t3fPn5Vwjtevrlp5+WfYislDIRVnIgUK7RhMBLBl+N2sYK1DfqMGrgJHzx+1v45PeXxbchOz8D63YuQVRIT1x5g37oErUALekBTgZal4BpFwFLHgzwdJhsCcwcGI+EV2hfUGSgHYDz35mKM6778wvHNB0JQUtg+o5fTLqBUe1rrq1H4QS4c9yxYwf+/e9/q9PSjsDd6sjZMQiIcMeBzdkyOa+6nF03x5s8GvSqdzt7a1j5lGLuivfRqYs/AnReQgS0MeS33HKLlNA08HvF0hoJN78nJNckgyTPJOCaLbKGrLwMxCevxZQh1zZxP6RGoL6hXqYnOto7SasfhyZxeBKDdFV1Bfy9gyVLsGDd9wj1i4G755kFvckzJuKzd3/AuoTF0DXWwtXJDT0i+mJQ/2GodjuGdN0BNPKseAGXTJ2AzVs244/1X8Cq3g59IkfghivuQFAXvaagLUDSxaDPDIC2aWFJhgOeKCxsrUTAEg7XuKgo8/bSCucXigy0A3Ah467FeGGiWJDBneORWwLLCHwcW3v4pWTpoKODRKogowJZqcWoOT50hYYsgV088N2330mGhcYoCu0LNIDqNiQIXQcFIutgsd6sq7hGP1zKyRbeQS7o0s8Pz778JNw9XfDQQw8JudPGIn/88cciZOO8EA3G/yaoJWCqmyWFdevWYdKkEyJFoqSkUAjIgg3fY/HmH5od4xvfPophcZdgxojr5XNW1VgEe4962HrYoRoFSDpyUEhDTHgsXLzObCfO0uGUcTOx9LM9qCiuRb1bAfIbUqFDPnSNgA1sYQ19AA4O7IzLZgaiobEBjXXW8LYJx7jrerVpN8w777wj5TVuOJi5LCgoQEJCghAE6m80aARByxqQBDBDw3PP7I0xWVNoP1BkoB3A2PaY4O5l2bJlErSMJyKagrsbehGwo4CEwHhCXEdEfV2D+Mcnb81BXnqZ/G6YVkwbARug/lAgrpwwWHaax9fRiwIkjhyxzZQ4PwdMyXL3RlEqS0ncHZ/vFlTuKA8ePCi7dQYIlr4YGLhTNM6AMYAFR3vJxRwYgCii5XPxNWokmjV5BiEK1LSdqym4M2Uw4zlioDJFVHQUnn78BSRtzIaDi63hnC3b8itqddWYPuIGeLp7o7j+KEoaMlDTyO+cXp9AQr94y1zYOwO9hoejuKSwmeDuVPHOR//GH3Pno6igBHWNNfrzYzSC+5br74Srqxvid2zGofQ0FJcUi4Wzu7sb9uauwSuvvCLCv7YAA/5PP/0k55/vJTsMaMtOIkZyYEwGSLaMwUmv/Ewyu6DQPqHIQDsAFxFt0SExoIUnhTiXX365xdQb2xHXrl0rA5a4MDIYGCuUO1pb5pbNW3FgdxpsGhwQ4tsFlwydhUC/YMN5/WX5p2J3y3P0yxprvPSfp0VEFR4Rjt9+azow6kICPy/cdTG7xJ0XPy+aLwV/37Vrl5wjCtW4K2M6tzVQaJa2Mw95R8tQU1EnPers5Q/t6YOQGM9THqLFzyZbZpntYjnLtO68efNmIS0kviQGraWbGYj4PCQUxmUxZgj4/lKtT/KjQXbwVVUSjNjWxowArzMOYBpYcrjm1pmYW7BDyKQ2i+LbRe9IOaCw7G3oGqvQiAaUlpaIWJA74+FDR+PQ4YMoKSlCeHgE3vjwBbz45j+EoHAK4q233npaFsVXXHGF6CC2b9su4j/U2cixr9mwAm6u7nB2cpEOiNy8XPh6+6Fb1+5w9XRGXl4u/vrrL8mCLF68uFk55HTA1sHZs2eL0JmEgITLnGDQWI+jnXu+Z/wM8HPKx6oMZvuDIgPtAPre5EZZvH/++WdZ5NhhYKn+z9ntVPbSpXDKFL1AiLC0G7rY2zITEnYi1C0WY3r2Q21jObbsW4mP/3ge9175HAJ99KJLxh0Gi9kT75ZFniI0WhOPuLwHLkQwC8DpeiSEXGhZJqKfvqnxC4kmyQJHdFNfQgEdu1JYWzcNToVZFdi3PgPpewpEVS8Bm2lmDrNrbETK9ly4+zrKwKauQwJbdGXk8X344YfYtm2bBGDu/DnghkHf+O/yebmTpxENsxq//vqrkARqOsaPH2/2uRlgGVCMDbdIAqi90ciHMZgF+O9//2v4nd8r9stb6nd3crNHzKAA7FmTIQPH2Lfv5xUMO3s7DB0+BPWohQ3scDAtFTsSt4kL4N59uxAUGAwnByccOpyGvv36CNmgJwjfJ+6Wn376aZwq2AFBY5+u3bqK8I4ZrbSUw2Iy1DWmu5gM8T264oorxYpYcxok+FkgUSYpYEdSW4DnmyU2vm/8TPG94/pFQmScedLeV154Gz1TbrvtNjz11FNirsYSD+cUnO9slcIJKDLQDsCFkovZnDlzZIFirdBSepEOhNzJcregZQ60zEJrYsOLESRNU/vcjLSEIji52cni2KfrULFsXbNjAWZPvMdwX2sra8MIaRKCqvJa2BdceGlL7vYYzPm+9+vXT0ggfSm+//57ud44Lay1g3GnxuCUnZ2Nt99+W34yOGlBe+umeJnVEOQVjslDZiMsJLJJvfnT318VB0B65jMpzoFbzKwMGz4M7733XrPj09T6DNgkucxO8HNrenz83HJ3yQsH2VD1z+ChKc/NgYGc7bRMPQ8bNsxQMtPKbSRHxuCumN8pni8GZZIOBiv+21JJod8lYSgtqMaRPQVy34evexlH6raJgt8WnFNgBXc3TxxKP4ghA4ejf99ByMnOwq9//oRRo0dh9Gi91ocEjISNJP/qq68+ZWMkkiSaY2lDgVjqSj3MMek2GDi0Lzw9LdffuR7w2Pl+tBUZIEjmSNRIQEnKWPbh7l+Oz8rK8N6RfDMTxc9oeHi4XEe79Q8++EA+g8xaPfvsswZDI4XzC0UG2gFo2vH444/j6NGjsmBYMsKhloAlBPb1XnXVVYZWHi627J+OiIjA+QaPJTk5WQyRNA0DFzLu5lhfbIt0pTHCg6OR+EuCjInWz3jQD7fx9wqRtK4puFDRuY2KcDsHW9EYxI3vDHfftj2uswkuqMweMYVM0khCyGBvKqDjTpnBnjVdClG1DhUG0rlz50p56cUXX4Snmw/6dZ4EXUAddqatxVdL/o37Zj0vkxuN4eHmjclDrjJkVnxCXDHhmn5mj4/BnUGbnwfuxOlHb07gp4GpfQaHIUOGSCaDtX9mOsyBGhnueNkVQgEhQXJMC2++NtPgzuClfTcYjBmYvv76a/ksWiID/CyNviYGS79OwL7Nx2DvW4NaxwrYWtkbdrPJqfsl5RQVESMkKacwWx7Xo0d3w/Pwb7D0wddGHdCpkgGm1rXXQHDTwPeUZRCWNIxhWg7hudYCNB/XFpMUNfA5mbWgNTE/ZyRYJC1axwAJH28z3aDwGB588EHxfaDPB7NUb7zxhsXyFc9rVlqJdJewrdTGzlpIf+du3iIqVWg7qLPZDvC///1PdvxM4fLLrC0AGlhL5aJF8Q4XVy6YmlpXWwCYhjufZh7clW3fvl12gNwN8bi02i8XJxIELtTcKdJ4pa3GmNI6VlfbABePEws6/3Z5VSkCvE7UjYnaulo8/9nd0Olq4OTogt5RQzCqx0yk7shFv0kXznhinmeSABJA7RwzhWwqoCO5JLgoaws4iRkXXgaUf/3rX3Cwd8DVg/8PjTV2ogsY0mc03vzhCSzdMgc3TH6wyd8lgdIyK1T411ToYF/a3I+eu0USXAYG7vr4d80dnzGYyiZZZNDWyAB38uY+0/wOMPBTA8FMAokHNREMfoQ5LYAxSLZ5HkgmmBVh6YIXXs/drnZsDDx/xn+GmjJb9InpCVQCjfVWaLRqkI6BlIPJCPQPgo+/DxydbWHvpH8vjPvttXPO18y/dypghoPrgvHzMVPA7ztFoaYwVw5hRoCPZ6Bu7bycDvj6SEyMNRonA65XJEncBJEQPPPMM0LyNHBOSNpOioH1baZ658TjaNTPeWFnSVT/AOkyUThzKDLQDkBWzQWDnQHUA5gKqEgGGGA18RXToxrI+LlgtjYk5GyK95ii5b95HFxM+UU33RGQEHAR5/yFJ554Qhb6Rx99VMRVpwvuUFO258DGRr/70bAzeSNKywsxcdAVhuvo4T6q71SE+IULWUg+sgtb9qzAsezD8PT5J+LGdTZkFto7+Dng58VUvGUqoNMEpcbBhJ8t/s7PEz930ydcicZqO6mTS+rbxRMRwd2QdHgnamqrDY52Ghoa6qGr08n1OlsrpGzLRa8xnQyT9bQgpin1jXejlgR+JCYcgnPXXXdJ0OJxMLDxu8AdJtPcvPB4+ZMtbZrBEIO4Bt6fAfBk1PP8/DFAU9NAfwLNIpd/m4p3EgMGXab4H3jgAegaKuDh7aYnAw0QvYCurgZ9+veGu4/+HGnBliTMeNfOkgw/cwzkLYHnhq+bxJoXvk5+Z4yzacyE8JxyV24Kc+UQvhfad6+9geSQmp9XX30VDz/8sGg9eO7r6xpRXlCNuroG6Zu4+4p/IrJTV9TqahCftBZ703YgK+8oav5XDV+PAMy+9irc/4/b2jTz0RGhyEA7wKeffio/qbSl8pcLE3dSxgGOiwvFNxr4BediwcDAuvn5cNPjFzk+Pl4WQdZuuZhx98PrtZkKxkGIAYzpUh43j1+r8Z4uqst1soMwHuTC0sC8td8gNDAa/bqOMFw/eejVTR4bFz0Evh6BWLr5V8Tv3YTpZX3heoZ94ecK3G1rAaYlAZ2l4KR54zPYleXVAoF0ATzxWaPDHQfh5BQeQ2jgCYOY/JIcPPvpnXKbq7MHBnQbhaFdp0qpJWbgCTLKBZ2fZWOPDEsCPwYrqtMHDRokx6iRAV7PvnaK57TXzMwBxWv8ydfAiwaSUYr1WMvWyDS/R3wuU2c8kg+t5/3dd9+VzyJLcCQkPFf8ycA9b948eSzb5EjISRC08516iHV7W8TGntihc6fLbA1fDx9HssKdPR/PY+J7xrKMFuy1wK/9NA3YzFAMHz7ckK3g6+F5ZfbEXLnNUjmEx9Jex53zdfCccL176aWX0DkoAiNiZ6Kuht4SPG4rBPjoxyPT8Gn+uu/QJaQHRvWfAkc7J+w7mIh33n8TSan78NGXb5/vl3NBQ5GBdgR+gfmFXrJkiSyY4j1+XKlLcPEn69cWDe6+WVJg7e3zzz83a6+q7aD5pWpLAxLi+uuvlxYhLp78MvN4mb40nqlgDC56zCJwgWNKl8d+ssNNzEFXUy+7NKvjn2JObvt6wVtwtHfG9ZPvb/V5R/SZjGVbfsPBjH3Q1dCc6MIgAyR+JI2cvMiSkSUBnaXgxNY6CZK29khJS8K0QSfOE1XqR3MOyr+NHfg4QIjzAdidwT52Tg1cveNPZOdlIiT6703IADNDLGUw9d/S8RH8nJAoaEJADfweMAi+/vrrUlriv7X3k4Tz22+/le8DXx+DLJXt7K6hNkEDsxN0GeQuWssW8L783JF43HvvvXIdn1ebE0CxG8FuBn4fKepliYvtjxoRsBSU+T2l5odDw6jJ0K5jf77Wd8/sBYkNy2R8Tfw3vzvaT+3C3/ncFIBqcxa0nT7r7ScDkhc+Dx/Xnqea8rxOmDABX3/9Dbp6D0O4V2/4Bno2W6/cnD3E8ZHOjxoGx47DT0s+wZIli7Fl1W0YPNayL4tCy1BkoJ2B9Vymzrl74aLFmigDJr8wXBS4SHLnxYDAHRJ91tmyw1YdCrS4+2bfMeexp2zLQe7hUqnvygQ3VztE9vGTWpu7z5kL5ngcLFlwodEWatOZCsYgAeCizF0W06/cBXK3eDKTA1l/JumgAxp3b/yZdSQPEY1j4efvK4YsX/75H1TVVuDuy/8Jd5fW59szGNJWtrq2otmY2PYMvvcUiDEoavoScwI6S8GJu2u+bzbWdZJJ+WPdlxjdb5p8vlbFz0dpZYncl0Ffw6xxdzQ5BmoH5q76Apt3r8Kq1Svx14EvhaTywr9B/wvu1ls6Pn62GWRZKzbVEPA5+FlhkDAFgzaJL18Dsxv8zLFtkc9vTABZMiCRYPlNGw1O8sDP38yZM+X7Yw48bpKARx55RD7jzDgYZ+laCsrMht15553y+ee54HeBr4XiQWo3vvzyS5wKSOh4PPyO8SfP06mIEHmcPCetDWhqD7ButIWfZ5DMUKCPg+nERhcnN7mYonfMQOxIXo+1C7dj0JhY1a54mlBkoB2CixjTplR7M+gxS6D5gXMRNN4lMZC+//77uOOOO0SM89Ct/8TeNZkoyasWJS5FUGTYjQ0NKCuoRsJfR6R/ulM3LwycFnFGqXHWL3lcJzNTgUGfJQJNI6ApnPkcGhnQBFPGAV/7yRqotkPSuhNCO4fDudpR+rx/WPaupLFvv/SJJjuHlsCaeEVlGdxcPE55gtv5BLUCzK6wS4BBpyUBnbngxM/O0qVL0bNbHHytohGfshIJB9bL/UP8IzCqz1Ssjp8Pe7uWd5Mj+0zB5t0rkZ6VguBAO9lta5ksvj8M5CwJWTo+EkJ+1imcJTEgtA4UbYyuucwRPy8U/jHIz58/3+CGZ9qzzs+JsQ+HCEvLy+W+zFqZ63Hn33vttdckCNNgh9Ba3zRFfmtBmc9rXCKjGJLP25KbqCWQcJB8sBzIrAKzHKZlD0vlEB4nSTRJUnvv5+daVV2hw/yNX2Hu+k9gZWWN8OCumDpsNjr5t2yUVV5F8ycr1JTYoDCzQrpcFE4digy0YxjXAFsT8XFx+vXHP7B64TaEBcRg8rCrxIFPw9a9q7AzeZPsBCurK+Dq6IFu38Ti2X8/jh59Tn1wCBdgirlMzUbMzVTgIqztjLiQaws/Xx9rwkx3sxWROzCtR5lBRVN5s0WNwYX/ZuAgIdL+5qY/UvHcy08jPScVN019GGGBzRdokgWqv03FcCu2/4GGxkaMHjtcBuVcKOA55HlmID0ZAZ1pcCIBY4CdOeNyuOb2wCUjLkNecRYcHZwQ6NMZSzfPMbRotgQPV29Dxol94xr4PrLzhcGppeNjgGMqn4HdFCtWrJCuAooKLZlvsSbOrAI/W/zM8TPDv2ka+DQDHM2xcNq0aUJOzIFeCMzKffHFFwZBGv+OZmGsvQ5zQdnS94QlAqb+Ke47VfD8kcDxNfK7YS4bYa4cwveApRMSIqr12ztK82oQE9wX3SP6iLMi16l1CYvwye+v4N4rnkWwn/luH5a1NuxaBm8PPwS6hyE1PleRgdOEIgMXOCjW45eeRjsxLqNRUV2KnWnr8NHvzzVx4MvMPwIvd190j+gLJwcXFJTkYvOulbjmqusxf/FvCI9qPVVvDKZnTVu/LM1UYEaAxiTsizcGF20u0tw5UvjFhU8L+gxeJ7Ob+WvHL0jJSJT58lXV5Ug4sKFZOru0shjv/fKsiAa13vmUI7uRdDgRMZ174aqb286Q5VyAugwGLJ5/LSCZE9CZA883gyyD4ayrrsTy/x2AlbWTjMnVkHJ0D9xdvSVl2xIo6GKyJiCoaRaInS18fh5PS8fH1D4zAMbgZ4jBk1kxfl5a86Vgqp87d74mPj+zHwzixm2t3NGTLHO336/3IJSlWyF+72E01DfCzsEGPsGuCO7qieLiIjHEueyyy5oIcvkaGIRZ4qB+wFJQJliSYeBnACf50ASzFPlqLZ6nAr4OajBImPg9IRk2hblyCI+BpRqei/vuuw/PPfdcM12G8a48+1Ap0hJyUZpfjdqaOtg72MLD3wld+vrDP9ztrGcWQny64KpR98LZQ9/V0iOiH2IjB4p52JLNv+C2GY+bfdz8td8gtzADN09/FDa2NuKiqXB6UGTgAgcXmScfewaL3t+DOvcGaREbHDeqmQPfZaNvbvbYHuH98N4vz+HTt77Dqx/+45T+LgORsZrd0kwFLohMD1NIZuotwIWKOxkKES3t1FrD0axDsqDvP7wTB44mGo1wOUEGnByc0T28D1KP7sGOA+vR2NgALzc/jO41E1dfcQ18O11YY59JrJhNIWFiloQlGHMCOnPBiU6FzMywHz0ozEcmOR7bX2RoDUxM2YyM3EOYOvxaw3tYXVsFW2tb2Nqe2AnzvV+xdZ7YPE+ZOb6ZzwDT9/xstCTwM84aaODunsGW2hna1Z6MdwbJJ1P6/DvMmDAo8rUSJBNM5/s4d0LegVqs/eoI6mrq9QOsWHWy0ltV03QqKXsrnOxcpZXQHAGjRoPnj+RDc9QzBbURfK08ByQRLOmxXEKCcbqtb3x9FEyytMPsBF+TcUbOuBzC7yWJuqYN4WNfeOEFMfrhOeJPrSWV7yF30vs3ZKEouwL19ceFxtQnHTf7Yeuud7Areg4PRkQf37NGCigG1r8XJ57f1zNASMHetO1my0VrEhZi277VmDj4SnQLi0NVWS1qj08pVTh1KDJwgYM7mMQVR1FbXS+sujUHPmP4ePrLQngsLRcVJTVw8TCvH+DujWl8pke50PLCOj53KdIXXF9vcaYCywD8InNXpJUHtNYyLlpMFZ/JAkPDJh77ss/2oCi7UoxzTAfpMBNy9YS7Db9TYFmUV4aswiNwiDizv38+wJ03gw49G5iGJiEwJ6AzDU4MEHy/qCGg8JTB7Zul78OxLBju7h44ln8Q8fvXISasN4b3PjHaNzPvMH5c9qFkVnw8AlBXr5MFOu1YMob3HY9RUwY1+ZskdtQFcIfPz44lgZ85aJ0GJA6nYqLF95CvlReCEw0/+eQTyaAcOZgFXSXg5xGEEXFT0LvrgKa+FAc2Ye3CxcguOAY7e1s8eN/DuPOe20WToYF6CGY1+FnXBL3mQNKrdVDwtfAzzuzFmQ7mIaEhCaBoki2VfF7jlkEek2ZLzvM9cuRIg6aBxI9EnW2UzCRKacfRUcZDs07P7I6eBAD/uPkdKf8Yupd0DTiamoMn37wD9bbVeO/DtzFhYnNR55lCPD6MfIU0eLr6SCur5hqqgZ/TJZt+waDYcRg3YKbheC8kIXB7gyIDFzjqdQ1I3pYjIkFtgbLkwEdUVpeLcUxxeQFWbp8ni0Cob1ekJeQhuJeTwfjI+CfFe8YLPXdFXNy4EHHBo1Ld0kwFdgxw4WLQNgXbykgWaK18JqNeSWLG39wDq79PQkFGBaxsrODgZNvMRIgtljVVdWisb0RghBeO2K3Fk898gpfwUrO59u0Z3O1999134vFAER4DgTkBHcHAxCDM94kpdD6WmRvel++lu7cTNiT8hfLyCiGHk4bMwoi4yTKNT4Onmy8igrti36Ed0r5JcZevWyCmD70J9z/BgUfNBX60JGbanpkJ7mZJEFsjXSxhkKywK8ZY+Hc6YJaARKRHpyEIs7FCg5UOB44l4KcV76G6/lYM7jlW7rdx11/4c9236BreBwN7jkZleSX27t6CB+57EG++/R9J0WtgTZ5kgAGZhJY7bJYfTF+XcfsviQA1L21hiMPzyiwaRbbMgJDwaMZSfH5+L1me40/jv8fjY1aAWRmWDPi9HtZjCmwq3UVgTL3M72u+gpebr4EIaI/jbRsPLICuXoeaKh1S43MwfsKJjGBbQTYyVhRpNjT5PBWU5khGyt72xEaFn8PfVn2OnpH9MXPkCT0EXQpdvS+M9uD2CEUGLnDkppcKw+fEspYc+DS89vVDIqgj2FY3Y+SNCPWPwS+fL8Li/XrzIy4kTOFyUZk6dar8pJCRP5mS1MC2MLoJcnGaNWuW2RZBLkC0pjUGF2n2yTNbQFU3dQJnCqZ5J90Ri6RNWdJSWV5cI6lObnckI3x8Ap+rpwOiBwag29AgXOrYBy+8ABnYQ8LCdrMLCayrMxhxB27sS6EFAr5mZmtIBHgd3wcK7rTuD75fH3z4AcoKq7H0f3tkUBHLTKbB3dvdD9ddcr/hOZmK5Y6xx7AgdB1iWVfAXTrfX5Y0mAHiTp8B1HRqIY+Px8kAw1p8a7qHkwG7LXxsIrH5jzTY+llLGWRU/8l4f86zWJ+45AQZ2P2XdFDcPPURQ3q8T5cReH/BPzB/3vwmZIBgsGWWgESM2TKed83/n69Fa/9l+YtZO61u31bgedGEtSSBPG/8m6bn1RxYeqGR0sdvfIvqg14I6OoPJxcHHM5MFovuPtFDmz2G2ZIte1bK7nvZ5t9waFcBMlOKERLTeuvuqcDZt1FIPVP9/AwSmfnpUv7rGtrL8HngsKwfl30g5PSaCfcaruemiP+OjDv9TUVHhyIDFziqK+tECKU5yFly4NNwy7RHRYHL+yUkb5D0m52dLYL9Q/HvW/8tQZ9B4mSU0jSzYeaAi4ylmQpMaZtaJbNcwAWMO1OWFVjTbgtw9nyfCaGIHR2Co/sKkZVaok+DslTgYif18dCe3k1G79KwiQSH7mfcwbJObQoGiILMclSV6SS7wIl9HH+sLVrnE9pOUPOlYCZG2y1qEwEpLmOQNW5JNQanD066vachs0L2JJkVtqVq2aaGRhlOxJG+NnY26D22E/pOCmt1h6jV8ymkYxcDz7HWIqqBJIHBTfPOaAu3PJaC9q3PlL+l6SGk397VG8dyDxnuV1NbJWU17XWQMHr6eMCqwRa1TSchG6AFY2bDKIykhoOZAH5nqIsh6WIJ7Wy7/vH5T3XwF8ljt8AhOFZSKESA2JmySUhzXExzMrBg/XeyA2fwZRaR5zV5a06bk4FnnvsnSnNq4VgdAC9vL+QVZeL/2/sS+CjLq/uTZLLve0hYkxC2AGHfV9kERRHcQK27tbW2/azda/3672fVql/71dqqoNRdREVUNkF2ISxhhyQQIAsh+75OJsn/d+7whMlkkkxCkIQ858f8kkxmwjsz7/vc89x77rn7Tm4TN8x5483uoYUleXh3/d9E6BEbNUaMrxSqKmoQFRmNCBuvQcM+aDLQxcGUtyq22ePAF9XT7Gk+oM8wDO43An/76LdwqDVgysg5mDlzXJv+bxIBLkbso6Z9q3VgaK6vmmlU7mq4c7SevNYRYLDvNzxYbq2B79GvfvUrIQScO8+sBcfv8rWQSJw/mockNSzFZH6f+TI5JbHv0CAZlPJ9qK3t8aWgex5T8yRmJASqv98essXMyrxHh+LckVwk7ctG4cVyVFeYzKIu2cGbX3P06FD0Hx2K0H4+dr9mfsZsNWW5gs59DJ7c0XIny/edKn+WBzryPbyQXCTKeEfnWpRXlqLKWIFT5w8hKfUohvW/fJ7TVfHYmX1SLhjYN070EHuOfYPqmkrERkxpJJK1BmvzlrqCrgCq7bPOFjcQJNbjj52JR5+waMkAWeLomX1IzTqNn9/9PIpKzSZiJIIZiYUoya/sEOMyBYod1675CvGHv0FVdQW8vXwRGzkaM0ffKkJCorA0V36nuggUeG6yRHB/3wea6IU07IcmA10czu5OspuprKposwMfxWDhQX1xNGUvbpg0v10zFRjYqRxn/ZLpZ1vjYC2hzJMYeKmw7gzgYk8FOVPZzBSwvYyjak1lBvTw74eZIxchPDQCjh7cKZt3ydVVNXj/3Y9w6I87UGLMQ0iEHwYMHID/+q//alIW+b6gdtjtBUfCMu0fMzYM2edLkJdRJmNjHQ0OknWhUVVzIlN7QHJir5XulYIqeX5OG/d9LB4bBLUO3OUunHJ5ONbNk5cJWaBugDfCw90bD970K3jV9ZDgydbD6wWpx/Mlpe7qYb5Ok9OPiY4oLqZx2yHdJ9d/96HoR0gSFBkwuDhKdij1WL4MqOoo3HXXXXJLOZSD3atp0lQPd6/GnhEkbn/58TuNj7O6VnRA/YYFYerd1+a6u16gyUAXBxcqR+d6vP3VK2124CNYJqiuNqJHlG+7AxCV6fShp1Ma06UM9lz4LQWNFIcppzQGS4qqWiMO3yd4XCQD3O2ZypwwJmYeauqqcfD0VqxY//8aeTZQoPjV3v/gUPIexEVNwCj/GfDwd4RLYHnDtL6uDJLLsEhfuXVVsFWOpbNJw+diaNRYmbNwNGWfnIsU0Co4G1wR7B8GXy9/yQzQlXLXkY34aMuruHfm0yjNj7muyEBlibFRtuNI8h44OhkwLLpxVnBbwldi1DV95M2N7lexubLs8nyJjgR9DUhW9n15TrRQzGAwI9VEpFlTh8qyahQVFkkGcPLtnOuiswJXAk0GujjcvAxYf/gtpGWfwf03/dymAx8XP/aKUzBoCQ6kycpLx9B+4yT9214wqFP9TUEgVc5UK9N3QImqCNZTmQ7mzpC6hM44bpT6hSce/CU2LD8iJQH/YB+MGTqpiWfD0TPxSEjchWU3PimpTOoIKHzqHR2IsWPabiyj0fHgjpGkJsQ/XG7EyIGTsWLti1j59Sv48ZJn5fz8YNOrcHRwxA8W/FfDc9nb/tL7T2Prkc9xS3XXKgO0BlPNZSJE4nPy/CHE9IpttDbQTGrHoXW4Zep9TVw7FRiwrxaYmWLZ6sSuTGQmF6K82CilKrFVvyTSZDkgIMwTJ/O2Yc+2o1jw6PssYly1Y+oO0GSgi4M2sKfSDiOm5zCUlJbYdOCrrqnCC+/8HEOjxyE0oCdcnF2RlZ+Og6d2wMXJDbfOp/HOle1+RMkbGSk37o7pQ6Daq5glYPranjnz1xLUOGxcfkJKBJ7B5syGLc+GXYc3oGdopBABcbirN8LV0xkZyYW4mFKE8P4dK67SaJ9upKq+6e6VwrM1295GXlEWnBydkJx6FIumP9DoMQyMfcJikH4xBQbn62u3KVqBS5vsk+cOmrsIrEoEm/d9Bl9Pf0nLkxgQpZeGV7GkUliWByeXy1bnVwMqM1WcW4mUhBwR8LKLhZ+rp78r+g0NQo/+fohLDZB2S5b2WKLTaD80GejiYGqevcDn8o4jMe1wg3mIJRlgKnT04Ok4e+EUjqfsl55hHw8/DO49FjNG3Yw5t3fsIBOa4DQ3Trkzg/VhTnmkkr45zwZmWNJzzmJ87A3i4c/WNKOxCv4+dDS8Ff32B2ky0AngE+QmrZLWMF2axEhBYR3nX1/6jJs+zoS6+lqZoHc9wS/MA2y2ZU2eXQQuLm4Y1LexIVJRaT7yi7Px13efavL8Ndv/I91LM+9a1+L/w/eUQlYlFLVHT2QLvsHuGDnX9lwCglnGJ554QjRINLZiu61G+6DJQBcHRXwqLbrlnVPSA8zWN1r0qoBmcDKIUEqBC0FVqVFax8bfGoXw/h2v6O+KOHMgByZjHVz9XJr1bCgozhH58tHTe8WY58YJd0r3xu6jG/H5rjfh6eGFkfP6SLuexrVDYJQzMpLMRlPKfIrK+YSk3TAYXCTbU1NLDYsjjpyJx9ghMxqul+KyAum97x85CMG9upZVdWtgB0zCxjQUFhTgTPoJcZVkptASc8YtQXlV477K7IIMfBP/KSYOnovI3gMwYJRtXRKzgZzfwDZXzplQRIuEgFlDlgmZJezItksKD2l5TttlGZDl5Irzx/KQcaoAFaU1IiR19TSgR6QvIkeEiEupRlNoMnCdgMH/hh8MwnefnhHFcGlBJQeEw9PbXbIF0n5Ta3bgYyeiu7ezEAEuDhpmZJ0rFtGZCgq2PBtYciGowH58yR/ROzRKfubu6sV3n8LWhLV4MOM2TQauMd774t84vjsVEQFRCAoMljQ3p3ayf33+pKVSC3eFG0YNmoIDJ7dj+RfPIzZqNKqMVdh7bLOo6R988AGpU19PYFdIVFwQVi7fKFqiOBt9+ZZDqxQ434PwdQ2Bh5+znP/Kp4Bg0KcREy2y1fApZgJIAvg7trrSk4GZTM7JmD17doN19JWCxIJdQPcuvR+vPfsxevkMQYUIJQGzkaY5E5J+qlCs2/sMDUTs1J7wC71soKahycB1BZYLpt4VI8Yx//rLe/CsDYPByfmSEx8vDAcEhnsiZlyYtOK4erRuLNSdYKw0i85a8mxwvjSsh2UBRQQIBhdOhDx4chcqK8xDcjSuHditkpHyHvYf3YaqmnIZz8w22nkT7hCBoMKiafcjPKg39p/cjg0c3VwPhPn1kVkWC+5o7D54vYDtoyef3Qd3F29ERQyx6znmgF4v46p3Hf8a6297F48//rgE+HXr1okToyIBlrt+pvBpxET7bOqIzN0cdfj9738v/hNM7f/jH/9o92shAXnsscdE8FuQU4q/n3gOBoO53fpHi59B7zDzePatB9aKWDKvMFuMpgJ8g3DjLbPws1/+uEuWNK8GdGagi4MXId3nlNmMsnyNT/0Kdy5ZitlTYxv1igf08LzudjsdBWZQGA1obNKcZ4P63sujadudp5u3tGMZa8x1aY1rB86auGHmLGz/IAnnj+fDxd1JxvJag6WeCUNny42BijtKiuxuuH+QaEeuR3BH/N6H78h7U1Vmgoe3Y4trAjcTYV5R+J/HVmLmvQPhGfIYXn31Vbz88suyy6fFNeeXMAugnEs3bNggEystJ5XSHIvmQgTXKq5TCxYsuOLXw13/8N5T4RvVAy7uznC65MZKHxWFC3nnhfQN7z8OrgY3ZFxMx6oPP8P+Q/H49PNVbXZyvB5xfZ7t3QB0cSMrpqObGtdKRq5ScvREL68qQXp+MuLi4uyyF+7u4LCUgpwSfPTN35v1bCAZIBEoKWvqJ1BcViiZGL/A66vO3FVBrcCUO2NwIXMrLp4uQ1BgoGTDLG2WVTAxz1uohYe3i2TXwvp1XY8Fe9BrYABm3DMQOz9OFgJEMsD5JpbDvdjLX11hnmro5esqpj7Ke+K3v/2tmIb99a9/Ff8QthUrp8v09HQxF+PYZ0sw+CtXUq5T3MioCaZXAvoRBEb3weihE5p1ILxn3pONfh49uB69gqPw2e7XsW3bdtx447xW/x9mNDiHJSMjQ7QRavojZ6uw5NHVpp9aQ5OBLgSm4TjXfPfu3TLtjz7v7O+ngtb6RKTN63fffSdjXPk8zgfgBXstHfI6O3oN9sf/vvlnpGafwX3zf2bTs4EY1n88vjuyEafTj6N/r9iGlquTZxPQv89ghEfrtGNnQWFxPt7e+BxmjroVQb36oiDTbLOsjHfUVwrMYjjAamIP+IV0j1oy5wss/OkIad3j5NPS/EpIg4U5QWae0xDigZixoYiMC24yi4NTHB9++GGZnMmpmQqcQcH31JoMWI5aVsZkDK7c2NA2uz3g+HKOb+foYgpCnR041rl1vwHz1M5QmEx1OH/qItDCkEySncTERJmvwWPla7AEMyJcX9VsjbaM3u5M6JpH3U3BAT/sp+XJyDqXcvuzxUg3btwoFyUvSPb3sz7HlN314JB3tfDVrg9wJvMoYnoNR2VVmU3PBmLGyJvEz/29Df+HKcNvlHr03uPfilr90Yd+KNoNjWsPBnoOoHJxdcGTz9wPby9vsVlOPZGPqrIa2flyN0wdDV3sWEbrbqCynrbCg6eE4+LpIpn2aaquhcHVSUSwdCZtbrfN3T/XGEu3UWYlWbaMiIhoMneEaw8zCXwMyQOzl7xRd9BeMnAhqVDKGF/segufbOVu3RF9wwdg/sQ70TMkstFjpQzEEe71tcgrysaGPavEcMrL1LQzokGLUFcn6y5fK8HX+YMf/KBhQquauklSw/ksq1atEnLA18npm10Jmgx0ITANxxNRpdhWrlxp83G8uNjas3jxYmGqZOKs0XGYDYfFaNjG2XNnJDgkph5GcsYR5c3ShAywTPDDRb/Huu8+knG41AlQdDZr2p2YMHuYfns7CT777DPs2bMH//d//9dQu+7qNstXC7Ty7TkwoE3PoU6ATqMkAwoMiFxrrLMC3LxwkmNwcLAET+60mbnkTpu6A6UlUOAaxyxO+qkCsT7mQDbaEtMcrdfgy5NHM5NLMLjPKMTGjBSzKHYA7Ty0Dq9//j94/LZnEB582aOgrLIYz719uVzg4xWA2294DCjxkjHe1u3AJAJcP6knYDlACSNpWa5AcsANGW+7du0SS3M+TpGHrgRNBroQaPPLi4SsuqVaG9t7OK6WJzJPaN7IVmkVTDJgefFqNPZsoK3wpuUnxPGMu6bmdkUBviG450bzwsLphtVV1dh9+gs8/tOH8fzzz8t7r3HtwLo13Tlvu+02TJzY2GFPo2PAzKRaWxSYKeDPLCFYwlooSO0AuxASEhKEQPDvMIhSv8FJocn7spCTWtpge1yPejFL4j8vP1f0HxOKqJEhCPXui7vnPNEg9mSnSGzkGLEQ37B3FR68+emG/9Pd1QsPLfyVlBMyc1Nx4uwB1NYZUWuqR0WxsQkZYGCn6JGaq9bs03Nzc3HkyBE517Zv3y7fs6OlK0GTgS4CXiysWREtGXZQTMjOAo6zpRHHgQMHpOZFZe/48ePFEKS50cIa9F9wwYx7B+Lbd06h4GIFnF0dRV1uS20tAqvyGunUGHNjFOb+9L9EWHX//feLNmPJkiVdXlTUGcG0sJqqSOEfW2aZ4o8Y4C8LOq8V9p2zPPazn/3sWh9utwHXGQqa6QpojzqfGxOSAd6YtaRQsbyo6pKmw9zd8/jiP6BPD7N2h2W4LfvX4uCpnSj5RxH8/QIwrO8kTB+5oFEo48hjkgIGe0UylPladK8hDb4g/P5fn/4/uEzzwBxT40xGQUGBrKUUXrOsweu4pXWXg9q4AWD2g49jGyXJhGU3RWeHJgNdBFlZWeLoxXRVS2BdjtkDlgp4Us6YMUOes3//fqxfv15qXZoMtAwOSZn7SCwObjiP88fyL6mt6dOgRhjTrpaLjIPYuw6b0VOmrRFvvfUW/v73v+OFF14QIsZ+au4uNK4cxioTzh0x7xqZQqYtbkMtpx5S5+49KAAp+YeEOL/55psipNW4OmCGkWsMgyVFczQUYnrc3jHVKlDy+cSYmJlwrwkWMaCaCcHZIAofb34dx1L2YfTAqQgP7ouzqUnYcuAzFJfl4855jzT6235egUIeOJXVzcU2MaFA2MvdF/sTd+DjTxwRM7w3Bg4ciL59+0q2guvopk2b8NVXXwkZYNcA11NmXS1BjQQ7DB599FEUFxfLY/k+8P6uVJbVZKCLgLU51v5bY9xk5wTrdtQXUMhD9O/fX8w9eHI/88wz38sxd/UMweTbYzBiTjXOHsrF2cO5Qgro4ujsYUBwb29JVdLK2exPYAZrh08//bR0ePzpT3+SISosG1inTTXsA0ktF+O938Uj8dhZOMMNEUFRmDdxCUKDwhvNkNiZsAl/X7UZuYUXxXlz8+YtDTVfjY5HaGiokC12K5EM8LPi+c+1xh6ojQvFg5lp2fCuD0dc7HibAtyMnLMi2p05+hbMHrdY7hs/ZCZcNnpgX+IWTBk1p5E+IL8kGwaDM1wMLZdETaYaVJsq8N2B7Xj/szNyH88Xnjd8HezA4mtkGYAdXDRPuu+++6R7gGDQ//bbbyWzQcEkyQBBkkTd1pgxYzrUevlqQpOBLgISAXug2lp4YioiQPAijYqKEuEOmXhnHCHcGeHp6ypqa97EZ/1Sy1VrmDlzpuwyfv3rX+PBBx/Ek08+ibvvvluXDdoIimQTDh5CuNtgzBw6Csb6csSf3IJ/ff4sHl/8R4QFmlXdVIbvOPS1TCUcP+QG5BRcxFuvv4Nz585KB45Gx4NrCrMAFGlys0JNE0mvtaeJGlZk2XIn5G3nTlmLEk8morSoEo4ODqhDDdi5Z90eeC4zuaGtV4HX4cB+wxCftBmHTu5B+DQzGcjMS8Wp84cxoPdQCcRGsRB3kBkMJPOc48Ls3olzB1BRWY4xN4zGyyv+KK9BzVU4f/68tAoqfRWJAa/n5cuXS/mV8xAIvna+BmtdCl8vW7+pq+gq2SlNBroIVAuh6otuDiolbdn3q0DGq6aJKYMQDfsh73sbJABMJ65YsUIyMpyqxrIBa9ldqY54rbF06TJM7nUn8tIq4OHjKvqAuAHjRSC2PeEr3Dn7hygpL5KuDnZ70EZYlRQCj4Rh25ZPpQ9+6tSp1/qlXJdg8GcbHuv+rM/bKhGwxPnFF1/IY9lVwI0NSwqpqamYPHky+oXE4qNV72HtnpVYvePfNtsDTbVmdb6zobHXwbeHP0NhWQ7iT25FcFCodBPsO7kNLgYXzBt/hzwmtygby9c8j0G9RsPPPQT19Q7ILkzF8bR4+LgHINpnPLZ/mIT+o0MxIm6EXLcffPBBkw0Tuwjo0cJj52ulJmDv3r3iwGg9kVEZwDFTq8mARoeCJyIZN9NSLXUDkAww0NvqNuB91A90lZPzegA/M4oJR48eLURg6dKleO6552TX0dLUN9YgSdq4qPDz4sQ3CrO+L0MTLmSZmZmS+uVirsRUFKJyh8Qs0/fhahns3huHM0+KJz6JgKojc+ogF34iLeu0DN2x3DVS9Dm031hsOvghNqzfoMnAVVyXWBJj+pybDdbbrcFzhpMKeV5z901Szfuotuc18eWb8RjSbwyGDhjVbHtg8CXtQOrFZAT4BDf8bT+fQNTX1+FC3jl8vnUlvL18ERs5GtE9Y/G/H/5GHlNTY0JJRRHOZSaJx0CQdw8E+0VgVPQMXCw8ixc/+hnqP+QmC3BxN8DN2wAfH2/ceeedTV4LiTwzAVyHSTK53lI0SC8CgtkA9ZVkoSsZEHWdI+3m4EVHAUtKSkqrrYGsc1EwyLQdAwjBE5OimClTpnSZGtb1BO5MudtgtwFd2zjA5Z577mn4LFhrPHTokARf1mAtM0D8nm2hXEAp/mSr09VsD6UinOcP+8i5i+Mxqp0Oa6dnzpyRRZHHwpro1Vzwkg9kS4+56isneBxllSUI9TeXwUx15hKas1NjcuLp7SFtYwn7zF04GlcHTJH/+c9/lnOUZNayJ1+VLBctWmT+rC55nvD8pXsqBc2Brhdx96wnhPA11x44sE8c/LyDxNuD2YGI4H5Iz0lB2sXTCAvqBS9nPzxx63Nw9XSWEe7nMhPlbzHgB3v1koyeXE4ODogMi4WbwRPOLo74aPf/wq82APPG3y521DXGOgREeKLUcF6O1ZrwUufA8533c03lz6+99prN7gK+BywPdhVoMtCFwMWXRipMUakJYVy4VRaADJ0XIi9OKlk//fTTBl8Beg/w5PzVr351jV9F9wVFR/Qy4OJBIxyKOVmLZJqVJIALDFOU1BvQiEWBmQQGQDUbno/jAsvPk8IlW4tRe0R6LGMoRTjFYTSC4VfLshQfSyEVO1sIkk3qIq6G21pZYTUyEgulS8ASh5O/Q0lZAWaPvU1+DvYzj8JNzTqNqJ6XhZrpuafl64X0ix1+bBqXwfOQI4lJEHkuq92x6jZQ81KYMufP3Njw8cwWECYjp4U2fket2wMpBrz/pqfwwcZX8f4G85RD3jdvwp0ykdDN2R29BgUgM6UQxQUlUibilMUw3ygM7j2yocSq7JY58p1tqPQuYLfBiEuGYiQE1eUm1HiHobD6tKyn6vwnOSYRZpaOr4MTF9U6rMDrgj4DbO2eNWtWlyrHajLQhcAUHE096Nql6llcvHkj6PrFk5d6gXvvvVdUriQBZLgMRFS3M2ugce3AXQV3CywbUFDIHQQDOmun/JxYe6Vojp0gdGsjbr755obnc2Hk7otpexI+ekdcKfj/0SSF5wbPHe7c2JrH1K/lcZAskMBER0eLjSyPgc+jbeu//vUvWeA7EiV5Fag11jbsGAmmkL/Y8Q56h/XHyAGT5b6I4L7oFRqF7Ye+lkFSkRGDpKNgzfaVMDg5obKCAjKNqwkGxwkTJkj5iyJlivCYOlfCZ65XJL7UFJBAWtbjmfUxGRv7/dtqD+TQsJ/d9ZycA5VV5QgJCJcswde7PkDPwGgMmhSGo5nbcOFcKfy9Qs2j2+vrUW2sgsGRZSYncTF0u5Q9MKcKzKirq0WNqUZGkde61OGND16HyaMI/QdECcnJz8+XzB2vX7YXEorMWILrL0kHMx7sJNJlAo2rAp5YW7duxSeffCLpKS7czXUFUKhD9zUGDrJyMlVry0+Nawdmb95++22ZIcFaK8maGu5CxTJVygsXLpTHWvtC8POkIIv1145wOWO5guY8PK9INqhRYLC3Pg6SAU5nu/322xt2Szw2qvX5WjqaDHAATZ1FuaS0ogj/+eoVuLl4YNm8JxqlopfN+wk+3PRPfPrtcvmZIrQpcTfidNpJ5JdkiYq8OTdJjY4D1yRmKEeMGCEZS9VJwHO8Oa2ST4g7yk+bJ69awlZ7IM8Fy0miialH5HoI8OyBn/zX4zh4fA9uueUWhPXuK7F+4+H3YDxgnlnQL3wAFky+Cz7ujWcWcELpM288IsSDVuOjB03DwF4jkZi3RzZT/Ps8drYbkrRb2hFbQzm+8jpp6XGdEToz0MXAujGd7dauXSsZAsVGSRTUosmTkRch071MKXP3SNau3fA6Fyg8Yo+1ZSqSCwhLBCoNbwvceVD/oZ57peBuju1STOOqbhRbx8FzivdbnkdMg/Ico+aBu8C27IR4ntLpjY6ZljcKFylaNBW7YGKv2+Hh0wPV1RV4+8uXUGksx2OLfi8ZAEv4egXgh7f9AXlFWSitKJY0s7eHHw6e+okIDu1pB9XoOJCoca2yByH9XXDxDBoRNuv2QFuoMRnxTfyn8HLzxQ0zZuPLQ6/Lmrd3bzyMZwMR0ysOcYPGtjizgLbizCSFBfaUv0dTo20H12Jg79H46W3Poyz0BAqLClrceCmQNND1kmU9rtFdDZoMdEGwXkylK8sDTMdx4WQ9i+kstVCzXse0L3ea9BvQRKDzgZ+duR7auP+aNVdLzYA1KCJl4Ga6viNczpg9ohbAklDaOg6SF/5/zBDw/2bwZ42YX9lhQMGqpeEMXxsJq3WwVzeet8okSxELaiaYJaEwMdSvF4zJvjL34Z2N/ys7uIcW/rLRztAaDPzKtS674AJKy4swceQMff53Yvz7/ZeRfqII4X6RktG01R5IUC/g7emPEP9wVBsrcSBxBwqKc3Hn9CcwZtZAvLUpF3/84x9x1+3L8NlfD2Gqyzy4ejQvSiSWzHy40bGMGDAJn219C/tObENK6mnce/d8bN21SYSzJCUk39akgOc/ryFeM1xrb7rppi7ZsaXJQBcFgz13dKzBbd68Gf/+979FpU5mypQcRS5dSbzS3UAlMvusrbsCqMhmerWlvng+hoGbPc9q+NSVgAYrFEJZelPYOg6WAfg46gZ4I7josV2S59rnn38uv1c7exJUZTWrSCzTp7yxq4VfVfDn99a2zVxc1/37KP5vxXNIyz6De+f/TCxk7QGJCJXnBoMLlt7XtEVMo/Ng5g0z8H7qanx3YhNqaqvg5eEj7YEzR98qGR6FiJB+Mpdg34lvRSvQJywGC8c9hEGDBmDnoQ3ymVOHY6h3hYuzCxwNjTMKzc0ssMaUuBvNZODCSXi6LZKyGDU0JO/MgCkhrwL/Ds9tlsxYXuuqg+A0Geji4InIRZvztOfMmdOlBCvdGfzMGCgt0/xMyTPItjQ/gql6ZgbY588ATGETRYgNammLhUr9bHm/rfv4/6oWwpaOg+UAkk0GbWYAeCxsQWSHy/z58+Vnvi4Gdj7POti31RaYr2nX6TVIyjiCwZEjUFlVhkNJuxs9RqnAv9z5nhjT9AjqLSOljyTvQVpWCpbMeARjZzYeQqPRuUA3v9tvvwM7PkzC+aP50utPtb81po1YIDeC0w0rSqrh6eOKsYv64J6HfiEj25lZkFkiTHA1jtl2zyzw9TLX+quM5XAymH0+WGql6JfXHidiMhPA85PnNFu+rUWRXRE6clwH4A6T6SlNBLoOGHwtAzfFgKtWrZJdBXuym9u1UKnN57L8QzLB9OUf/vAHu+2qbYHpf2aYuJC2dBzc+XMBvOOOy6lbZieYlWIXxAMPPCBixI5EbnGGLMgnzx5CUurhJr9XZCA8qA92H90obYeMBBFB/bB44uOYNW+yqMc1OjecnBwx+Y4YOBrO4NzhPJlGSVJgcHFsVLqqMZrkdxxS5R3ohhvuG4yvNq+W4KzOPY4zZocCbYetSYU9MwsKSnLl//Ly8G7UycL1lSLC63U8uSYD1wHIVMlONbqmvTQXso8//lh21lzQWppyyNZSBmpVs+fufcOGDbIr4d9TNwXL+9T91vexPZClJrYUNncc7F7hrohGMZbgzojHwMxUR4gZrbF8xZvITSvFN2+flHHRHj4uNsWAowZNkZsaLV1ZakRafiKeX/FLePR7VlTgGp0bHFA05Y4Y9Bzgj+T4bOSml5oDf12dnOuKQNcbalDnXYhylxJ8+10q1q1bJ3V6CmoJJ2dHBEY548JRI+rrL+tgrEWJVcZKGBwNQg4U+Pe3HlwrI5QnT+1eRFKTgS6KouwKlBVWSX9uSaYJQ0Zcn2z1egWV1ky7MwCvWbNGVPWsd6qeflvgrp1ZIGpFuEvhc5mCZ73ySqAWUWYEmjsOZSRjXS8lmKHgTU1y62hwQuSMewZix0dJKC+qlhG33DVaTouU4zDVobqiRhZyBpRbb7kdpc+fws9//nM8/vjjkrnQQtrODX6mHAceGReMnLRi7Pg6AVkXclDrVAcHz3o4eZrg6FMBR4c6Oeeod2HJil0u3BSp3v8PNv4TmYml6NujP3y8fW2KEjNzz+PDTa9heP/xCPQNlTIT9QTnM09j9IBpmHXLBHQnaDLQhVBbU4e0k/lI3p+N7HMlsvjRUWuIzxx45Xpg04oTiBkbKk5cTK1qdF6wDsnd/V/+8hfZVbMViTvslkDFP4MxSwTcLRHNaQvaAgZxGlRxHgK7VGwdB0sIDKTsJmAPuQqqFEJyEaY24GqOae4R5Yt5j8bi5K5MnDuSh8oS7vrqzVkC8Zapl9kFPoHucg0MmNBDdpovvfQS3nzzTXFppFsnx3c3p12gVoKfBTMjBB/HEooeLPX9gm2zNLGikx+7UZhFI2Fl3b5/bwpIubY5CiGmqI/dLSSxHAh26623Stls7vxZWHHmQ+w8vB419UZ4uXs3ESXS3pjeAyfPJYiHBb0I6GZ545hlmDNjvpxz3QmaDHQRFFwsl8layUlnsP3wF8gqTEN5dYmoatlqM3HIXDgnueJCciH8wzwwfelA+IV2vfaW7gQa+rAdj6SAu3wubJawDvQsETB1z5KQGo3aEfXLv/3tb0hLSxOHS2YAbB0HOw2olD58+LDMWOD/y7ZAthbyK41errbJim+wByYsisaI2X1w9kiulA+qymuE+Lp7OaPX4ABEDPCX+rMC08F0SOTukW1nDz30kBAEkheCu0uWP/iaSWqU9kKVcNTob2oquOvUcz2uPtiNwqFGfL95zvFzoFaGplgsU5GMEiSwnBrItla2ovJ5PDfZtkoTrAWzb8XG5SdQml8JD1/XJpkkDjxaOveJhp/rL4kS3X1cMeWOAd3Om0KTgS6AvIwybFl5EuUl1aisLUKdYw1GD54CH08/Mco4fvYAPtz8D9w6/QFJbxVkVmDT8hOY9cBgBIQ3HWWs0TnARYvBhjtW7oasYUkG2DXAx9O6WKXlWS7oiDo9/Sr4dxgMeRzWqmh1HHQ75A6NGgOaFBH0IeD9tia8XS1Q1DV4Ujhg1g7aBc574C6fEySZhSEB43tKIiAOdgEB0qLJ7IalYI0BhjoKvvfMELA2/dRTT0mmROPqgOSLbqk8D1WbHl0NGeDpCEgywJIZvydJUw6ZdFllyWznzp0NY6tn3jcQ295LRHFuZbPlJZIAY5VJBIde/m6YtnRAt1w3HeptFQE1Og3Ki6ux/t/HUJJfCU9fV5s1T6aMX/3kGdTU1uCppS/IyV1ebIRfiDvmPTZURFcanRPcia9evVrSodzpt9YRwl04swLcmS9YsKBD25mYauVwK3rKt+a4pkyJ+BgOZLE1x74zgmWN+++/X7IyFBXyPec1xQE0JEOWO08+9q233pKAxEmR9FAgcSJheOedd76XEc7dEWyVZQaKWTDL9Y6aFpKyn/70p5IpYEsrO1tI7CyFrrTHpjfAK6+80jDw6ujWdCkvUZBIODhxRBFdD+ulA9HVzQl9hgZi6PRe8A1uWwvs9QKdGejkSIrPQkl+FTx8bBOBButPrwBk5JyTn5ne8vB1ETZ8en82ht/QdKCGRucAgy5nSHBqIMVQJHYUF1q7AZIEKJ937tQ5JbCj+5q5O+au+csvv2ywumYgZNBTx6Lc1nicrKnTiKgrtVpxd8908wsvvNAgwOR7zayA5c6T+O677+R9p/CQnwm/5+PpwcD3iJ+bRsdCES6ec7QW5vnG857ZM2ZxlC5FmVlZEzJeT7xeWL5S8PJ3xcTbojFyjrm8lHGqAJVlZqEps0w9Y/wQOSKk22+aNBnoxKgx1uLMAU4odGiS2jLWVMmUrSpjBU6dZw/2UQzrf9mJjo8nKaDYMHZqhLTbaHROcAdE4RNr2zQv4SLIgGsJLnqse5MI8DFXq3bN9De7CbhTpusa0+TKYEVEepc853kctLvuiuI6lgeYEaBpk8rE8HXxc+DOU4FBie+58thnSYfvPUsqHDClyUDHg+8/zzcGdb7HnBRI8Pwj6aSxGkHzK4KiV5Z/FPgzH0sHzObKS1Ji0mgCTQY6MdJO5EuZwNL4QuHr3R9i34mt8j1VsEMiR2HhlHsbPcbVwyDth+mnCtB3WPNe9xrfHwoyy5GbxnnrnOHuIJ9RRIw/Nm/7RroFqIRmoGIQ5o5I+aFT3Pd9TUEj8WCgpzCLGQIGT+6KGTi5SFPY1ZUNrijEJOFi1oM7UVs7T5YIWAahc6Il+Fmw7VIFKY2OBT8L6RJxcBBRIM9B6gPYxcL7VRcNMzQUgbLcQ1JHQsDzlJ4bvGZUR4iG/ei6V3Q3EQ4yl2WpjlaYNHwuhkaNRUl5IY6m7Lt0oVz2gSekvbDe/Hc0Gbh2YAsoiV1DS2hNHaRgSdQDLu5OOHL2NOZNXST1Ty6EdJS81pBxsaGhcrtewCDBwEIyQzV6cztPBiDCcl4DwUDD+5g54d/qqj70XQEUp6pBWcxEffjhh6IboOaDnxczM/To+Prrr+UxvI/lHopgSeY02gZNBq4S1HhWxXS5o2AKti113pqqWqlr2QLbCXkjRg6cjBVrX8TKr1/Bj5c820hbQHkMlbIa3y+443z33Xdx+NAR7Nl2EEVFxbhl4gMYEzsVrh5m90HiN/+8T3zWazm+1dERn375kQhFCXYOsD9eo+PAQTO8Jpnyb2nnqVoMbV2vfC5r1swcaDLQseA6yeuAn4P1e8/Pav369bKuskzAEtV9990nP/MzZOaMWQJ2u7Rk3qVhG5oMdDAoSqLSlX3LTFupZg0u/qw9sh2MJ7U9EwWl7m9nq2ts1Bis2fa2zHMP9m+c2qQZi8b3Cyry33j9DThWe8LPNQwljiViberi1viSu2PWY/KV5wltdJk1qPHMx94Tm8VkRaNjwXIHAw2vx5Z2nqoMYjl1UUERBu1m2PFgRoxlMuWjYQlF0KxLACQBqoTGshaJAVtANdoGTQY6CFzMSQDY40pCwIWCuwYl9OICwraXrVu3Sp1rwoQJ0jvb0oJi1gqYhVutLTwmk3kuPAWFlsfEBpru5K/dWcCdy2/v/z8Up9cjvzID//78v20+Tg3aUWAW59OtK1BVZpL+fY2OherSsNVRbbnzVGRd2TBbgsGGO9iuOLO+s4NrJjUpKlOj1j2SMq6v/PwUgbMG11jO2GDm5tFHH/2ej7zrQ5OBDkJ8fLy0IvEEZr2K8+DpI8+0JFuwyHinTZsmphhkvbTa5KLC+2wFei5Ix87tRXGJJwyu/nB1N3sFlFUUw8ujsU0mR3ImJO2W2e0h/pdrzTTRcHCC2BNrfL8ozqpGWZZZxOlUY39mxtFQj8S0BESERsLfz6yY1ug4KP8E7jKtRZCWO0+SOQZ7y+4CBYo7KSzUPgNXByRk9BngmsnPgYSM6ykzrWypZbAn2OJJkkAjLBIBCkPZTUCXSU7T1GgbNBnoAPBE5W6fWQAGfrbE0OOcOw2eqDyZ6Z9NA5Mf/OAHUs8iIWAvLNuZmCEgeELTXpOiGJIF/r0lY36GmmpfuF7ywfh8+0pUGyvRt8cA+Hr5o7SiGIeT9yC3MBPzJy2Fq8tlR7rSonKkZB1D0j++xgMPPtCmC4SkJud8Kc4kZKPgQrnsWJ2cneAd4IbIuCCxfuWYUA3bOH0gR4SDbc3KJKYdQbWpEoN6jhbRIYe2aHQceL0xpZydnd3IvdHWzpPXL9srSe5VCyXto0nUaXajrYmvDmhtTRJGcSeJGUkZRax0kWSrpwI7Cvbv3y8kgOC6eu+994opkUbbocnAFYItSswIMJCr1CIVrdw5WApg2Kq1fPlyIQ20z+RCRJLA4M/FiQRi7dq1UvOiovxnP/sZ5s+fj9wz1dj1SXLDbO5h0eOw/+R2xJ/4FhVVZRL8w4P6Yt6EOzC438jLx1VdC3d3N4ycGYWP1m3F0qVLMWnSJDz44IPi990Szh/Nw/EdF5CfWYZaU/0lzwKz8r3wYrkMS6KRR8zYMLOHgR6K1AhVZTXyHpIstbWufDj5OzgbnDGo92gk78vWZOAqgESbbZxsR2Omrrmd58SJEyVd/f7774vYkHoDXr8k+A8//PDVODQNQDo61DhttnqSDHC9tBYUUn9FrYfy5SBh49qqraLbB00GrhBsMeLOwbJ+aGvqGwM+dxycjKbALEJubq6MWOVugzVims+QOKgg4h1XhwtJhUg5nCtiQo7b5K01syLabkaNCsHk2yfhzkcWSEqNDmtModHDm65qFKg16jyor8fRbzNweEsa6kx1cPFwhpunY8Njth5Yi03xqxHsH4Ef3vzfSNiQitzUUky9K0Y8vzXMoPMjPwPXNr4nnK+elHoEA3oPh7eXlxAvWkt3t4EpVxuLFy+WHT4zc8pvwNbOk8HlnnvukTo0tT7MBHA3ypa2qzWuWQPYsmUL/ud//kcyBHPnzpWsqmr1ZGmGnwPXKmZzVLmHw7toi63MiDTajm69gpN90myEdX0uCjzRqPjngtDcmFNbokGitZZB5eVuKX7hSc1dCAdq/PCHP7QpSHJ0csTEJdEwsVf9eL7s+FmH5v3WqKvlPHeTtKrRV2DioqhLzoVO4rnOC4sCR5YrfvKTn0galJkCDgXhsZzYmYlD36RJ94GnX+MBOMVlBdia8CVcnGmLDHh4u8BkrJUswc5VyZi+bKDOEFxCTbWpXUH8eMp+mEw1iIuZIASMvun83DmKV6PjwGuBpTnOYeA1SR1Bcyl/pp7pyKhaCUn8aWfM55BY61JBx4KZl9/97ncS2H/zm9/I+8sMANdplmy4eVLdHCRx/B1HepPM6e6OK0O3JANMxTOIswWQhMAaDJgMlExBMSXYHFjPou2lPUIipiFLS0sl8FuCiwoXGqYgm1MnMxhMXzoAx7ZlyKyCimKjmNYwPc/ATGG0MrLhUKKB43sgdnpEE7MiXlgULPIYWGtjpuCXv/yluNstve0BlJ30lwBmq8697ruP0Ds0Si7E8qpSuY9TwEgZ0k4WIHHPRQyZcu2NcjoDqK2QhUkU6/YTAmo/3Fw9MLBPHGr5ETteMo7S6HDwumZKmTMhGOB5DVt2/yjw2uR1zh0oAw4JAHehb7zxhpQa/vSnP4kOoaVNAAk6STxJNrNFvG66C6ibyUgqxJmDOSjKqpANhJOLI3yD3BE9KqSR9ogagV/84hfSacX3VX0W3DAx4LNkwE0b10puvnh/R8/n6M7odmSAFzDTUGrYCg0qKFbhjcSAdXoK7RISEuSxrCEqi1JrcJGwZY5hDZYGmKZn+cB6Rj1PeDWMoyUwKMTN6i0BN/V4Pk4fyEZJXqVcXFxcOGmr/5hQ9B4S2OpOkoGKhja8kRSRFKz/aDfiek9HUA+/Jo8/eyERx87sw5N3/j+s3fFuo9/x/2ZJgiRl0MQeNjMW3Q0kZI4GB/ENcLHz/SgpL8LZC6fEQMpgcIaxwgifIBqw6BLB1QLV6hQCMg3NOQTc+av5C4TyIyBhZzAaPXq0EPZHHnlE1oTf//73Ynrz17/+tdHkPKK6ogbnjuaJ7oNlI8kUXSLwfWIDET0qFCF9G0/luxqoKq+RaX0ZSQWiZSFI9sP7czhPMNy9rs5EU77eU3su4tTuiyjNrxRezGtC2jor6lGaVyUkwcvPFQPG94AhpFR0Ulwfn3/+eZt213wuCYDSdGh0LLoVGaDqlOMxyfbJ5lke2L17t5QGuFNgKyAXAl7wKq3PIM7vuRjYOjlbu5hZ66KRCXcdixYtajataG+6kSJCMmreCHs8CFoCL76//PkFfPTcXtTXNjVS4YL45a73MGbwNIQF2p5+6OrhjJK8KlxILtJtjKw1B7khtI8PMk8XNTEZag5Hz+xFfX0d4mImykLK950BQ+PqgmIzTl6kWJCZQo4xpnukWgciIyNlc2AdgCjGpcPk008/LSZFnCnBMhw/u2PbL+DEzgvm4OtgvmYdOSisHkIQSRDOJOQgKMILExdHwz+sseVxR6C8qBrHtmfg7OFcVJdfGtt7iViyjJiRWIjDm9PRb3gQhk7vKV1CVwraAL/++uuyiUo9nQlTJRDsF45poxYgNtrcMUVwpgqzYDmFmaioLIfXCl94uHui18AeMnZYuzpeG3QbMsBWInqRkwiQ6TPoUf3/5JNPylf2Dq9cubLh8er3DOZ8HoUptkRD3NEzxWiLrTL7QEUsH0Mhkq10Io9HDaNpDzpiZ8GFoa7GAe7eTV9D/IktKCrNw0MLf9ns8zkRsa68HueP5WkycOkziRkbijVffYZaVKOsqljep1Oph1BcXiDfTxw6W0oCClwcfTz9ERk+UNo4SSKiRmhL1e8LXBOoI1BtvvaAmT5m1UgI2DLMNcKx1hW1lQ4isp0+egGGRF7u8EnPTsHBxF3y9WJeGkymWrxQ+A6m3zMQoX19OiwYHz10HOeSM+BQb0BoQIQEY3UcJJmHknbj+NkDyMg+h4oV5QgKCMWd996KH/30kSvadTO7yg3UkF7j0cfggHqHGiSmH8L7G/+OW6sfwLghM+RxmXlp8PcJwqB+I+Du6onsvEzEH9+KqpMlKC0t02ZO1wjdhgywHMDgzICsAihTUa3ZAnORYK2f4hWSAZKGXbt2yY3pRQ43iYqKsmmdSaERuwQoQGrOK5v1L9Yg7bEnvlrgbG/COiXN1sVv9n2GmaNvgZd7y4sV31LRMmgIWAvdf/ob5BXkyghq4kTKAbkRzAAoMpBbeBEXcs5hctw8SaeyvsySj6efHoLT2UESf+edd4rArfRiPQb1Hiu6AAbB99b/DbdOvxwEOWZ8/6nt6BHYCwG+IcgvykJFSTW2v5+IOY/Ewi/kyhwNGYyLCkrQ22s4ouImwsGlDifPHWx0HDUmI1Z/+yZ6hUVj4rBZ8HDzxtm0JLz699dw+FgC3v7P8nZvMJgt6e0/CN++ewpOgY7SYTRl5Dy8+skz2HVkQ8P7cOu0HzR57pB+Y/Cvz5/Fyn99hF89++QVvQ8a7cN1RQbE391YJ19ZN1dpMbLV5ORk2cG39UTn4/k8JbjjRU+NAHcQTzzxhBABEgNLRzOy788//1yMh5YsWWKz1dDS95yp+muphKXIxxY2xq+Gh6sXJgyd3erf4PGbapr6uLcFfN+YwaF2g58h04UkUV0xbUhR1PoN6/DN2yckTevhe3k4kTU4S+IvP35HPofKEiOCenphzIJ+3/sxa7QPkydPxgdvf4o1/3tABoN5+3naDILjY2di2sgFcDa44Isd7wgZ8PBxRVlRNQ5tSsOMewZe0UfAUkfpcR9kphSbdSuODpg0bE6j43ByMuCHi/+APmGXWyjHDp4On52B2L3ta+yL34dx48e1+xiSD2TL0C2VZWTW09crABk551p8XkhQqBDhcyczr7j0qdFNyQBrdDmppSKoSz9Z0BDYqNyNGOCP/qNDkVVyVrIC1uNI7QWDEXf4zAJQPERTIfW3GMBoTEKRIHf3PIkpUCRpYIsiA5tqP1Rg8OcJz9oke5mtxUffN1jTZD3T8iLkwKP9J7bhpsnLZEyygqm2BrV1tSgoyYWbizs83LwaPgdqB9oDkjXWa5l94fwG1TqkyifUa/DWnCd5Z0Vwb29pudzxUbLUcPk+M/1v3XLY0BJaW4/gPj6YsWygnifRxXDmYDYc4ARP3+aDoLWNOMFzwdnNIGK60oKqK6rd518ok7WQmQmV5bM+DoOToRERUMcwfMAY7Di6Fof3nWw3GSjOrUBmciHgVIvyylKZk3Lq/CHJiAzr3/RvMvPIsetFZfn49sAXcswh7pHISy+Ta0fj+0WXJgPZ50uw76tzKLjklGdwdmy4CCjUSTmUI05wlT6pqHUwj4htDrYGlyjweQzaNOuhMYn173jfF1980dCzzN0tQULAmzViY2OFCDDjwOdea4/zgB6eQp7YnqjankgAKGj7cue7crPGX999ChOHz8XNk5fJe0fSk1WUirKytpU82HK5bds2MW9i37e1KJPE4Pbbb5e2I75v9ESwpTTurAiP9sO8R2JFzEWfiPKSalRWVMLD093sJcCO0EstoSSuQ6aEt5tUaVwbsJsmJSEXpvoaVFQZWw2C1nBxc0J5kREpCTnSMdRenDmQI74UDi4mlFea2nQcFcZS2ZmXZdnOEtqDtBMFkpnddGgV9p/cKvc5ODhiSOQoLJxyb5PH/+U/PxVfDYKbipun3oN+oYOReiJfk4FrgK6zqlqBvu07V52WFh5XT2cYXGuxef9nOJS8G5XV5aJ8nzNuCfqFDUJeWRWMBhOqXU1i2GMJBmXu6qkibgkMeOxxtQXW/KkkpqUw9QWsIarpaNZgOYGPoVCHROBaZwUIipf8e3jKzkKRgdCAnrjnxqYe33QgNNZU4abJ9yDQ19zRYKyqhbG2Gss/+Tv++fGfJG3K92PKlCktpvipuaCnAwM+TZ5IrNi+RStnS7DMwveSw0tIuBYsWNAuQsCd18UzRbJ4c+FjTTMs0lfaMq8W+HnnllxAmXcKnGLKUZNeh/zCErj590JwaDC8/TzQe3AAese23hKq0TmRnVoiLXxbGARPbWs1CFqD57ajk9mvo71kgCW6s0doge2Idd99IIr9thzHjkPr4ObqDt+6vqiuNLXqnqnWQ2qeKJDm7WJGjpRJJsfNxbDosbKhOJqyTx7LDIA17l/wFEy1Jukq4LpNPQMfW1WmtUfXAl2SDGSdKxYiQNU1RVa8mD7c9Lo4uE0aPheBvqFISNyFlV+9jEdu+Q3cQ1xQXmtut2HmwJlMvLxcSADJAFPRNBSxRzvQHNh+xIBG21L+Xe6UlXUmwYDHwMCfWQen8Q/bljoDmCYcMDYU331WJilregV4unvLImKN3Uc2ylf1O1kUqkwYPjEaS599X1o3SYp+/etfi6iSO3kSA5ZWLAM4+7pJBAhmEugLr4gVDZ9sgVoNZlroLU//B3vA8kXmmSKc3p+N9MRCmKrZP3npd/U0dHJEeIw/YkaHImKgf4f19XORJHlh6YMtrKpnHc5Afc9yFHgUoqLeDf2D+sOnlyYCXRlCLuvqMYlBsP+4VoOgLTCInk9Jx4svbpLzhOe6kARHxyY/W97U7+prnFBZFAovb09ZA4dGtR6MFWgznpJxAjdNvAeVZUY889v/RnlNYUOQVzee0yy3qu+ts6kjwudgWM+pCO0ZjhD/cLmPvhkr1r6IlV+/gh8vebbRBimqp9m/ZUCfYRjcbwT+9tFvUV/tiMgRd7bzk9DoVmSAF138mrMwVtbAw9dMBNiqc/T0Xtw48S5MHTFfHjdqwGT870e/wfo9H+POW++TVCwDXUlBBUqN+RKsGbw5O1u1Fjb7f1466dXksubAv8UWQo7RZPqbrT4MAnw+L1gGf+oFOCCls9mY9hsejFPfXURBVjk8fVztstKV8kCpUXbYTG+HhPhg2bJlcktLSxOPBhKDdevWiZcDAzjnL9BClMOdKKC0VVLgQkPiYP0ekVzxOXxv4+LiWvUhpyHTnjUpOHsoVz57g6sBhcZMfHtgDc5fTEaNqQZ+nkGIi5qCCSdno09sACYt6W+3N0BzIMH8+uuvxbeC7wH1ELayRDw3aAfN84QWuf36adFgV0T9pcw6AyBb+VoLgrbAc4HloyOHD8s5bg7gdU1uvF/58ls+xt3gi+m974Onl4cchz3BmDhyei827fsUowdNw7ghM5GbU4D62nopd3KeCjN7vDGTyU2T+l7db3nLPVmH7JNNs6exUWOwZtvbokOiWNYWuIHjwLVj5+Nxn/s97f0oNLoTGbiYUoTCnAopDagT+3jKAUmHjb2k2iXo4sYTfNPeT1BX4QxHVwPqnWpRa3SAwcEVvXsH2y0oJBPmBWDPCGAGMO5ueeNFqpwFO7t1JgM6+52/WXFC0ulUA9uywn100W8bSBmJAD0GJiyKQkifxkSJr5+T3Wjfyt08SQFvn332mXRgkBBwYpz14kR7WJIB3s+/MWPGDISHmxc2gosOyyw0NmEZojlQSLr9o2Rxa2RNlq8nOe0Y3ln3vwgP6iPtki4GV+SX5MBYXybivvNH81FdWYsbfjCo3Sl7ft6cPsmyEzMjzIjYOm82bNggBIklEXpZkDxwSFVznScanRdMqZM8kxQ4OLUtCCo4ORowcEh//OpnH7TrGLijX/38AZuko7njOJ1+HJ9seQMD+wzHoun3o9ZkNmF68Knft0vIeN49DzmnEuXas1w7TCZz2p8ahpZgrDFnHAIjOt6ESeM6JAOn9+eYd3nOl80xLuSeR5B/mKjbLdErxJyGz8nLgn+vYJTUXYSTkwvc3fzg6eneULfmAs4AI3+f/cKXvmf7IIMPT1Cq2VvLDNgiBvYMPOosYJ/znIeHYOt7iSjMqpAOA5IEg4s5HSk7kVqz1zpdzNw9nTHhtij0HRrUsgFPTIzcfvzjH0tnBYMliZJl2YBEid0a1FAwiHIgSXx8PN577z2xfFWGT/x7fB6zAwy0zZmkJGxMFcGem4fZC54TAc0LXxyWzn3CZmaGCzpdA+PXnsXkJY0V1/aCGQ9FBHicDO7WAZ6/Z72V55SluRUzKDSv6YqtlN0ZwX28RYvEsqXlXA97g6C6rnoNCmj3Mbh5OEvJtCS3sklmy9ZxpGWn4L31f0dEcL9L14MTqqqN0g1BMWt74BkKIRFlhdUNf6O21oSEpN0wGFwQ4h8h5Qpei6oLScFsxJSOETETWlxPNK4euhQZ4FjY9MSCJru20ooi+Hg09dT39jTfV1pRjH6OI1BWl4N6RxNMRke5+KigZ8BhTdeyls0bwcWaQYsBhzvZ7gDfYA8s+PFwCaRJ+7KQl1YmIk3zzofvmaNc8HTYo7e5p6/9gUtNIKMvg7UY0zposi2T2oHly5dLt8Fdd93V8Dt+HizzUJthmTVQqCgxiuUrsxZKEHkkeQ/KKooxZ9xiOQ6KIA1OLo1IAcVXzBCcO5yLYdN7wieobUSOx8SMBUlASwJH2mKTBCiLa35P8sCBOSSj7JrQ6DqoqC5FZFwwTu5mj7xZOGwdBFtCTVUtnF0dETWy/Y6TvD5DB7qiOOfSHIRLZT5bx5FTcAH/+epl+HkH4QcLfi6+B1J+qK2Xjpb2Dsd64cXnkZacDffqHggKDEZ5VbE4a+YWZmL+pKVwdXETcfcL7/wcQ6PHiUiZU1Cz8tNx8NQOuDq74wf3PaDHoV8jGLqiUMd6GA7bU2imYQ1nJzNLrzFVw93RD0FO0citPQ1TnRF1tW5wdHKS3aotqHY5fqU6vjulb0m2okaGSLDPv1CO4pwKccVjcOXo4rAo33YvGBRRst5pj6kIa5bMKJCckZSpwM2vliUYa5w7kiuKaE+LHc7pjONwdXGXgUDvrP+7GL5wIYobMAk3T1omZaWGNq9itnnlYsSctim7eZw8Z1oqP/G105eCQ3JYJlFQJSQKDlXGQKNr4LnnnkN+dhGQHwhfL39UmcqaBEGisCRPVPMEHSeJLfu/EP1T/yH94Bs88YqOY9XGFTixJxW9QvojKCBINkHWx1FtrMJbX76EyuoK0VclpR5pGLtNYdVAyUq1r6Nhzpw5WF30KeJ37EDpsRJ4uHkgPLgv5k24A4P7me2QnQ2uGD14ugzlouC7prZGNnKDeo3BnEmLMH2h/XbQGt2YDJAI2LID4EJOBmwNnmjqBCT8HHuhtrYeBUhBeUUZXOvMwhfLhVdGjl5Sy1Kwxro0/QW6I/i+0A2Pt478m6rkYA9YmmEAZSZBpc/5XGZzXn75ZSEMLCGoG7tCTu0pADdGliLI/OIc1NXX4Z31f8PoQVMROf52nM1MxJ6j36CqugJ3z/lRw/HRPpgmVkNnRDSMV7UHzAoQLYlDKRYkYbA1+Iqvjx4VHLHdWneLRucBgyAdRw8e2428nAK4u3ogIqRxECQKS3PxTfynjZ67cc9qs8dH0HgAP7yi41hw8424cPYd7D+xFVWmcmkVpCjP8jg4fry4zNy5s2HPKvnKS5FlPzcvZ3htKcWEKWPa/T7wxhblzW+fFE8NTkW03DjQ9IjeJAos+Rbm8ZgKMO32wW3Oxml0UzLAWhhbv0gKLOHt4YeSissueQql5UWXfu/bsND71veEoc4DgZHVyMhMa9AHNPp/XFykfk3FOkVsGh0Hps8Z9Fgjtwd0JORzLNs6mRXg3+DOml0gHDfNACr3O3ng5sE/QViPxkOljMYq1NRUY2zszIaeawqrSCLZkz177G0I8jM/x+DqhIrSapxLToebj1Ozym6l7lbKbgby1vwPWCJgFsDWWGw+l0SU740mA10HKghSOLfj42ScP5InWTTrXv3IiEFiO61M0arL6ZFiwLS7ByAixr9DjuOGmbOw/YMkEc6yhZplL8vNToBPcMMxEMz4MYvWc4A/Zt43sE3ktzkERniJ9mjb+4koyqm0qT3i6zdWmDdw/iFe2JXxIRJeXI0VK1bYHOimcfXRpcgAT+7Anl7ISiluVFcKD+6Ds4dPiTDFUkRIUYr6vQJ74kPCeuCWxXEoLimWti/WarkIM8BwJ8patWUKV6NjQaK1b5+5/1ktVMq90RIMrnQmZEum5W6bvc4kaWzjVPczIFNDcP70BRxdUwxn58antioDDI/mDuwyhvefIGQgLetMAxng6pWTnYMf/fB5FFZm2f266KnAUdjNiUaZbaImgC2Eth7TsFCamma5NDo/uAOeelcMvP1dsWfdKTgUOcHL28sckJmlEuJYL14X/NkvzEOEqh1pvSvHcHcM9q5JkVIXDcFY9nNxv0wKeI7xfh4HsxJRccGYuCS6Q4iAAscy3/STODGHS47PRm56aYPZlxynswOCentjwLhQ9I0NwvjsMGmz/cUvfoF//OMfsiEj2WaLMgk0BcUqW8u1mWSaXUldyY20s8PQFUfDZp0tlmEYTpe0A9zh7Ty0ThZ15TNAHcHBxJ3oFRoFPy9zPzpTYUwq8G/Ixejnh/HjGwcHjasPXsi0F+bFrVL/a9asaVDfU0xHEyI+hvexvVBBzS2gENGSIHC3zd20t7s/kjdwMmDjmjvHA1M4pbJECl4e5g4RCpsUHOAgHgZ/uPv38PA3+x20duO5yfbA5lwqlaaAv29OIKjI0ZWMkdW4tuCaFBLrhNX/8zIeXPxTeJmCUF5cbRb1idOgA/oMDZSplBH9/ZronzoCDOqTFvdH5IiQhpktMlHU0mzL1Ql9hweJ2VZ4fz+7fEXapT0aESLiyoLMcmlZZiaC97PzIaiXeZYL0bdvX7zyyiv40Y9+hGeffVYEw8z4cSYMib4yXeI1QtKfkpIirblcB+ha2pnbtrsKuhQZIHoPCRQFO0d/cuKX3BcahaHRY7Fx7ycyIIPjQelAWFiah9tmPNTwXKri3TwNcnJqXDvQgZEGTayfM/Cp9kPuAJgxYJaGhIAZBIo3qQsg1HAn9TtbYIsXOwjMCu3LC0REcF+cST+O4vLCRr3WFBQSnhYjmpnudXV3wbhJo9o0J4CiQGYyWtIU8PWyU8IW1M6Hvd4aXRcrV66Em5cBdz8xT4TNdD7l+chdO8sCrKNfbTC4cy4Gb/z/6cLJnTmhbLivZChSm47FwUFKB7w1B64FH374oXz/5ptv4uOPP5Z1gpu1QYMGNXk8CUJRUREeffRRySr+7ne/wwMPPHBVX8f1ji5HBsgq42b3wp7PUsQPXPX13nHDY9jk/SkSknZJPy3bVtg2ExlhtraVFFVtvbSMWfYCa1wbMKVOhs8SDWuEY8aMkVtzUESAOwDOdGguFc8Fl2TvxM7Go1CHRY/D9oSvcODUdkRfskElDpzcJj3W6jwRe2VjLfqPCm3zwCDu+LmoWY6zVuCCde7cOcmKNGdrzcdYdxlodE5Qy8KSD89JBiaSPAYvElVmiJ588smGDI9P4LUVxXEXzpbBzoyLFy/K+T9p0iQpo/E6ZImAwkyWBUeMGNHo8VwH2JVDfQ03D/QvURkEjW5CBgie2Ex7HdmSjopSowR31oTnT7xLbpZgeq6qgi2JdRg0KRxDprTc86vx/YBp+JtvvhlffvmlEAJlc2rdUqdq6FTg8/fTpk1rdnaBQvSoECTFZ4kdsbOroUE3MmrQVOlnZqmhX/gAnMtMxLEz+zB91M1SRiA4uZGp3uh2LJ5cxJi6ZKeDtc0yFy7+v82VCFg+4KL3wQcf4ODBg3jsscdsdhzYC573ygnuaqSAuyN4LpLQsf2TVtO2Sitfa4oAACT5SURBVEI8T5m1mj179jU5xq4KkgBmCzm5lKVCWh/zenn77bclW2hNBkgc6FcyceJEmVWSlZUlmcXu4gdzNdAlyQADxvAbekkrTMKGVFSW0L7WnP5iTY6gqRBFMlwUKZ4ZOr0vhk6L0AtjJwINgziemBczdwHs7CCzV+xeDXdSegBe+PZMeQwI90RoPx9cSCqCk7NTw/ChRdPuF/0ItSQnzh2Ev1cgFkxeisnD510mjuUmcZQL69c2t0mC2QC6Vn777bcNglQFLlQUSLI2atOXvrJSSA4XRRot0YmQ35MU2Oo8sAVmv84dzZM6MZ3oqJHha2c6mDXqvsOCdFasHeDu/z//+Y/MmaDzJUkbZ0mw5diSvCoPCT6GLclBQUFC6OigyXKXRsvg+8brXXUTUIvD75k1sAaNyLihILnesWOHELUjR47Iz51t7ktXgUO9vQ3fnRT05D53OA/J+7JQklclCyDh4Ah4+bXPKU/j+wcFg6yps7uDiy3BdDp3CxzuxN1CWy7yssIqbHzzBIrzKuDh7dpAEpsDzxvqUHjOsC2K1sztAQM7yQAnFvL4rX0srMEAwl0OF7YlS5ZINwv/Bqc/snZ6/vx5CSysjdqqncrfMNXhyJY0JMVno6qsRoRidFO0bONiexfr1cyqjZjdu8GZUaN1cPAYS1MkeyzhXLhwATfddFOTXSitzUkYqHLn+UoSyywRie6LL74of0PDNpgdpNaCJFq12LIMs2XLFiHDt9xyS8Nj2U5McnbvvfdKBu61116TjCFbwbm54Jqh0U0yA5agGGfw5HAMnNgDRVkV0jOrnOT8Qj3a7ZSn8f2CwZBBjzfLSY/thZe/mwwc2vreKRRmV8BgcGqUObIkAdxRs6TA2i6HNbWXCBAkLFz0WdJgup/ZDpICpj0tW7u4A1KkhxkSBhc1+4J/g2OfmWpmcCEp4MLHsdckBZZlEqqzt3+YJIpxR4MjymsLsOXg5zKVsaKqDH7egYjrPwGThs2DyViHY9svoOBiOWYsG6htX+0EW1zZxkoiQNLKoGULJAMcPHXHHXfIz/zsqW356KOPZACXJgPNg90BFNAy9U8iTfB6YcmF/gkKvHZ4TZAYk3BRREhwrSCxJoHQZKCbkgEFpkOZHtbo+uioNB/J4NxHhuL49gykHMqVKYtcTMyEgOZVbFN0kB1zzJhQDJka0SEKax4/SQ01BCwPqOFXlhkCLl4MHNxdcsGz1U7Iv8ORz1wMOeWQ5QMGJe6CWD6IjorG7tWnkXaSA5mcUWYswr8+/5N4bUwYOgvurp7in7B532e4kHsO983/OUw1tVI+2bkqGdOXDdRkuRWQmFKcxhJPawOkuJtl54v6nLlrZdaHf4NkUKN58H0ixo4dK4GewkCWXJThlwL1GjQYu+2221r8OxrdmAxoaNgCp6eNvTkScbN6Sz09I6kQVWVGSZtTc8Ie637Dgzu8ls6AwB0Kb6z9U3hGXQB3LwwqtE5mRsCeGQQkBfPnz5dsAUkBMwVLly7Fgsl3obfzWLh5uEja/9DR3aiqLscPb/s9QgPMQtlxQ2bIgnooaZdkCjgtzs0TSDtZgJSEHMSMbezUqNEYTPGz192eYN6nTx8JYMwQUNvC7A+nWLJrRmULNGxDBXzqLHgjWB5ku+GqVatw//33S+aAWgG2G9qaIMtrqSWfD42WocmARrcASwQDxoXJ7fsGRVAdoXJmNmHBggWSLfjqq6+RsCYb1V7V8PY3t65V1VTKVy8LzwT5/z194eDgKL7wBIkDx1An7csWDYHuNmgezOxYj9tuDizrUGzINDZvBEnEwoULpe1Qo3k0Z7TFktj69euFkKnPgpkDVR5QdvIsufG+5tp2NVqHJgMaGl0MJAWTRs1EcfxR8cBXiAwfhB0JX+PTrcsxa+xtkgVIvXga8ce/xcRhs+HifHl36+JhQMGFMmSnliCsX2NXRo3LoEbA3rIVAxG1LyR/NJZi2YBtcWyfZfcBPSQ0bIM23tzZW3t0KGtuvpcUYzK7xsyYNZiB2blzZ7cdKtcR0GRAQ6MLIiOxwOyU6Hn5Eh7QZxhmj1uMrQe/xKlzhxrunz5qIeaOX9Lo+ew2YHYgI7FQk4EWwNS0veOkaZDDx1qWBOis+eqrr+L999/HDTfc0JaPuFuBBIrOmzRzUh4dLKlRr0FywNIBTcn4flqCmRhmDpgtoPaGJTmN9kGTAQ2NLggGcsI6UPl7B4mhUmzkaMkMJKYewbaEL+Hj6YsJQy8b4cjz6i//HXtA7UHO+VLkpJbAWGWS8gKFixED/OEbfH2OnuVu357uawYxKuKZAbAEywTUhrRkU60BvPDCC9JGSzC7wgwAfQeYmSGJYhlBjSm3hCoXsNODJTQ97bP90GRAQ6MLQvlpWOLI6b34bNvb+MWyF+HrFdAwxIvBbP2eVTKhkQThMuplnnxroD3zuSO5Mn0uP7MMdab6xkNvXBwRHuMvQ296DvS/rjQI7A6ger01KBW7NXFgmtvSFlvDNtgxs3r1auzevbuhJZOBn+2Yzc3yUO+3Gmlu7VKo0TZoMqCh0QVBHw1r7D2+BeFBfRqIgMKgfiOQkLgTmbmpiO512eJYXDvdWl4COHGPc+mZESByyzPwbcJnSM063TAkbNbI21FzvBbppwoQPTIE42+N7NBxuNcSNLxJTk62OW/CEkxxM+Czm4BBSQV/dhKQTFj2yms0Bd8f3vhesdxCEyK2c7bkNaKEnb/85S8lK6BFmlcGTQY0NLoggnt7SzBX8weI0opieLg29dqoq6uVr7WXvhImE50PK5CRdxrDq8Jtts5VlBjxzVsnZPysu7cLsgrTsOKr5+DrFYhZYxahrr5OxIlvr38BP17yLHydg3F6f7YYIU27O+aKxvNSPc62PqrEufvjzo8mMxSafZ+gfTQD/datWyXAK/W68o4gaEHNwDV8+HAxzOF8CdavKXrbv3+/lBr0RD37wM938eLFIrpkiYDgZ8/3UDlqsn2QWg5+T30BSzNs69S4MmgyoKHRBcE6vXeQO0rzKxtGeQf7heF0+nHkFWUhyC+sUfmArYU9gi7btBorTKisLcWL/3oJf1/pghkzZsjuirPhqZ7nnAYaE5EI0KuBgf2b+E/hbHDBjxY/01BuGBEzCS9/8EtsjP8E98x7UkoE54/mwS/UHSNmt22B5k6PfgwUjalBQCoAENwFUpHPNk1a/trT7mcvSvIqkX3ukhbCwUFaUXtE+4qNOW1uX3rpJTHCUTv+pKQkuRGcP0AyxZZPBjN65LMfngI4EpjnnntOq9zbAIoFly1bJjoLmgxxNgFJId97niPFOVUID+iL6MgYRPQMh2OFlziIaovtbj6bQEOju+LYtgwcWHce7t7OEqzPXkjE8i+eh6e7N8bH3nBJQHgYyalHMXrwNCye8ZDFHAYjRs7pjeBBTli3bp3c0tPTJdXKoDZpxA04saFY0v3OruZU7R/ffBQDeg/D0rlPNDqOlV+/gjPpx/GHB1+Dq4ubzAth+WHx06NatTymGvzdd9+VAMp6MTMC7NdnALacYsllikGBXvX0pudOnBbOTBFz594e8H24kFQo2YwLyYWoMdZJtoXCSsLZzQl9YgMRNTIYB07uREJCgpjd8P9uCTxWEgcSBHrqa3vc9oPvJe2gC/IKkX22HHlnjEg9lQNPD2+4KUdIBwhp4zAuflbXemR0V4XODGhodFFw8aOLIGcNePi6IjJiIB5f/Ads3v+56Acqqsulu2DOuCWYNnKBPIc7/oriapm/QPdB7vofeeQRPPzww2LqQlLAFG3i1gIM6z0FAaGXDYxqa00wODU1h3ExuMjvsgsy0DssGq7uzmL9fP5YXqsOh1SDv/HGGxL0KRpTX62tf5mSpxMd76cdMwV7a9euxdmzZ/Hee+/ZbTbTQD4OH8We7QdRVFiEhRMewJghU+Hpe5l8pF1Mwb4D25G+OgU5RRmoNJXjxptmYeCggXIslqlrBe5auYOlvkClrzURaB/4ub7++uuiwcjNzkVFUR383EIwYfBcxEaPEqLG9/6TLW/gYOIuOa+5raUtvYevCwYMjpZxyBr2Q5MBDY0uClooc7DS5rdOoiS/UuyVe4VG4YGbfmHz8bU1dagsN8oQJ84lIBFQ4MLK8a+8Pf7oT/Dxc3thcGoc7IL8eiAt+4wEPWXEY6o1IT07Rb4vKS+Ur2oYVPK+7FbJAFPCzzzzjNTg2Z7HwG4LNJVhnZi1d19fXwm4bCOjPTPJS3Ne9bbJx5sw1HjC1zkExQ7F8j5aZzCS04/i0JldCAvoCX+fYBjzazBn6H0YOicYx44fk+wENQGWiVW+Vx4eHpLVoH6AnQga7QOzQCRus2fORcbhShTll+J05mF8suOfqHF8QGy2FZwNzrht+oPyWVRX1UqjS9zUSP3WtxGaDGhodGFwhz/nkSHY/mEy8jPKUF1ugsHVSdr9JOfNCYnGOhirTRLAgyK8MG3pAPgGNz+ZsbK4RjIArlYBckLsDVizfaU4HE4dsUAW360H16Kkolh+X2MyNjzW4OKI4pwKScVzt9YcqAugUJD1/5Y0AKzPs8WMRED+vsEgP2/fvl0mAtpLBkg+/vSjfyI7qQZ55Rl4fc1/23zc+NiZkk2hRuKLHe+goCQHFRedUJ3pKaZCTF2zps0MBbUBLGmwxELhoB5KdOWgedD4cROw4fVj8AwugUeUK2Y4zMernzyDXUc2NCIDjg6OGDHgstkQy1S46CDdLb0GaUJmLzQZ0NDo4mCNdMGPhiHzdBFOH8gWV8Gq8sv97U4GB/SJDZLJjBExfq2q/CnGQl3T3vhxsTNRVFaAHYfXISFxl9wXEdIPU+PmY9vBtY3sjikkrK2tg7GyBm6etn3nCaaBucNuqQ7PNjMGXU55tAQDMAV7auStPagoMqHgXC1cXJ1gMDZPUrw8Gls0863giOhT32Vh8KRwmwY4Gh0LBvOctFIZU68mjbJtNiPnXJPHMltlNFXLxE5mesqLjaKpEd8L7fFgFzQZ0NC4DsDdd88B/nIrK6xGWWGVmAU5uzjB08+1TaOZnQxOEv3MKfDGAZO2xlPi5iGnIBNuru4IC+yFjXs/kd9ZdjBUV1UjOycb02Y8heCQIAmcTOurIMrveWMg52Ldkv8/xXiENWHg82hjyxIDH6NsbFsCNRYUClIfAHNnoN1w9TCgssQo0y8Hjm9MTDQ6HhR2Go3VgIsRVRUVOHX+EJJSj2JY/3GNHmc0GfHs8sdQU1MNdzdPDO8/HjeMXIK89DK5sQ1Xo3VoMqChcZ3By99Vbu2Fp5+LZBOoMVAeBpZgl0Lf8Mse8Wxn9PEKQLDf5QDp5GhARO9Q/PLmp5GVlSVpddaB2TbI75laZ6p/ypQpTXb81lDDamwZ0FDIx10hzX1aIwMkR2cO5shra89uUcodDkBSfJZMv9Q7zquHouwKXEwpxpYjn+Bg0na5j+2xQyJHYeGUexse5+3hh6kj5iMiuK+Q1+S0o9h7bAsyc9OwbPpTOHMwW5MBO6HJgIaGRiNQYBje3w+pJ/JbbQ2kh8GFnHOYP+nuht09ld0cTz9mZjSGTp/e5DkM3jSUoacAzXxa6wRQWgISCGvwPt5eeeUVqdWTHFh+VTf+7GRyR0mhJzy92t96xkxLaV6VGCu15t6o0X6wQ4blqikj5yFuwHgRpx5N2XfJfvjyeTBvwuWhUASzAkG+YdgUvxqJGQkISdcTOe2FPps1NDRsti2yZmvpcEgfg28PfIH+vWPh4eolnQUHT+1ETJ9hmDTsst0ujXtolxw5ItjmO0vSQLEd0/579uxpdRCQ2vEr/39LsDzAQM92RA63YbcA2/t4oxbB8quvSyhm9b8fnt72l0ysQS0E5zloMnB1wfeXmZdQ/wiEBphHP48cOBkr1r4ovhY/XvJHpKamITgkGJ4ejctHk+Pm4Zt9n+HsxZMYXznlKh/p9QNNBjQ0NGw6HLJToSCrXAxduDD7evmLcnvHoXUw1lTB3zsYc8YvweTh8+DoaE7hkzywJh8zNlSe1xIYxCkCZKBuCTT6YcseywzWYIsfXQn/+Mc/ymNaQm5GCdb98xjpSLs/cSEuIsps/9/QaB0UDPK9th7yxMFba7a9jbSLZ1FZWQOnS+edJdgBwlJWZXW5/pzaAE0GNDQ0msDJyRFT7x6Ab1acQEWxEe4+Lgj0DcWDC59u9t0iEaDZUEgfH4xd0K/Vd5UaAFr5xsfHt5odGDhwoFjTsrOA5IBgmYHlBjoRtkYECC9fNwkOPE6Dc/uCOZ/r6uFsc1CURseBszBICOpq60XjoWC61L6aV5AHP/dgm22c1cYqlFeVSvaK4lkN+6DJgIaGhk0E9PDEzPsGYdsHSSjNr5IASg2BMhWyDJDVFSbRCoRF+WL60oGtag0spwKuWLFCDIVUhsB6CBAX/IkTJ0ob4vvvv48xY8bI41liYLnB3iFADDDMeLAbwNpDwR6QsNSa6hE1IviKhjBptA5Xv1p4+rmhvKi6wRyLLpcJSbthMLjAxcEDHp7uEvhpgW0JlrL4WUWFx6Lf8CD9dtsJTQY0NDSaBduy5j0ai1PfZSIlIVd2/papW35Plb13oLuUBgaMD2uTsI4mQCdPnpQSgOoWsDUEiNmAe+65B5s3bxbRIR/bu3dv3H777YiMjGyTFuKTT1bBhGqUVZnNkk6lHkJxeYF8P3HobLi5eqCwJA+HknfLfRRIEt/sXSOEx2v4GIxB08wH3wtmKuicxw4IlkDoQmhP1kKjMV746/NIS8qGu7EHgoOCUVZZjMPJe5BbmImZoxbB4OgMB0M9nn/n5yIaDPY3d6ScTjuGpNQjiOoRixGDx6LvUE0G7IUeVKShoWEXjJUm2VXnnC9BVXmN7I7dvAzoNTBAdtztraMzC7B69WppD2xthr3qRqBwkARhyZIlCAwMtPv/oiPiuKHTkJufAyerDAfx9L0vI8AnGGcvnMKba/7ScD+LGKYaExwMwJwF0/Hmm282/I5ZCjVhj22U7G4wkyRHIQR0JaTNM/0VdDuifdi0aRM++fhT7N12GBWVpXB390B4UF9MHDZbhKAUhfaICMWXO99DWtYZlFQUob6+DgE+IRgWPQGjo27AyFl9MWpeX7vPje4OTQY0NDSuOTiXgDMG6EFAMsBsgDUpsBwExEmFN998sxgXtRUcy7xpxXHJcnD8MzsEWgLr1hUl1aiqL8VHu1/C6AlxMi3xiy++kFLF3r17pdOB0xZHjBghx82gT1JAU6Xk5GTk5uY2lEXmz5+Pe++9V4iCRvNghmX3umM4+12ZZGScXB2EwGVmXkRgYAACA4Oa0a3UoEeUL2Y9MLhh4qZG69BkQENDo9Ms/hwTfPz4cdn5q3KEmmOvBgENGjRItARKSNgeZJ8rwfYPk8SpkQGDpQ1rUsAsgrHCBFNNLfzDPDHjnoE4mnQAzz77rHgfpKamikcCWx9Z5qCQcdiwYY0yBi+99JJ0O/Tt21ceS9Jw/vx5Of5///vfOlNgAyROHGmdmJgo5M+xxB/OBT2AOkfUO9bCVGeEh4c7PD29GqZbst2TuhUSt7BIX0xfNkA0Ihr2Q5MBDQ2NTgUGUYoIGTRJEEgK6CNAjUBHDgIqzq3E8e0ZMmrZWGk2slGEoJ6uSXCQSZCRccEYOr1ng5CN7Yy/+93vhJgwyJO4rFy5sgkZYGaAJKFnz54NP5MMcDzv/v378dprr2Hs2LEd8lq6OtRo6V27dkl3CX+eN2+ekCaWW+pL3VGX64v6SjccTNqBhDPbUVyeD093bwzpOxYzRyyCX6A3+o8Olc9KZwTaDi0g1NDQ6FRg+pzCQd6uJnyD3TFpSX+MmNMHZw/nIvNMEarLasRymCSg96AA9B0e3KTzIDw8HAsXLpTuBs5GULMTrMFygSIC6meSGj8/P6l5szVSkwEzaBb16quvSgaIoku+P9z1K1dLB+9KOHpV4ttN2xF/LB4xveIwatAU5BVn4sCZLXAJrsBbz78pDpEa7YMmAxoaGt0a3PHHTo2Qm736Bo5dptixrYJAVSqgWyJJgcbl94U6CgZ/CkqZabFGeXkZ9h/ag9gRg7FwodnxsqoqDL4H65GQsA979u7G1KlT9VvaTuhmWQ0NDY02gK2QLGWoenVbQV2EMlLSMIPdGCRWLQ2bysjIkMwBRZgKLBn1799fygobN27Ub+cVQJMBDQ0NjTYgPT29QdjYVuzevVueP2HCBJuzFrojampqZJql6sJoDmpQlfVgK2Zo+DfYuaHRfugygYaGhkYbIAr3S7XstmYUduzYgeHDh8uNdfHWQD0CyYN6LLMR1CFQq3C9gEJRlgaop2gJyk+CGYI+ffo03E9vB4pM+T5ptB+aDGhoaGi0Ae3JCFAsSB+FqKgo3HjjjZIVaO7vMLBduHBBWizZVUHyYen4yNR4dHS0GBmxbbE9xKQzobi4WF5Xa2ZTNG2ieJPeDiwnkBDQ8XHDhg3yXJYKNNoPTQY0NDQ02gAGImUiZA8Y2D/99FP06NEDixYtarjfciesWuvoYshSAoPcrFmzJIPALIAiA4cOHZIefP6eugUGyLlz5+Kxxx6TQNkZUGOsRdbZYlSV1YhZED0cAnt6wTvAdksoTaTsxW233YY1a9bg66+/lp/5vowbNw4pKSmaDFwhNBnQ0NDQaAO4K+dOn2K21nbltFhetWqVGCRxjgLr3UyJs/5t6Z7I1jpaHDO4cefPryQL1qlzOjQyXU7xIY2PSArohMj+/I8++kgGN10rFOdWyPyK0weyUVFiFCLA18GBlM5uTtKqGT06BOHRfo0Mnqw1AC2B7+N9992HgoICKaGwDZHkjHoBEiON9kOTAQ0NDY02gMZH3L3ThpjpbTVh0XraIokCAzTT/OPHjxfFPMExzGxNZLZg2bJlQhI4sOmZZ54R62K2Lr733ns2ywg04rEEhXMsPaxdu1Z2y/fff7/dr4POipxGWVNdK5Mo3Tyd4eVvv6GTymZQ/Lf/u0PIySrAzePvx6gBk+Hu5SwBn69h1eY3cDBx1yVyQGJggLe/K/r26yvvga+vb4N9c2ulAgWSAN6InJwcKbswi6LRfmgyoKGhodEGcOdOS2Sa5Fh2BFhPW1SBn+CkRYLZBLXD7dWrl6S8OZaZ/fEsI3C3z5u94K6aQZG+BfaWLkoLqpCSkIPk/dmoKq1BHW2f2Vrm5ICQvj6IGRsmu3gn55azHiqb4e7kC/e6IKA+H26ehiY2wCQALs7OuG36gzCZ6lBTZYJfqCemLDK3CNLFkUZT3OmTGLQFfD+3bNkiz3/44Yfb9FyNxtBkQENDQ6ONYJ3617/+taTtW5q0+Nvf/rbhezVkiVmCSZMmyX0M4uvXrxcRHO2NSQjsAZ/HQEjxHbMU/L616Y0c4rN/3Xmc3p8N06VsAG17Vcqev89MLkLm6SKp79OdkQN/mgOzGf964T2c3JyPiwXn8eZXf4aD0IqmcHRwxIgBkxoNE8LFQNFILF++XCyeVYlEOTpaZ1r4O04zZAYhJCREXvOJEyeku2Dp0qUYOnSoXe+dhm1oMqChoaHRRjBwc2oi6/XckXNnyrY/W6l9Bi8Gb4JTDekxoEBNwK233iq7bJYH7O0M+Mc//tEgvOPfmDFjhpQsbKXamc7/z8r/YNPnu5CYnIgqYzlun/UIRg9q7NbHEdQU+zFYF+WW4dabF6HSoQBP//opcQe0hsHJgPRD5TLQycXKstkWGLyNpmq4ubjD1cOAE/Hn8PXy1+Eb5irljd/85jfyPqj3wDrTQjJAXQDnOpAEENRdsMzy0EMP6aFPVwhNBjQ0NDTaAY5RXrJkCbZv3y5qdu5iGchUmp/BWQVspr9Hjx6NuLi4JkGLwZrPbctI4zvvvFP+NgWKbEFU2gWWLaynOZJk/P3l1+AKb/QI6oXUrKRmd/CKFBxJ3YHC0nyYao0oyTcTGWtcSC5CSV4V3DycgVYsE4wmI55d/hhqaqrh7uaJYdHjMS5yHu675Se4++cz5X1jqyTbL+mpQFGgLWLFQVBqGBQJFskPszR0IdS4MmgyoKGhodFOMGgtWLBA0vUcXMQdq5q0yB06fQAYvCjya04LwBZB7prtFc8RynSHfzcmJgZvvPGGPJ9/yxoO1W54cuGL8PHyQ05pGv75ybMt/u2yimJ8e/ALzBh9Ezbu+RSpx/JtPo6aAznuVrQF3h5+mDpiPiKC+8r7kpx2FPHHt+BCdioeDvq1jB5293IR7QAnP7JsQmLD94vZAMtsCZ9PssDXSb3ExIkThQy0x/tBozE0GdDQ0NC4QnDnTy0AbwyQ3LEymNkTpJQNLwNdezMUrKEzQ5CYmCjZB0uP/7SjRXBz9pL2PphL8C1iw95PEOQXJjX+b+I/k1HPBZnlCAj3bPS4vLRSGJxbJzDzJtzR6Ofh/ccjyDcMG+NX40hyPGZlxwoZIPr164e77rpLOhRIrJRAk++Nei9JAijgpEbA0olQ48qgyYCGhoZGB4I72ba4ArLmT+LQFvMda3C3zIzEL37xC/k7NCBi6jwmahCc06JhcHaxi5ikZ6cgIXEXHrvtd/Izn1JXW4czCdkYGx5p8f8ZUV1VAweH9rkfTo6bh2/2fYaUCydhrGr8utkdMW3aNCFWFBEqgyWSAIo1+br0xMeOhyYDGhoaGtcQ1AowwFFZz7R4c2DGgUHf2oiIDocMmOxQ+POf/yxeBeq2bcNejAoORkSf1g15uPteu/NdDIseiz5h/VFQYm5VrEc9vtt0GO9seEU0CrxR6LdoyFMICQmDq0fzkwabg7PBBe5uXiJmdHKyTVIoyKSOQOP7gSYDGhoaGtcYVMt/8MEHsqtXHvvWrXXEP//5T0mRs62PJIKGO7QnZuD86U9/KhoCNf+ASDmUg+0fJEkbYWs4mLgTWfkZWDbvJ01+V1NdDw9fD4wcOVL+b94KE3xQX9W+EFJtrEJ5ZSk8PXzgdqlEoHFtocmAhoaGxjUGhYaskzO4KyGhdWsdZxTQZS81NVW0ASQO1AbQHpltjmPHjm3yd+0V1lUZK7Fx7yci9PPzauxXwOPp07s3nv7d4kb3H/VOx4F158VZsDmYTDWorauFq0vjjMe3B76Q8kPc0NEItNIiaFwbaDKgoaGhcY3BoP3NN99g9erVkg1gbdyW7mD27NmNygY06CFJYIujrcezn9/R0QF1tfVwMjRPDHYeWofaOhOGRY9rKA+UlBXK18qqcpSbCsX62HKOQNSIELz56n9QWV2OihpzBuNU6iEUlxfI9xOHzkZFdTn+seoZEQ0G+5sNlU6nHUNi6hFER8Ri8dIFjeYUaFw7aDKgoaGh0QlAB8GFCxfiq6++kpo8Ay/T/9ZBnrV9uhkyOLOLge14TNvbAu2F3X1cZIKgtU2wJYrK8iXo/+3D3zT53Y6jX+PA+W8w6e7V0sao4OnnioPnv0XmhUwpQzCkn0g5IDciLmYi3F09MKhvHM6kH0dC0i7U19chwCcE04fdihvG34zIuJAreMc0OhKaDGhoaGh0EtCOmI568fHxIgBUrXWKEDAbQDJAoSEnF7LHnq2FzcHZxQn9R4fi8Ob0FlsXJw6bg8H9zLoEhbLKEny29W2MHDAZDz91h80RyZu3bsCm5cdRcLEcHj4ucHRqmp24Y9ZjDd/zGCpLjWJsNPWOgXC1w7lQ4/uB/iQ0NDQ0OhHYNjd37lzpDqCREcclK1EhOwnYi08RIcsD9iBqZDDeeuNd2flXmmyn82kIxJsl8otyRA8wYHB/zF84x+bfJgGYed8gfPvuKfEiMLg4iTUxSxOWIAngdERjpUl+P3FRNHoNMk8d1Ogc0GRAQ0NDoxOC4sAxY8bI7UrgG+yBY5nbkHYugxOD+K9JOt/N1aPRc0gCKkprRGfQN9Z2CULBJ8gdcx+OxZEt6Th7OBeVJUawZmDuYHCQv8UbnQp7DvDHsBt6Iaxf26YTalx9ONS31/ZKQ0NDQ6NLgMF4/9fncHL3RfmZo4ZtpfRlnoKxDtUVNfDyc8OMewciuLd9GQiiqrwG547k4vyxfCkHULjIUkBopA+iR4UioIfuHOis0GRAQ0NDoxuAgf7UdxdxbFsGKrh7Z2rYxUlS+vxdnYmDlWrFYpgEYMKtUfALbZwx0Lh+ocmAhoaGRjdCjbFWhg8l78+SOr/4BDg4wODsiH7Dg2QHHxjhqYf/dDNoMqChoaHRTWEy1sJYVSvaAGe3psI/je4DTQY0NDQ0NDS6Odo3ckpDQ0NDQ0PjuoEmAxoaGhoaGt0cmgxoaGhoaGh0c2gyoKGhoaGh0c2hyYCGhoaGhkY3hyYDGhoaGhoa3RyaDGhoaGhoaHRzaDKgoaGhoaHRzaHJgIaGhoaGRjeHJgMaGhoaGhrdHJoMaGhoaGhodHNoMqChoaGhodHNocmAhoaGhoZGN4cmAxoaGhoaGt0cmgxoaGhoaGh0c2gyoKGhoaGh0c2hyYCGhoaGhkY3hyYDGhoaGhoa3RyaDGhoaGhoaHRzaDKgoaGhoaHRzaHJgIaGhoaGRjeHJgMaGhoaGhrdHJoMaGhoaGhodHNoMqChoaGhodHNocmAhoaGhoZGN4cmAxoaGhoaGt0cmgxoaGhoaGh0c2gyoKGhoaGh0c2hyYCGhoaGhkY3hyYDGhoaGhoa3RyaDGhoaGhoaKB74/8DNSirzOw34UQAAAAASUVORK5CYII=", "text/plain": [ - "\"Output" + "
" ] }, "metadata": {}, @@ -1291,11 +1312,17 @@ " colors = [\"tab:grey\" if i == 0 else \"tab:purple\" for i in x]\n", " pos, _default_axes = rx.spring_layout(G), plt.axes(frameon=True)\n", " rx.visualization.mpl_draw(\n", - " G, node_color=colors, node_size=100, alpha=0.8, pos=pos\n", + " G,\n", + " node_color=colors,\n", + " node_size=150,\n", + " alpha=0.8,\n", + " pos=pos,\n", + " with_labels=True,\n", + " width=1,\n", " )\n", "\n", "\n", - "plot_result(graph_100, to_bitstring(sorted_result_dict[0][0], 100))" + "plot_result(graph_100, to_bitstring(sorted_result_dict[0][0], 100)[::-1])" ] }, { From 37ece5e58abc8f50a6a4d5952a4f51b686a9f7df Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Tue, 2 Dec 2025 14:02:12 -0500 Subject: [PATCH 18/28] generate avif Co-Authored-By: Mirko Amico <31739486+miamico@users.noreply.github.com> --- ...2ae28b3-85eb-4487-8100-1e622e93cccf-1.avif | 0 ...b864d32-9483-45ea-831f-60488e330adb-0.avif | Bin 21036 -> 19235 bytes ...689e09e-6ca7-4154-8602-d1d954ebe80b-1.avif | Bin 18607 -> 17646 bytes 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/82ae28b3-85eb-4487-8100-1e622e93cccf-1.avif diff --git a/public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/82ae28b3-85eb-4487-8100-1e622e93cccf-1.avif b/public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/82ae28b3-85eb-4487-8100-1e622e93cccf-1.avif new file mode 100644 index 00000000000..e69de29bb2d diff --git a/public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/8b864d32-9483-45ea-831f-60488e330adb-0.avif b/public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/8b864d32-9483-45ea-831f-60488e330adb-0.avif index b4e4d1bf125673904639e4564128ab81a240b078..300574b452a20c4d0334f975d18e8e35dea7ade8 100644 GIT binary patch delta 18997 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more avifs --- .../advanced-techniques-for-qaoa.ipynb | 15 +++++---------- ...61821cb-9a0c-4efb-806b-75513302d34a-0.avif | Bin 6301 -> 5989 bytes ...2ae28b3-85eb-4487-8100-1e622e93cccf-0.avif | Bin 101100 -> 0 bytes ...2ae28b3-85eb-4487-8100-1e622e93cccf-1.avif | Bin 0 -> 80636 bytes ...52dfeed-2871-4ca1-9754-15c95293198e-0.avif | Bin 8323 -> 13946 bytes 5 files changed, 5 insertions(+), 10 deletions(-) delete mode 100644 public/docs/images/tutorials/advanced-techniques-for-qaoa/extracted-outputs/82ae28b3-85eb-4487-8100-1e622e93cccf-0.avif diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 6ef371af1c2..ded239b9bde 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -161,9 +161,8 @@ "outputs": [ { "data": { - "image/png": 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" + "\"Output" ] }, "metadata": {}, @@ -539,9 +538,8 @@ }, { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -817,9 +815,8 @@ }, { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "execution_count": 105, @@ -1154,9 +1151,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -1298,9 +1294,8 @@ "outputs": [ { "data": { - "image/png": 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z#%4GT0geC_>CohMR6s$HgF6h|-XbXd1g)3W7H2u-?H92g6dJC$$ny~Z>uAm?m((Ahs8*(fgfNx<5#b`q`_6wOZNm{i-6evqF7L^7d%@;xQB3kzaZw>SEZ^nQ0MQTimmR9~VWA-gB1^SKR-nc!}r|ud5!dnIR=ZA*v_-W`r zFUsRQx;?9@dG8HmGN979dqjUj1k3E7PtdX0PaktE zw4K~}0Wp%9Rru7SD3I@&*1Q5u;KajQphmg_!}V<8ld7n1p=B8$=q>>i!FW%FBZ^$^ zGf4prmWp?Q2@U62Idi>BK&61pRGK$fSSlq(IcT7yhi1P7!|tuVsufYIa%Y78? z`-ITDHhj`c^~`^h(Kfs-1J3bZwKM91h9q;;zG$!OjV1Nz3l1h6sK pk0!zPqfdu+!5TsxMaEJYC^YW=6h4o6eYR%U!S(VS5&)A&HZu*9d#L~b From 3122dc991a381f06ae34edcfd5baf3178bc96351 Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Tue, 2 Dec 2025 14:03:46 -0500 Subject: [PATCH 20/28] rmv copyright --- .../tutorials/advanced-techniques-for-qaoa.ipynb | 16 ---------------- 1 file changed, 16 deletions(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index ded239b9bde..3a8f3730564 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -1347,22 +1347,6 @@ "\n", "[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_cZwpScxyXVDpIeq)" ] - }, - { - "cell_type": "markdown", - "id": "d325feb7-b9c5-4e6a-b1ee-93e5f96213cb", - "metadata": {}, - "source": [ - "© IBM Corp. 2025" - ] - }, - { - "cell_type": "markdown", - "id": "a1b8767d", - "metadata": {}, - "source": [ - "© IBM Corp., 2017-2025" - ] } ], "metadata": { From 12668c081f8f4c13d5ef84ae538950780aaa0a13 Mon Sep 17 00:00:00 2001 From: Ibrahim Shehzad Date: Thu, 12 Mar 2026 13:43:59 -0400 Subject: [PATCH 21/28] update tutorial per updated template --- .../advanced-techniques-for-qaoa.ipynb | 90 ++++++++++++++----- 1 file changed, 70 insertions(+), 20 deletions(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 3a8f3730564..0690a0514ff 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -10,6 +10,22 @@ "*Usage estimate: 3 minutes on a Heron r2 processor (NOTE: This is an estimate only. Your runtime may vary.)*" ] }, + { + "cell_type": "markdown", + "id": "1a6bbeef-966f-45e1-89aa-e964ef891eeb", + "metadata": {}, + "source": [ + "## Prerequisites and Learning Outcomes\n", + "\n", + "We suggest that users are familiar with the following topics before going through this tutorial\n", + "* The baics of the Quantum Approximate Optimization Algorithm (QAOA). See [Solve utility-scale quantum optimization problems](/docs/tutorials/quantum-approximate-optimization-algorithm) and this lesson on [Utility-scale QAOA](https://quantum.cloud.ibm.com/learning/en/courses/quantum-computing-in-practice/utility-scale-qaoa#utility-scale-qaoa) tutorial for an introduction to QAOA.\n", + "\n", + "After going through this tutorial, users should be able to:\n", + "* Utilize advanced techniques to extract the best performance from large scale QAOA circuits\n", + "\n", + "For a detailed walk-through of the contents of this tutorial, see this [Qiskit video](https://youtu.be/rBfK-l-qSNk?si=PC28gFAdu4JYSYdk)." + ] + }, { "cell_type": "markdown", "id": "ea97567d-810f-4cca-8edf-a47d70ea870a", @@ -17,14 +33,16 @@ "source": [ "## Background\n", "\n", - "This notebook introduces advanced techniques to improve the performance of the **Quantum Approximate Optimization Algorithm (QAOA)** with a large number of qubits.\n", - "See the [Solve utility-scale quantum optimization problems](/docs/tutorials/quantum-approximate-optimization-algorithm) tutorial for an introduction to QAOA.\n", + "This tutorial introduces two advanced techniques to improve the performance of the **Quantum Approximate Optimization Algorithm (QAOA)** at a large number of qubits.\n", + "\n", "\n", "The advanced techniques in this notebook include:\n", "\n", "* **SWAP strategy with SAT initial mapping**: This is a specifically designed transpiler pass for QAOA that uses a SWAP strategy and a SAT solver together to improve the selection of which physical qubits on the QPU to use. The SWAP strategy exploits the commutativity of the QAOA operators to reorder gates so that layers of SWAP gates can be simultaneously executed, thus reducing the depth of the circuit [\\[1\\]](#references). The SAT solver is used to find an initial mapping that minimizes the number of SWAP operations needed to map the qubits in the circuit to the physical qubits on the device [\\[2\\]](#references) .\n", "* **CVaR cost function**: Typically the expected value of the cost Hamiltonian is used as the cost function for QAOA, but as was shown in [\\[3\\]](#references) , focusing on the tail of the distribution, rather than the expected value, can improve the performance of QAOA for combinatorial optimization problems. The CVaR accomplishes this. For a given set of shots with corresponding objective values of the considered optimization problem, the Conditional Value at Risk (CVaR) with confidence level $\\alpha \\in [0, 1]$ is defined as the average of the $\\alpha$ best shots [\\[3\\]](#references).\n", - "Thus, $\\alpha = 1$ corresponds to the standard expected value, while $\\alpha=0$ corresponds to the minimum of the given shots, and $\\alpha \\in (0, 1)$ is a tradeoff between focusing on better shots, while still applying some averaging to smooth out the optimization landscape. Additionally, the CVaR can be used as an error mitigation technique to improve the quality of the objective value estimation [\\[4\\]](#references)." + "Thus, $\\alpha = 1$ corresponds to the standard expected value, while $\\alpha=0$ corresponds to the minimum of the given shots, and $\\alpha \\in (0, 1)$ is a tradeoff between focusing on better shots, while still applying some averaging to smooth out the optimization landscape. Additionally, the CVaR can be used as an error mitigation technique to improve the quality of the objective value estimation [\\[4\\]](#references).\n", + "\n", + "By the end of this tutorial, you should be able to leverage these techniques for getting the best results from running QAOA for your optimization problems." ] }, { @@ -97,6 +115,24 @@ "In this example, we use a graph with 100 nodes that is based on a hardware coupling map." ] }, + { + "cell_type": "markdown", + "id": "663d13f5-a5f7-4b67-a89b-26deb41224ec", + "metadata": {}, + "source": [ + "## Small Scale Simulator Example\n", + "\n", + "Since the goal of this tutorial is to show how QAOA performs at scales beyond what a simulator can handle, we will forgo this step." + ] + }, + { + "cell_type": "markdown", + "id": "6ca05a7c-5fde-4485-a3ad-c0ba02844e50", + "metadata": {}, + "source": [ + "## Large Scale Hardware Example" + ] + }, { "cell_type": "markdown", "id": "b825afbf-10fd-4926-bd65-05272044f107", @@ -106,7 +142,7 @@ "\n", "### Graph → Hamiltonian\n", "\n", - "First, map the problem onto a quantum circuit that is suited for the QAOA. Details on this process can be found in the [introductory QAOA tutorial.](/docs/tutorials/quantum-approximate-optimization-algorithm)" + "First, let's map the problem onto a quantum circuit that is suited for the QAOA. Details on this process can be found in the [introductory QAOA tutorial.](/docs/tutorials/quantum-approximate-optimization-algorithm)" ] }, { @@ -134,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "id": "e04ce982-8294-4ebe-8dcc-f74205938800", "metadata": {}, "outputs": [ @@ -150,12 +186,15 @@ } ], "source": [ + "# Check if the coupling map is symmetric. We will add a conditional below\n", + "# to avoid overcounting edges for symmetric/bi-directional coupling maps.\n", + "\n", "backend.coupling_map.is_symmetric" ] }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "id": "8b864d32-9483-45ea-831f-60488e330adb", "metadata": {}, "outputs": [ @@ -178,7 +217,12 @@ "\n", "for edge in backend.coupling_map:\n", " if (edge[0] < n) and (edge[1] < n):\n", - " if (edge[1], edge[0], w) not in elist:\n", + " # Conditional to avoid overcounting edges\n", + " if (\n", + " edge[1],\n", + " edge[0],\n", + " w,\n", + " ) not in elist:\n", " elist.append((edge[0], edge[1], w))\n", "\n", "graph_100.add_edges_from(elist)\n", @@ -839,11 +883,9 @@ "id": "e2afd1a7-0980-433b-a3a8-303d7e7718b1", "metadata": {}, "source": [ - "## Step 3: Execute using Qiskit primitives\n", - "\n", - "### Define a CVaR cost function\n", + "## Step 3: Execute using Qiskit IBM Runtime primitives\n", "\n", - "This example shows how to use the Conditional Value at Risk (CVaR) cost function introduced in [\\[3\\]](#references) within the variational quantum optimization algorithms.\n", + "Let's now prepare for hardware execution. Our first step will be to define a Conditional Value at Risk (CVaR) cost function which was introduced in [\\[3\\]](#references) for use within the paradigm of variational quantum optimization algorithms.\n", "\n", "The CVaR of a random variable $X$ for a confidence level $α ∈ (0, 1]$ is defined as\n", "$CVaR_{\\alpha}(X) = \\mathbb{E} \\lbrack X | X \\leq F_X^{-1}(\\alpha) \\rbrack$\n", @@ -1018,7 +1060,7 @@ "id": "63fa2ab4-5354-4022-ab46-e9bbf73870de", "metadata": {}, "source": [ - "The CVaR can be used as an error mitigation technique as previously discussed [\\[4\\]](#references). In this example, we determine $\\alpha$ and the number of shots according to the circuit's error rate." + "The CVaR can be used as an error mitigation technique as previously discussed [\\[4\\]](#references). In this example, we determine $\\alpha$ and the number of shots according to the error per layered gate (EPLG) associated with the circuit." ] }, { @@ -1065,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "id": "382e8acd-d0d0-4302-99aa-b64e5dd31e17", "metadata": {}, "outputs": [ @@ -1119,6 +1161,9 @@ " sampler.options.dynamical_decoupling.sequence_type = \"XY4\"\n", " sampler.options.twirling.enable_gates = True\n", " sampler.options.twirling.enable_measure = True\n", + " sampler.options.environment.job_tags = [\n", + " \"TUT_AQAOA\"\n", + " ] # add tag for your job execution\n", "\n", " result = minimize(\n", " qaoa_sampler_cost_fun,\n", @@ -1140,7 +1185,9 @@ "id": "1d190fa4-3bbe-412a-b296-6dddd3ad2b12", "metadata": {}, "source": [ - "## Step 4: Post-process and return result in desired classical format" + "## Step 4: Post-process and return result in desired classical format\n", + "\n", + "Let's now visualize our results and then postprocess them to find the value of the cut" ] }, { @@ -1187,7 +1234,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": null, "id": "7e8af29e-c99b-41f2-b6dd-2be471e1af21", "metadata": {}, "outputs": [ @@ -1200,7 +1247,7 @@ } ], "source": [ - "# sort the result_dict[iter_counts]['evaluated'] by the CVaR value\n", + "# Sort the result_dict[iter_counts]['evaluated'] by the CVaR value\n", "sorted_result_dict = [\n", " (k, v)\n", " for k, v in sorted(\n", @@ -1338,14 +1385,17 @@ }, { "cell_type": "markdown", - "id": "c048cd84-d6e8-4b8a-926e-a7f7724a86e2", + "id": "a6a5bbfe-a159-4dc1-9333-488737aff503", "metadata": {}, "source": [ - "## Tutorial survey\n", + "## What to look into next\n", + "\n", + "If you found this work interesting you may be interested in the following material:\n", "\n", - "Please take one minute to provide feedback on this tutorial. Your insights will help us improve our content offerings and user experience.\n", + "* [The intractable decathlon](https://arxiv.org/pdf/2504.03832): a listing of 10 optimizaiton problems that are difficult for classical optimization algorithms which may be good use cases to test out the techniques introduced in this tutorial on.\n", + "* [A repo of best practices for quantum optimization](https://github.com/qiskit-community/qopt-best-practices) to further improve the results of your QAOA-based workflow.\n", "\n", - "[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_cZwpScxyXVDpIeq)" + "© IBM Corp. [2025]" ] } ], From a0813e76218a2325a98f1f6b74c8a04e15bcf94f Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Thu, 12 Mar 2026 16:47:50 -0400 Subject: [PATCH 22/28] Fix metadata --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 0690a0514ff..cc99ceaf7e9 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -5,6 +5,11 @@ "id": "1a869c2d-ae51-47d3-8a41-bfcf5e505f59", "metadata": {}, "source": [ + "---\n", + "title: Advanced techniques for QAOA\n", + "description: This notebook introduces advanced techniques to improve the performance of the Quantum Approximate Optimization Algorithm (QAOA) with a large number of qubits.\n", + "---\n", + "\n", "# Advanced techniques for QAOA\n", "\n", "*Usage estimate: 3 minutes on a Heron r2 processor (NOTE: This is an estimate only. Your runtime may vary.)*" @@ -1400,7 +1405,6 @@ } ], "metadata": { - "description": "This notebook introduces advanced techniques to improve the performance of the Quantum Approximate Optimization Algorithm (QAOA) with a large number of qubits.", "kernelspec": { "display_name": "Python 3", "language": "python", @@ -1417,8 +1421,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3" - }, - "title": "Advanced techniques for QAOA" + } }, "nbformat": 4, "nbformat_minor": 4 From 7357ba707cc978635d685ee51e3e752a396c9f32 Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Thu, 12 Mar 2026 17:23:43 -0400 Subject: [PATCH 23/28] some cspell --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index cc99ceaf7e9..f65f7d66f08 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -23,7 +23,7 @@ "## Prerequisites and Learning Outcomes\n", "\n", "We suggest that users are familiar with the following topics before going through this tutorial\n", - "* The baics of the Quantum Approximate Optimization Algorithm (QAOA). See [Solve utility-scale quantum optimization problems](/docs/tutorials/quantum-approximate-optimization-algorithm) and this lesson on [Utility-scale QAOA](https://quantum.cloud.ibm.com/learning/en/courses/quantum-computing-in-practice/utility-scale-qaoa#utility-scale-qaoa) tutorial for an introduction to QAOA.\n", + "* The basics of the Quantum Approximate Optimization Algorithm (QAOA). See [Solve utility-scale quantum optimization problems](/docs/tutorials/quantum-approximate-optimization-algorithm) and this lesson on [Utility-scale QAOA](https://quantum.cloud.ibm.com/learning/en/courses/quantum-computing-in-practice/utility-scale-qaoa#utility-scale-qaoa) tutorial for an introduction to QAOA.\n", "\n", "After going through this tutorial, users should be able to:\n", "* Utilize advanced techniques to extract the best performance from large scale QAOA circuits\n", @@ -192,7 +192,7 @@ ], "source": [ "# Check if the coupling map is symmetric. We will add a conditional below\n", - "# to avoid overcounting edges for symmetric/bi-directional coupling maps.\n", + "# to avoid over-counting edges for symmetric/bi-directional coupling maps.\n", "\n", "backend.coupling_map.is_symmetric" ] From 4c38da1578acb71cd17a02a82c40fa6c1c242066 Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Fri, 13 Mar 2026 13:02:37 -0400 Subject: [PATCH 24/28] more cspell --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index f65f7d66f08..e3d69d68d24 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -9,6 +9,7 @@ "title: Advanced techniques for QAOA\n", "description: This notebook introduces advanced techniques to improve the performance of the Quantum Approximate Optimization Algorithm (QAOA) with a large number of qubits.\n", "---\n", + "{/* cspell:ignore pysat, lbrack, frameon, IEICE */}\n", "\n", "# Advanced techniques for QAOA\n", "\n", @@ -1397,7 +1398,7 @@ "\n", "If you found this work interesting you may be interested in the following material:\n", "\n", - "* [The intractable decathlon](https://arxiv.org/pdf/2504.03832): a listing of 10 optimizaiton problems that are difficult for classical optimization algorithms which may be good use cases to test out the techniques introduced in this tutorial on.\n", + "* [The intractable decathlon](https://arxiv.org/pdf/2504.03832): a listing of 10 optimization problems that are difficult for classical optimization algorithms which may be good use cases to test out the techniques introduced in this tutorial on.\n", "* [A repo of best practices for quantum optimization](https://github.com/qiskit-community/qopt-best-practices) to further improve the results of your QAOA-based workflow.\n", "\n", "© IBM Corp. [2025]" From e8cd0af5fda6f4c88cef63f5ffc0dba69cfaf98b Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Fri, 13 Mar 2026 13:03:03 -0400 Subject: [PATCH 25/28] missed one --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index e3d69d68d24..ab53f148d9e 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -223,7 +223,7 @@ "\n", "for edge in backend.coupling_map:\n", " if (edge[0] < n) and (edge[1] < n):\n", - " # Conditional to avoid overcounting edges\n", + " # Conditional to avoid over-counting edges\n", " if (\n", " edge[1],\n", " edge[0],\n", From ee68ce9fcbbd5face18ca4e371eb4e1489a125fc Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Fri, 13 Mar 2026 13:07:57 -0400 Subject: [PATCH 26/28] rmv copyright --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index ab53f148d9e..009a9038a0c 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -1399,9 +1399,7 @@ "If you found this work interesting you may be interested in the following material:\n", "\n", "* [The intractable decathlon](https://arxiv.org/pdf/2504.03832): a listing of 10 optimization problems that are difficult for classical optimization algorithms which may be good use cases to test out the techniques introduced in this tutorial on.\n", - "* [A repo of best practices for quantum optimization](https://github.com/qiskit-community/qopt-best-practices) to further improve the results of your QAOA-based workflow.\n", - "\n", - "© IBM Corp. [2025]" + "* [A repo of best practices for quantum optimization](https://github.com/qiskit-community/qopt-best-practices) to further improve the results of your QAOA-based workflow." ] } ], From 63fa22dbee73b1753e49d1c466000dd183a0e01c Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Fri, 13 Mar 2026 13:17:33 -0400 Subject: [PATCH 27/28] some copyediting --- docs/tutorials/advanced-techniques-for-qaoa.ipynb | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 009a9038a0c..8abbf078a19 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -21,10 +21,10 @@ "id": "1a6bbeef-966f-45e1-89aa-e964ef891eeb", "metadata": {}, "source": [ - "## Prerequisites and Learning Outcomes\n", + "## Prerequisites and learning outcomes\n", "\n", - "We suggest that users are familiar with the following topics before going through this tutorial\n", - "* The basics of the Quantum Approximate Optimization Algorithm (QAOA). See [Solve utility-scale quantum optimization problems](/docs/tutorials/quantum-approximate-optimization-algorithm) and this lesson on [Utility-scale QAOA](https://quantum.cloud.ibm.com/learning/en/courses/quantum-computing-in-practice/utility-scale-qaoa#utility-scale-qaoa) tutorial for an introduction to QAOA.\n", + "We suggest that users are familiar with the following topics before going through this tutorial.\n", + "* The basics of the Quantum Approximate Optimization Algorithm (QAOA). See [Solve utility-scale quantum optimization problems](/docs/tutorials/quantum-approximate-optimization-algorithm) and this lesson on [Utility-scale QAOA](/learning/courses/quantum-computing-in-practice/utility-scale-qaoa#utility-scale-qaoa) for an introduction to QAOA.\n", "\n", "After going through this tutorial, users should be able to:\n", "* Utilize advanced techniques to extract the best performance from large scale QAOA circuits\n", @@ -44,11 +44,11 @@ "\n", "The advanced techniques in this notebook include:\n", "\n", - "* **SWAP strategy with SAT initial mapping**: This is a specifically designed transpiler pass for QAOA that uses a SWAP strategy and a SAT solver together to improve the selection of which physical qubits on the QPU to use. The SWAP strategy exploits the commutativity of the QAOA operators to reorder gates so that layers of SWAP gates can be simultaneously executed, thus reducing the depth of the circuit [\\[1\\]](#references). The SAT solver is used to find an initial mapping that minimizes the number of SWAP operations needed to map the qubits in the circuit to the physical qubits on the device [\\[2\\]](#references) .\n", - "* **CVaR cost function**: Typically the expected value of the cost Hamiltonian is used as the cost function for QAOA, but as was shown in [\\[3\\]](#references) , focusing on the tail of the distribution, rather than the expected value, can improve the performance of QAOA for combinatorial optimization problems. The CVaR accomplishes this. For a given set of shots with corresponding objective values of the considered optimization problem, the Conditional Value at Risk (CVaR) with confidence level $\\alpha \\in [0, 1]$ is defined as the average of the $\\alpha$ best shots [\\[3\\]](#references).\n", + "* **SWAP strategy with SAT initial mapping**: This is a specifically designed transpiler pass for QAOA that uses a SWAP strategy and a SAT solver together to improve the selection of which physical qubits on the QPU to use. The SWAP strategy exploits the commutativity of the QAOA operators to reorder gates so that layers of SWAP gates can be simultaneously executed, thus reducing the depth of the circuit [\\[1\\]](#references). The SAT solver is used to find an initial mapping that minimizes the number of SWAP operations needed to map the qubits in the circuit to the physical qubits on the device [\\[2\\]](#references).\n", + "* **CVaR cost function**: Typically the expected value of the cost Hamiltonian is used as the cost function for QAOA, but as was shown in [\\[3\\]](#references), focusing on the tail of the distribution, rather than the expected value, can improve the performance of QAOA for combinatorial optimization problems. The CVaR accomplishes this. For a given set of shots with corresponding objective values of the considered optimization problem, the Conditional Value at Risk (CVaR) with confidence level $\\alpha \\in [0, 1]$ is defined as the average of the $\\alpha$ best shots [\\[3\\]](#references).\n", "Thus, $\\alpha = 1$ corresponds to the standard expected value, while $\\alpha=0$ corresponds to the minimum of the given shots, and $\\alpha \\in (0, 1)$ is a tradeoff between focusing on better shots, while still applying some averaging to smooth out the optimization landscape. Additionally, the CVaR can be used as an error mitigation technique to improve the quality of the objective value estimation [\\[4\\]](#references).\n", "\n", - "By the end of this tutorial, you should be able to leverage these techniques for getting the best results from running QAOA for your optimization problems." + "By the end of this tutorial, you should be able to use these techniques to get the best results from running QAOA for your optimization problems." ] }, { @@ -60,7 +60,7 @@ "\n", "Before starting this tutorial, be sure you have the following installed:\n", "\n", - "* Qiskit SDK v2.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", + "* Qiskit SDK v2.0 or later, with [visualization](/docs/api/qiskit/visualization) support\n", "* Qiskit Runtime v0.43 or later (`pip install qiskit-ibm-runtime`)\n", "* Rustworkx graph library (`pip install rustworkx`)\n", "* Python SAT (`pip install python-sat`)" From afa17fd9fceeccffc902b3d2bda6c7fd1f8717d9 Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Fri, 13 Mar 2026 13:28:08 -0400 Subject: [PATCH 28/28] finish copyediting --- .../advanced-techniques-for-qaoa.ipynb | 34 +++++++++---------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 8abbf078a19..bba16ad34db 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -126,7 +126,7 @@ "id": "663d13f5-a5f7-4b67-a89b-26deb41224ec", "metadata": {}, "source": [ - "## Small Scale Simulator Example\n", + "## Small-scale simulator example\n", "\n", "Since the goal of this tutorial is to show how QAOA performs at scales beyond what a simulator can handle, we will forgo this step." ] @@ -136,7 +136,7 @@ "id": "6ca05a7c-5fde-4485-a3ad-c0ba02844e50", "metadata": {}, "source": [ - "## Large Scale Hardware Example" + "## Large-scale hardware example" ] }, { @@ -144,11 +144,11 @@ "id": "b825afbf-10fd-4926-bd65-05272044f107", "metadata": {}, "source": [ - "## Step 1: Map classical inputs to a quantum problem\n", + "### Step 1: Map classical inputs to a quantum problem\n", "\n", - "### Graph → Hamiltonian\n", + "#### Graph → Hamiltonian\n", "\n", - "First, let's map the problem onto a quantum circuit that is suited for the QAOA. Details on this process can be found in the [introductory QAOA tutorial.](/docs/tutorials/quantum-approximate-optimization-algorithm)" + "First, let's map the problem onto a quantum circuit that is suited for the QAOA. Details on this process can be found in the [introductory QAOA tutorial](/docs/tutorials/quantum-approximate-optimization-algorithm)." ] }, { @@ -294,9 +294,9 @@ "id": "4e576068-53e7-4a06-a83b-87e95de141e9", "metadata": {}, "source": [ - "## Step 2: Optimize problem for quantum hardware execution\n", + "### Step 2: Optimize problem for quantum hardware execution\n", "\n", - "### SWAP strategy with the SAT initial mapping\n", + "#### SWAP strategy with the SAT initial mapping\n", "\n", "We will demonstrate how to build and optimize QAOA circuits using the **SWAP strategy with SAT initial mapping**, a specifically designed transpiler pass for QAOA applied to quadratic problems.\n", "\n", @@ -341,7 +341,7 @@ "source": [ "#### Remap the graph using a SAT mapper\n", "\n", - "Even when a circuit consists of commuting gates (this is the case for the QAOA circuit, but also for Trotterized simulations of Ising Hamiltonians), finding a good initial mapping is a challenging task. The SAT-based approach presented in [\\[2\\]](#references) enables the discovery of effective initial mappings for circuits with commuting gates, resulting in a significant reduction in the number of required SWAP layers. This approach has been demonstrated to scale to up to *500 qubits*, as illustrated in the paper.\n", + "Even when a circuit consists of commuting gates (this is the case for the QAOA circuit, but also for Trotterized simulations of Ising Hamiltonians), finding a good initial mapping is a challenging task. When we use the SAT-based approach presented in [\\[2\\]](#references), we can discover effective initial mappings for circuits with commuting gates, resulting in a significant reduction in the number of required SWAP layers. This approach has been demonstrated to scale to up to *500 qubits*, as illustrated in the paper.\n", "\n", "The following code demonstrates how to use the `SATMapper` from Matsuo et al. to remap the graph. This process allows the problem to be mapped to a more optimal initial state for a specified SWAP strategy, resulting in a significant reduction in the number of SWAP layers required to execute the circuit.\n", "\n", @@ -650,7 +650,7 @@ "We only want to apply the SWAP strategies to the cost operator layer, so we start by creating the isolated block that we will later transform and append to the final QAOA circuit.\n", "\n", "For this, we can use the [`QAOAAnsatz`](/docs/api/qiskit/qiskit.circuit.library.QAOAAnsatz) class from Qiskit. We input an empty circuit to the `initial_state` and `mixer_operator` fields to make sure we are building an isolated cost operator layer.\n", - "We also define the edge_coloring map so that RZZ gates are positioned next to SWAP gates. This strategic placement allows us to exploit CX cancellations, optimizing the circuit for better performance.\n", + "We also define the `edge_coloring` map so that RZZ gates are positioned next to SWAP gates. This strategic placement allows us to exploit CX cancellations, optimizing the circuit for better performance.\n", "This process is executed within the `create_qaoa_swap_circuit` function." ] }, @@ -889,9 +889,9 @@ "id": "e2afd1a7-0980-433b-a3a8-303d7e7718b1", "metadata": {}, "source": [ - "## Step 3: Execute using Qiskit IBM Runtime primitives\n", + "## Step 3: Execute using Qiskit Runtime primitives\n", "\n", - "Let's now prepare for hardware execution. Our first step will be to define a Conditional Value at Risk (CVaR) cost function which was introduced in [\\[3\\]](#references) for use within the paradigm of variational quantum optimization algorithms.\n", + "Let's now prepare for hardware execution. Our first step will be to define a Conditional Value at Risk (CVaR) cost function, which was introduced in [\\[3\\]](#references) for use within the paradigm of variational quantum optimization algorithms.\n", "\n", "The CVaR of a random variable $X$ for a confidence level $α ∈ (0, 1]$ is defined as\n", "$CVaR_{\\alpha}(X) = \\mathbb{E} \\lbrack X | X \\leq F_X^{-1}(\\alpha) \\rbrack$\n", @@ -1066,7 +1066,7 @@ "id": "63fa2ab4-5354-4022-ab46-e9bbf73870de", "metadata": {}, "source": [ - "The CVaR can be used as an error mitigation technique as previously discussed [\\[4\\]](#references). In this example, we determine $\\alpha$ and the number of shots according to the error per layered gate (EPLG) associated with the circuit." + "The CVaR can be used as an error mitigation technique as previously discussed [\\[4\\]](#references). In this example, we determine $\\alpha$ and the number of shots according to the [error per layered gate](/docs/guides/qpu-information#2q-error-layered) (EPLG) associated with the circuit." ] }, { @@ -1193,7 +1193,7 @@ "source": [ "## Step 4: Post-process and return result in desired classical format\n", "\n", - "Let's now visualize our results and then postprocess them to find the value of the cut" + "Let's now visualize our results and then post-process them to find the value of the cut." ] }, { @@ -1235,7 +1235,7 @@ "id": "38aadfcb-aec9-4dbb-a9d3-319239eae196", "metadata": {}, "source": [ - "The following retrieves the best solution from the sampled bitstrings" + "The following retrieves the best solution from the sampled bitstrings:" ] }, { @@ -1274,10 +1274,10 @@ "Consider the Hamiltonian $H_C$ for the **Max-Cut** problem. Let each vertex of the graph be associated with a qubit in state $|0\\rangle$ or $|1\\rangle$, where the value denotes the set the vertex is in. The goal of the problem is to maximize the number of edges $(v_1, v_2)$ for which $v_1 = |0\\rangle$ and $v_2 = |1\\rangle$, or vice versa. If we associate the $Z$ operator with each qubit, where\n", "\n", "$$\n", - " Z|0\\rangle = |0\\rangle \\qquad Z|1\\rangle = -|1\\rangle\n", + " Z|0\\rangle = |0\\rangle \\qquad Z|1\\rangle = -|1\\rangle,\n", "$$\n", "\n", - "then an edge $(v_1, v_2)$ belongs to the cut if the eigenvalue of $(Z_1|v_1\\rangle) \\cdot (Z_2|v_2\\rangle) = -1$; in other words, the qubits associated with $v_1$ and $v_2$ are different. Similarly, $(v_1, v_2)$ does not belong to the cut if the eigenvalue of $(Z_1|v_1\\rangle) \\cdot (Z_2|v_2\\rangle) = 1$" + "then an edge $(v_1, v_2)$ belongs to the cut if the eigenvalue of $(Z_1|v_1\\rangle) \\cdot (Z_2|v_2\\rangle) = -1$; in other words, the qubits associated with $v_1$ and $v_2$ are different. Similarly, $(v_1, v_2)$ does not belong to the cut if the eigenvalue of $(Z_1|v_1\\rangle) \\cdot (Z_2|v_2\\rangle) = 1$." ] }, { @@ -1398,7 +1398,7 @@ "\n", "If you found this work interesting you may be interested in the following material:\n", "\n", - "* [The intractable decathlon](https://arxiv.org/pdf/2504.03832): a listing of 10 optimization problems that are difficult for classical optimization algorithms which may be good use cases to test out the techniques introduced in this tutorial on.\n", + "* [The intractable decathlon](https://arxiv.org/pdf/2504.03832): a listing of 10 optimization problems that are difficult for classical optimization algorithms, and which may be good use cases to test the techniques introduced in this tutorial.\n", "* [A repo of best practices for quantum optimization](https://github.com/qiskit-community/qopt-best-practices) to further improve the results of your QAOA-based workflow." ] }