Open QEC harness – greedy vs. GE, uniform vs. clustered k=4

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GitHub - mrblakessinger-rgb/qec-evaluation-suite: Diagnostic wind tunnel for surface, Floquet, and HaPPY QEC — finite-size telemetry, honest non-claims. · GitHub

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QEC Evaluation Suite

Finite-size diagnostic tooling for circuit-level and holographic-style quantum error correction.

Digital evaluation / telemetry only — not hardware, not a production decoder, not an asymptotic threshold paper.

Layer C framing: tensor-network model of bulk-boundary encoding with greedy recovery (HaPPY); optional matched-sample GF(2) GE ablation.

Claims detail: QEC_CLAIMS.md · Prefer JSON under public/data/ if anything disagrees with this README.

Layers

Layer<br>What it is<br>Primary artifacts

A Surface<br>Stim rotated surface; graphlike DEM; MWPM smoke<br>public/data/layer_a_surface_smoke.json

A Floquet<br>Period-3 honeycomb; schedule-valid; not graphlike MWPM peer of surface<br>public/data/layer_a_floquet_smoke.json

B Geometry<br>Educational graph invariants — not circuit-level QEC<br>public/data/geometry_lab_metrics.json

C HaPPY<br>Depth-1 (n=10, k=6) & capped depth-2 (n=20, k=16); greedy + matched GE<br>public/data/happy_*.json

Headline results (reproducible JSON)

Matched greedy vs GE (same erasure masks)

Depth<br>p_erase<br>Greedy central<br>GE central

0.3<br>0.664<br>0.304

0.3<br>0.59<br>0.0575

Non-dominance: GE is not a recovery superset of greedy on this instance. Gap widens at depth-2 under the linear face-parameter model.

Sources: happy_ge_ablation_metrics.json, happy_ge_ablation_metrics_depth2.json (seed=42).

Pattern stress × GE (depth-1, k-matched)

mode<br>Greedy central<br>GE central

uniform<br>~0.27<br>~0.02

clustered<br>1.0<br>0.0

k=4 stays special for greedy (clustered holds, uniform drops). Under GE both modes sit near the floor. Decoder × pattern, not a universal geometry claim.

Source: happy_ge_pattern_stress.json (400 trials/cell, seed=42).

Quickstart

git clone https://github.com/mrblakessinger-rgb/qec-evaluation-suite.git<br>cd qec-evaluation-suite<br>python -m qec_engine.layer_a_smoke<br>python -m qec_engine.repo_rollup

Prefer frozen JSON under public/data/ over re-running heavy sweeps when comparing claims. Full honesty language and non-claims: QEC_CLAIMS.md.

About<br>Diagnostic wind tunnel for surface, Floquet, and HaPPY QEC — finite-size telemetry, honest non-claims.<br>Resources<br>Readme<br>Activity<br>Stars<br>0 stars<br>Watchers<br>0 watching<br>Forks<br>0 forks<br>Report repository

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