Datasets:

sunweiwei
/

ai4sci-surface-code-decoding

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 80, in _split_generators
                  raise ValueError(
                  ...<2 lines>...
                  )
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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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, CC-BY-4.0. Scientific model reference: Bausch et al., Learning high-accuracy error decoding for quantum processors, Nature 635, 834–840 (2024). 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 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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