| --- |
| language: |
| - en |
| license: cc-by-4.0 |
| pretty_name: Sycamore Surface-code Decoding — AI4Sci Materialized Release |
| size_categories: |
| - 1M<n<10M |
| task_categories: |
| - tabular-classification |
| tags: |
| - quantum-error-correction |
| - surface-code |
| - sycamore |
| - alphaqubit |
| - ai4science |
| --- |
| |
| # Sycamore surface-code decoding: materialized benchmark |
|
|
| Predict a logical observable flip from repeated stabilizer detection events in |
| a noisy quantum memory. The benchmark trains decoders that improve the |
| reliability of encoded quantum information. It uses real Sycamore hard-readout |
| experiments at code distances 3 and 5, not simulated soft-readout d11 data. |
|
|
|  |
|
|
| Source: [Google Quantum AI Sycamore memory experiments, Zenodo 6804040](https://zenodo.org/records/6804040), |
| CC-BY-4.0. Scientific model reference: |
| [Bausch et al., Learning high-accuracy error decoding for quantum processors, |
| Nature 635, 834–840 (2024)](https://www.nature.com/articles/s41586-024-08148-8). |
| The bundled AlphaQubit implementation and trained weights are independent |
| reproductions; they are not official Google weights and do not reproduce the |
| paper's headline ensemble accuracy. |
|
|
| ## Contents and splits |
|
|
| - `development.tar.gz`: physically separate train and validation arrays, |
| ideal circuits, published even-fitted detector error models, and the ten |
| authorized finetuned AlphaQubit warm-start specialists. |
| - `verifier-inputs.tar.gz`: input-only test arrays and ideal circuits. No logical |
| labels, raw measurement records, historical decoder predictions, or odd-fit |
| noise models are in this archive. |
| - `release-manifest.json`: archive sizes, expanded sizes and SHA-256 identities. |
| - `NOTICE.md`: attribution and source/licensing notes. |
|
|
| Each of 130 conditions contains 50,000 original shots: four d3 patches and one |
| d5 patch, X/Z bases, and 13 odd round counts 1..25. Original zero-based rows are |
| split into first 19,880 even rows for train, last 5,120 even rows for validation, |
| and all 25,000 odd rows for test. Totals are 2,584,400 / 665,600 / 3,250,000. |
| There is no geometry or acquisition-session holdout. IDs are opaque uint64 |
| values; the inference interface does not expose original source row numbers. |
|
|
| Training/validation NPZs contain `sample_ids:uint64[N]`, packed |
| `detectors:uint8[N,ceil(D/8)]` (little-endian bit order), and `labels:uint8[N]`. |
| Verifier inputs contain only the first two. A split manifest specifies geometry, |
| array paths, ideal circuits, shot counts and hashes. Targets are the official |
| logical-path observable flips, not raw final-data-qubit parity. Noise DEMs are |
| calibrated using all even syndromes, including validation syndromes, but no |
| logical labels or odd test data. This is a declared calibration exception. |
|
|
| The original dataset and historical test are public. A separate private |
| `Corning/ai4sci-surface-code-decoding-evaluation` repository supplies labels to |
| trusted benchmark operators; this runtime separation does not make the source |
| historically secret. Never expose test roles to an agent's development runtime. |
|
|
| ## Model and score |
|
|
| The supplied model has a recurrent Transformer core with width 320, three |
| blocks per round, four attention heads, and geometry/event-dependent bias. |
| Eight d3 specialists have 8,449,990 parameters each; two d5 specialists have |
| 8,456,070 each; total 84,512,060. Exactly one specialist serves each shot. |
| Weights are plain FP32 NumPy arrays, with BF16 compute in the frozen configuration. |
|
|
| Overall P = 1 minus balanced logical failure: average durations in each |
| geometry/basis group, groups within distance, and give d3/d5 equal weight. |
| Normalized score = clip((P-L)/(U-L),0,1), with fixed L=0.7160463461538461 from |
| MWPM and U=0.7615159615384615 from the strongest same-test released |
| tensor-network predictions. TN is a rescored historical reference, not a |
| newly reimplemented decoder. The frozen neural starter gives P=0.7550607692307693 |
| and normalized score=0.8580328368056459. Raw/unclipped scores and scientific LER, |
| error suppression and calibration metrics remain available. |
|
|
| The original accepted neural training/replay cost was about 200.3 allocated |
| GPU-hours on RTX PRO 6000 Blackwell Max-Q 96 GB hardware; this is not an H100 |
| throughput claim. The agent task uses a smaller warm-start budget (2 H100 ×24h). |
|
|
| ## Reproduce |
|
|
| See the [GitHub task](https://github.com/T0-RSI/ai4sci-tasks/tree/codex/surface-code-baseline/surface-code-decoding) |
| for `environment/data/materialize.py`, the immutable HF lock, Harbor environments, |
| strict submission schema and trusted verifier. Normal deployment downloads |
| these pre-materialized archives; it does not regenerate experimental data. |
| Use the full commit revision and archive hashes pinned in GitHub. Agent data, |
| inference inputs and trusted labels must be mounted separately. The verifier |
| runs without network, replays a relocated trained model, seals outputs, and |
| scores them in a separate label-holding service. |
|
|
| Do not compare the normalized scalar as a universal measure of scientific |
| difficulty across tasks. It measures progress between declared task-specific |
| references and saturates beyond the upper reference; raw metrics preserve |
| further progress. The scientific evaluation is limited to this historical |
| hard-readout d3/d5 distribution. |
|
|
