Gottesman-Software · DennisWayo · Sep 17, 2026 · Sep 17, 2026
diff --git a/examples/paper_runs/paper_05/README.md b/examples/paper_runs/paper_05/README.md
@@ -4,8 +4,8 @@ This workflow demonstrates syndrome extraction and decoder replay for four
 branches:
 
 - repetition-code circuits;
-- compact CSS-LDPC/qLDPC-style Steane Z-check circuits;
-- distance-5 surface-code Z-check circuits;
+- compact CSS/Steane Z-check circuits;
+- \(5\times5\) planar surface-check Z-layer circuits;
 - a PennyLane-backed digitized-GKP companion branch.
 
 The first three branches can run locally and, when credentials are configured,
@@ -17,9 +17,21 @@ Z-check bits. It tests the LiDMaS+ request interface for GKP-derived digitized
 syndromes without claiming physical GKP execution.
 
 Each decode stage replays every extracted syndrome stream through three
-policies: MWPM/minimum-weight, UF erasure peeling, and hard-decision BP/min-sum.
-The manuscript plots default to the MWPM baseline; the UF and BP rows are
-written into the generated decoder response files and `decoded_shots.csv`.
+policies: exact binary minimum weight (`emw`), a union-find-like greedy
+heuristic (`uf_like`), and a hard-decision bit-flip heuristic (`bit_flip`).
+The code also accepts historical aliases (`mwpm`, `uf`, and `bp`) so older
+generated filenames remain readable; these aliases do not imply that a matching
+graph or full BP/min-sum update was used. The manuscript plots default to the
+EMW baseline, while the heuristic rows are written into the generated decoder
+response files and `decoded_shots.csv`.
+
+## Revision evidence
+
+The `revision_evidence/` directory contains the fixed reviewer-facing artifacts
+cited by the FCS electronic supplement: `mapping_validation.csv`,
+`decoder_closure_audit.csv`, `support_metrics.csv`, `gkp_sensitivity.csv`, the
+surface-check matrix/support exports, the hardware data-register audit, and the
+revision validation figure.
 
 Run the local workflow:
 
@@ -74,7 +86,7 @@ Once the status is complete, run:
 ./examples/paper_runs/paper_05/06_analyze_and_plot.sh
 ```
 
-Run the compact qLDPC-style CSS-LDPC path:
+Run the compact CSS/Steane path:
 
 ```bash
 LIDMAS_P5_QLDPC_TARGETS=all \
@@ -86,11 +98,13 @@ LIDMAS_P5_QLDPC_SHOTS=4096 \
 ./examples/paper_runs/paper_05/12_run_qldpc_local_simulation.sh
 ```
 
-The qLDPC path uses the Steane CSS parity-check matrix as a compact
-LDPC-style hardware surrogate. It measures the Z-check half for clean and
+The compact CSS path uses the Steane CSS parity-check matrix as a small
+hardware-facing interface check. It measures the Z-check half for clean and
 single-X injected data-qubit circuits. This is suitable for live syndrome
-extraction and correction-localization tests, but it should be described as a
-small CSS-LDPC demonstration rather than a large asymptotic qLDPC memory.
+extraction and correction-localization tests, but it should not be described as
+a large asymptotic qLDPC memory. The `LIDMAS_P5_QLDPC_*` environment-variable
+prefix is retained only as a historical compatibility alias for this compact
+CSS/Steane branch.
 
 Submit and fetch the matching IBM Runtime job:
 
@@ -109,7 +123,7 @@ LIDMAS_P5_QLDPC_IBM_STATUS_ONLY=1 \
 ./examples/paper_runs/paper_05/16_analyze_qldpc.sh
 ```
 
-Run the distance-5 surface-code Z-check path:
+Run the \(5\times5\) planar surface-check Z-layer path:
 
 ```bash
 LIDMAS_P5_SURFACE_DISTANCE=5 \
@@ -122,9 +136,11 @@ LIDMAS_P5_SURFACE_SHOTS=4096 \
 ./examples/paper_runs/paper_05/22_run_surface_local_simulation.sh
 ```
 
-The surface path measures only the Z-check half for injected-X correction. The
-default representative target set avoids running all 40 single-data-qubit
-injections while still using a distance-5, 56-active-qubit circuit.
+The surface-check path measures only the Z-check half for injected-X
+correction. The default representative target set avoids running all 40
+single-data-qubit injections while still using a \(5\times5\), 56-active-qubit
+circuit. It is an interface audit, not a demonstration of an encoded logical
+qubit or code distance.
 
 Submit and fetch the matching IBM Runtime job:
 

diff --git a/examples/paper_runs/paper_05/revision_evidence/decoder_closure_audit.csv b/examples/paper_runs/paper_05/revision_evidence/decoder_closure_audit.csv
@@ -0,0 +1,22 @@
+study,source,policy,rows,native_nonzero_residual_rate,mean_native_residual_weight,closure_invocation_rate,mean_closure_weight_all_rows,mean_closure_weight_when_invoked,post_closure_nonzero_residual_rate
+repetition,IBM,exact_binary_minimum_weight,24576,0.0,0.0,0.0,0.0,0.0,0.0
+repetition,IBM,union_find_heuristic,24576,0.0579833984375,0.0579833984375,0.0579833984375,0.115966796875,2.0,0.0
+repetition,IBM,hard_decision_bit_flip,24576,0.0579833984375,0.0579833984375,0.0579833984375,0.115966796875,2.0,0.0
+repetition,noisy_local,exact_binary_minimum_weight,24576,0.0,0.0,0.0,0.0,0.0,0.0
+repetition,noisy_local,union_find_heuristic,24576,0.034138997395833336,0.034138997395833336,0.034138997395833336,0.06827799479166667,2.0,0.0
+repetition,noisy_local,hard_decision_bit_flip,24576,0.034138997395833336,0.034138997395833336,0.034138997395833336,0.06827799479166667,2.0,0.0
+compact_css_steane,IBM,exact_binary_minimum_weight,32768,0.0,0.0,0.0,0.0,0.0,0.0
+compact_css_steane,IBM,union_find_heuristic,32768,0.0,0.0,0.0,0.0,0.0,0.0
+compact_css_steane,IBM,hard_decision_bit_flip,32768,0.0,0.0,0.0,0.0,0.0,0.0
+compact_css_steane,noisy_local,exact_binary_minimum_weight,32768,0.0,0.0,0.0,0.0,0.0,0.0
+compact_css_steane,noisy_local,union_find_heuristic,32768,0.0,0.0,0.0,0.0,0.0,0.0
+compact_css_steane,noisy_local,hard_decision_bit_flip,32768,0.0,0.0,0.0,0.0,0.0,0.0
+surface_d5,IBM,exact_binary_minimum_weight,36864,0.0,0.0,0.0,0.0,0.0,0.0
+surface_d5,IBM,union_find_heuristic,36864,0.4156358506944444,0.4451768663194444,0.4156358506944444,0.8312717013888888,2.0,0.0
+surface_d5,IBM,hard_decision_bit_flip,36864,0.4156358506944444,0.4451768663194444,0.4156358506944444,0.8312717013888888,2.0,0.0
+surface_d5,noisy_local,exact_binary_minimum_weight,36864,0.0,0.0,0.0,0.0,0.0,0.0
+surface_d5,noisy_local,union_find_heuristic,36864,0.0683865017361111,0.06884765625,0.0683865017361111,0.1367730034722222,2.0,0.0
+surface_d5,noisy_local,hard_decision_bit_flip,36864,0.0683865017361111,0.06884765625,0.0683865017361111,0.1367730034722222,2.0,0.0
+digitized_gkp_proxy,PennyLane_proxy,exact_binary_minimum_weight,36864,0.0,0.0,0.0,0.0,0.0,0.0
+digitized_gkp_proxy,PennyLane_proxy,union_find_heuristic,36864,0.1623263888888889,0.16422526041666666,0.1623263888888889,0.3246527777777778,2.0,0.0
+digitized_gkp_proxy,PennyLane_proxy,hard_decision_bit_flip,36864,0.1623263888888889,0.16422526041666666,0.1623263888888889,0.3246527777777778,2.0,0.0
diff --git a/examples/paper_runs/paper_05/revision_evidence/figure_revision_validation.pdf b/examples/paper_runs/paper_05/revision_evidence/figure_revision_validation.pdf
diff --git a/examples/paper_runs/paper_05/revision_evidence/figure_revision_validation.png b/examples/paper_runs/paper_05/revision_evidence/figure_revision_validation.png
diff --git a/examples/paper_runs/paper_05/revision_evidence/gkp_sensitivity.csv b/examples/paper_runs/paper_05/revision_evidence/gkp_sensitivity.csv
@@ -0,0 +1,14 @@
+scenario,varied_parameter,varied_value,sigma_shift_scale,measurement_error_rate,jump_prob,decision_width_scale,squeeze_r,injected_shift_scale,shots_per_stream,clean_zero_correction_rate,exact_rate_min,exact_rate_max,contains_rate_min,contains_rate_max,mean_correction_weight
+baseline,baseline,,0.015,0.01,0.001,0.25,2.0,0.56,4096,0.769775390625,0.34521484375,0.498046875,0.417724609375,0.610107421875,1.0570475260416667
+$\sigma_s/\sqrt{\pi}$=0.01,sigma_shift_scale,0.01,0.01,0.01,0.001,0.25,2.0,0.56,4096,0.7783203125,0.35400390625,0.50244140625,0.4169921875,0.6171875,1.0509982638888888
+$\sigma_s/\sqrt{\pi}$=0.025,sigma_shift_scale,0.025,0.025,0.01,0.001,0.25,2.0,0.56,4096,0.7431640625,0.32958984375,0.470458984375,0.400146484375,0.599853515625,1.0806477864583333
+$p_m$=0,measurement_error_rate,0.0,0.015,0.0,0.001,0.25,2.0,0.56,4096,0.8984375,0.40625,0.583251953125,0.437744140625,0.633056640625,0.9198404947916666
+$p_m$=0.03,measurement_error_rate,0.03,0.015,0.03,0.001,0.25,2.0,0.56,4096,0.55908203125,0.256103515625,0.370361328125,0.369140625,0.5625,1.3114963107638888
+$p_j$=0,jump_prob,0.0,0.015,0.01,0.0,0.25,2.0,0.56,4096,0.83447265625,0.373779296875,0.53759765625,0.42333984375,0.62744140625,0.9974772135416666
+$p_j$=0.005,jump_prob,0.005,0.015,0.01,0.005,0.25,2.0,0.56,4096,0.5732421875,0.253173828125,0.380859375,0.3955078125,0.573974609375,1.2869737413194444
+$w/\sqrt{\pi}$=0.2,decision_width_scale,0.2,0.015,0.01,0.001,0.2,2.0,0.56,4096,0.629150390625,0.45849609375,0.52587890625,0.632568359375,0.7646484375,1.2984212239583333
+$w/\sqrt{\pi}$=0.3,decision_width_scale,0.3,0.015,0.01,0.001,0.3,2.0,0.56,4096,0.81494140625,0.15966796875,0.32861328125,0.192626953125,0.38623046875,0.8158094618055556
+$r$=1.5,squeeze_r,1.5,0.015,0.01,0.001,0.25,1.5,0.56,4096,0.35400390625,0.14990234375,0.221435546875,0.3095703125,0.479248046875,1.5906032986111112
+$r$=2.5,squeeze_r,2.5,0.015,0.01,0.001,0.25,2.5,0.56,4096,0.790771484375,0.422607421875,0.564453125,0.498046875,0.67919921875,1.0693901909722223
+$\delta/\sqrt{\pi}$=0.5,injected_shift_scale,0.5,0.015,0.01,0.001,0.25,2.0,0.5,4096,0.769775390625,0.2236328125,0.38037109375,0.2734375,0.48046875,0.952880859375
+$\delta/\sqrt{\pi}$=0.62,injected_shift_scale,0.62,0.015,0.01,0.001,0.25,2.0,0.62,4096,0.769775390625,0.47509765625,0.587890625,0.57470703125,0.727783203125,1.1149359809027777
diff --git a/examples/paper_runs/paper_05/revision_evidence/hardware_data_measurement_mapping_audit.csv b/examples/paper_runs/paper_05/revision_evidence/hardware_data_measurement_mapping_audit.csv
@@ -0,0 +1,9 @@
+target,shots,target_data_bit_activation_rate,mean_non_target_data_bit_activation_rate,max_non_target_data_bit_activation_rate,target_activation_rank
+1,4096,0.870849609375,0.04125350560897436,0.129638671875,1
+5,4096,0.82958984375,0.08186848958333333,0.66162109375,1
+10,4096,0.823486328125,0.08041616586538461,0.7431640625,1
+14,4096,0.832763671875,0.04301257011217949,0.112548828125,1
+17,4096,0.738037109375,0.06620592948717949,0.650390625,1
+22,4096,0.917724609375,0.045741937099358976,0.117919921875,1
+32,4096,0.8193359375,0.0808856670673077,0.7421875,1
+37,4096,0.7314453125,0.06696339142628205,0.679443359375,1
diff --git a/examples/paper_runs/paper_05/revision_evidence/mapping_validation.csv b/examples/paper_runs/paper_05/revision_evidence/mapping_validation.csv
@@ -0,0 +1,41 @@
+target,expected_syndrome,simulated_syndrome,mapping_pass,reversed_bit_syndrome,negative_control_detected,data_measurement_roundtrip_pass
+0,1000000000000000,1000000000000000,1,0000000000000001,1,1
+1,0100000000000000,0100000000000000,1,0000000000000010,1,1
+2,0010000000000000,0010000000000000,1,0000000000000100,1,1
+3,0001000000000000,0001000000000000,1,0000000000001000,1,1
+4,1000100000000000,1000100000000000,1,0000000000010001,1,1
+5,0100010000000000,0100010000000000,1,0000000000100010,1,1
+6,0010001000000000,0010001000000000,1,0000000001000100,1,1
+7,0001000100000000,0001000100000000,1,0000000010001000,1,1
+8,0000100010000000,0000100010000000,1,0000000100010000,1,1
+9,0000010001000000,0000010001000000,1,0000001000100000,1,1
+10,0000001000100000,0000001000100000,1,0000010001000000,1,1
+11,0000000100010000,0000000100010000,1,0000100010000000,1,1
+12,0000000010001000,0000000010001000,1,0001000100000000,1,1
+13,0000000001000100,0000000001000100,1,0010001000000000,1,1
+14,0000000000100010,0000000000100010,1,0100010000000000,1,1
+15,0000000000010001,0000000000010001,1,1000100000000000,1,1
+16,0000000000001000,0000000000001000,1,0001000000000000,1,1
+17,0000000000000100,0000000000000100,1,0010000000000000,1,1
+18,0000000000000010,0000000000000010,1,0100000000000000,1,1
+19,0000000000000001,0000000000000001,1,1000000000000000,1,1
+20,1000000000000000,1000000000000000,1,0000000000000001,1,1
+21,1100000000000000,1100000000000000,1,0000000000000011,1,1
+22,0110000000000000,0110000000000000,1,0000000000000110,1,1
+23,0011000000000000,0011000000000000,1,0000000000001100,1,1
+24,0001000000000000,0001000000000000,1,0000000000001000,1,1
+25,0000100000000000,0000100000000000,1,0000000000010000,1,1
+26,0000110000000000,0000110000000000,1,0000000000110000,1,1
+27,0000011000000000,0000011000000000,1,0000000001100000,1,1
+28,0000001100000000,0000001100000000,1,0000000011000000,1,1
+29,0000000100000000,0000000100000000,1,0000000010000000,1,1
+30,0000000010000000,0000000010000000,1,0000000100000000,1,1
+31,0000000011000000,0000000011000000,1,0000001100000000,1,1
+32,0000000001100000,0000000001100000,1,0000011000000000,1,1
+33,0000000000110000,0000000000110000,1,0000110000000000,1,1
+34,0000000000010000,0000000000010000,1,0000100000000000,1,1
+35,0000000000001000,0000000000001000,1,0001000000000000,1,1
+36,0000000000001100,0000000000001100,1,0011000000000000,1,1
+37,0000000000000110,0000000000000110,1,0110000000000000,1,1
+38,0000000000000011,0000000000000011,1,1100000000000000,1,1
+39,0000000000000001,0000000000000001,1,1000000000000000,1,1
diff --git a/examples/paper_runs/paper_05/revision_evidence/revision_evidence.json b/examples/paper_runs/paper_05/revision_evidence/revision_evidence.json
@@ -0,0 +1,16 @@
+{
+  "mapping": {
+    "targets": 40,
+    "noiseless_mapping_passes": 40,
+    "reversed_bit_negative_control_detections": 40,
+    "data_measurement_roundtrip_passes": 40
+  },
+  "decoder_audit_rows": 21,
+  "hardware_data_mapping_rows": 8,
+  "support_metric_rows": 48,
+  "gkp_sensitivity_scenarios": 13,
+  "software": {
+    "python": "3.14.0",
+    "numpy": "2.4.2"
+  }
+}
diff --git a/examples/paper_runs/paper_05/revision_evidence/support_metrics.csv b/examples/paper_runs/paper_05/revision_evidence/support_metrics.csv
@@ -0,0 +1,49 @@
+study,source,target,shots,exact_localization_rate,target_recall_rate,mean_false_positive_support,mean_support_precision,mean_jaccard_singleton_truth,mean_correction_weight
+compact_css_steane,ibm_ibm_fez,0,4096,0.843017578125,0.843017578125,0.151611328125,0.843017578125,0.843017578125,0.99462890625
+compact_css_steane,ibm_ibm_fez,1,4096,0.848388671875,0.848388671875,0.13916015625,0.848388671875,0.848388671875,0.987548828125
+compact_css_steane,ibm_ibm_fez,2,4096,0.854248046875,0.854248046875,0.133544921875,0.854248046875,0.854248046875,0.98779296875
+compact_css_steane,ibm_ibm_fez,3,4096,0.857666015625,0.857666015625,0.12841796875,0.857666015625,0.857666015625,0.986083984375
+compact_css_steane,ibm_ibm_fez,4,4096,0.852783203125,0.852783203125,0.10009765625,0.852783203125,0.852783203125,0.952880859375
+compact_css_steane,ibm_ibm_fez,5,4096,0.85546875,0.85546875,0.095458984375,0.85546875,0.85546875,0.950927734375
+compact_css_steane,ibm_ibm_fez,6,4096,0.8447265625,0.8447265625,0.06689453125,0.8447265625,0.8447265625,0.91162109375
+compact_css_steane,local_simulator,0,4096,0.93798828125,0.93798828125,0.06201171875,0.93798828125,0.93798828125,1.0
+compact_css_steane,local_simulator,1,4096,0.943603515625,0.943603515625,0.055908203125,0.943603515625,0.943603515625,0.99951171875
+compact_css_steane,local_simulator,2,4096,0.94287109375,0.94287109375,0.05712890625,0.94287109375,0.94287109375,1.0
+compact_css_steane,local_simulator,3,4096,0.93896484375,0.93896484375,0.06103515625,0.93896484375,0.93896484375,1.0
+compact_css_steane,local_simulator,4,4096,0.94384765625,0.94384765625,0.035888671875,0.94384765625,0.94384765625,0.979736328125
+compact_css_steane,local_simulator,5,4096,0.941650390625,0.941650390625,0.038330078125,0.941650390625,0.941650390625,0.97998046875
+compact_css_steane,local_simulator,6,4096,0.94189453125,0.94189453125,0.040283203125,0.94189453125,0.94189453125,0.982177734375
+digitized_gkp_proxy,digitized_gkp_pennylane,1,4096,0.49462890625,0.599365234375,0.291015625,0.5445760091145833,0.5445760091145833,0.890380859375
+digitized_gkp_proxy,digitized_gkp_pennylane,5,4096,0.353515625,0.601318359375,0.645751953125,0.4742024739583333,0.4742024739583333,1.2470703125
+digitized_gkp_proxy,digitized_gkp_pennylane,10,4096,0.350341796875,0.450927734375,0.943359375,0.39837646484375,0.39837646484375,1.394287109375
+digitized_gkp_proxy,digitized_gkp_pennylane,14,4096,0.353515625,0.60791015625,0.632568359375,0.47721354166666663,0.47721354166666663,1.240478515625
+digitized_gkp_proxy,digitized_gkp_pennylane,17,4096,0.486572265625,0.5751953125,0.28857421875,0.5295003255208333,0.5295003255208333,0.86376953125
+digitized_gkp_proxy,digitized_gkp_pennylane,22,4096,0.35400390625,0.4169921875,0.651123046875,0.3848063151041667,0.3848063151041667,1.068115234375
+digitized_gkp_proxy,digitized_gkp_pennylane,32,4096,0.363037109375,0.463134765625,0.930908203125,0.409423828125,0.409423828125,1.39404296875
+digitized_gkp_proxy,digitized_gkp_pennylane,37,4096,0.350341796875,0.428955078125,0.65478515625,0.3871256510416667,0.3871256510416667,1.083740234375
+repetition,ibm_ibm_fez,0,4096,0.868408203125,0.9033203125,0.114990234375,0.8858642578125,0.8858642578125,1.018310546875
+repetition,ibm_ibm_fez,1,4096,0.8427734375,0.919921875,0.1787109375,0.88134765625,0.88134765625,1.0986328125
+repetition,ibm_ibm_fez,2,4096,0.861083984375,0.92333984375,0.1943359375,0.8922119140625,0.8922119140625,1.11767578125
+repetition,ibm_ibm_fez,3,4096,0.820556640625,0.895263671875,0.21044921875,0.85791015625,0.85791015625,1.105712890625
+repetition,ibm_ibm_fez,4,4096,0.8291015625,0.876220703125,0.180908203125,0.8526611328125,0.8526611328125,1.05712890625
+repetition,local_simulator,0,4096,0.91015625,0.93310546875,0.08935546875,0.921630859375,0.921630859375,1.0224609375
+repetition,local_simulator,1,4096,0.921630859375,0.958251953125,0.098876953125,0.93994140625,0.93994140625,1.05712890625
+repetition,local_simulator,2,4096,0.926513671875,0.961181640625,0.110107421875,0.94384765625,0.94384765625,1.0712890625
+repetition,local_simulator,3,4096,0.916259765625,0.958251953125,0.10498046875,0.937255859375,0.937255859375,1.063232421875
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