Sycamore surface-code decoding release v1.1 (development without trained weights)

NOTICE.md

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+# Attribution and provenance
+
+Experimental measurements, ideal circuit descriptions and published calibrated
+detector error models derive from Google Quantum AI's Sycamore surface-code
+memory experiments, Zenodo record 6804040:
+https://zenodo.org/records/6804040
+
+The source data is distributed under Creative Commons Attribution 4.0
+International (CC-BY-4.0): https://creativecommons.org/licenses/by/4.0/
+Changes: the benchmark selects surface-code d3/d5 experiments, partitions
+source rows into train/validation/test, replaces row indices with stable opaque
+identifiers, converts detector/label arrays to NumPy NPZ, and separates
+inference inputs from grader labels. Original data provenance is preserved in
+release metadata and content hashes. The release does not alter the experiment.
+
+The scientific AlphaQubit reference is Bausch et al., “Learning high-accuracy
+error decoding for quantum processors,” Nature 635, 834–840 (2024),
+https://doi.org/10.1038/s41586-024-08148-8 . The bundled decoder implementation
+and weights are independent benchmark reproductions, not official Google code
+or weights. Code/dependency notices and licenses remain applicable; this dataset
+card's CC-BY-4.0 metadata describes the experimental data and does not relicense
+third-party libraries. Consult the linked code repository and retained upstream
+notices for software licensing. No endorsement by Google Quantum AI or Google
+DeepMind is implied.

README.md

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+---
+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.
+
+![Scientific problem, real validation example and output](science-introduction.png)
+
+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 reference AlphaQubit implementation is an independent reproduction, not
+official Google code or weights, and does not reproduce the paper's headline
+ensemble accuracy. Its trained weights are not distributed to agents.
+
+## Contents and splits
+
+- `development.tar.gz`: physically separate train and validation arrays,
+  ideal circuits, published even-fitted detector error models, and `reference/`:
+  the reference model's validation predictions, validation metrics, aggregate test
+  score and training cost, plus a submission template without weights.
+- `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 reference 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 reference AlphaQubit 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. Agents train from scratch; the agent budget is under review.
+
+## Reproduce
+
+See the [GitHub task](https://github.com/T0-RSI/ai4sci-tasks/tree/main/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.

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