Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
files: struct<development.tar.gz: struct<bytes: int64, expanded_bytes: int64, role: string, sha256: string> (... 197 chars omitted)
child 0, development.tar.gz: struct<bytes: int64, expanded_bytes: int64, role: string, sha256: string>
child 0, bytes: int64
child 1, expanded_bytes: int64
child 2, role: string
child 3, sha256: string
child 1, grader-labels.tar.gz: struct<bytes: int64, expanded_bytes: int64, role: string, sha256: string>
child 0, bytes: int64
child 1, expanded_bytes: int64
child 2, role: string
child 3, sha256: string
child 2, verifier-inputs.tar.gz: struct<bytes: int64, expanded_bytes: int64, role: string, sha256: string>
child 0, bytes: int64
child 1, expanded_bytes: int64
child 2, role: string
child 3, sha256: string
release: string
schema: string
condition: string
distance: int64
check_qubits: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
split: string
model_probability: null
basis: string
source_sha256: struct<circuit_ideal.stim: string, detection_events.b8: string, obs_flips_actual.01: string>
child 0, circuit_ideal.stim: string
child 1, detection_events.b8: string
child 2, obs_flips_actual.01: string
source_shot_index_zero_based: int64
selection: string
source_attribution: string
data_qubits: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
target_logical_flip: int64
source_license: string
output_note: string
rounds: int64
source_url: string
detectors: list<item: struct<id: int64, coords: list<item: double>, event: int64>>
child 0, item: struct<id: int64, coords: list<item: double>, event: int64>
child 0, id: int64
child 1, coords: list<item: double>
child 0, item: double
child 2, event: int64
to
{'schema': Value('string'), 'split': Value('string'), 'source_url': Value('string'), 'source_license': Value('string'), 'source_attribution': Value('string'), 'condition': Value('string'), 'source_shot_index_zero_based': Value('int64'), 'selection': Value('string'), 'source_sha256': {'circuit_ideal.stim': Value('string'), 'detection_events.b8': Value('string'), 'obs_flips_actual.01': Value('string')}, 'distance': Value('int64'), 'basis': Value('string'), 'rounds': Value('int64'), 'data_qubits': List(List(Value('float64'))), 'check_qubits': List(List(Value('float64'))), 'detectors': List({'id': Value('int64'), 'coords': List(Value('float64')), 'event': Value('int64')}), 'target_logical_flip': Value('int64'), 'model_probability': Value('null'), 'output_note': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
files: struct<development.tar.gz: struct<bytes: int64, expanded_bytes: int64, role: string, sha256: string> (... 197 chars omitted)
child 0, development.tar.gz: struct<bytes: int64, expanded_bytes: int64, role: string, sha256: string>
child 0, bytes: int64
child 1, expanded_bytes: int64
child 2, role: string
child 3, sha256: string
child 1, grader-labels.tar.gz: struct<bytes: int64, expanded_bytes: int64, role: string, sha256: string>
child 0, bytes: int64
child 1, expanded_bytes: int64
child 2, role: string
child 3, sha256: string
child 2, verifier-inputs.tar.gz: struct<bytes: int64, expanded_bytes: int64, role: string, sha256: string>
child 0, bytes: int64
child 1, expanded_bytes: int64
child 2, role: string
child 3, sha256: string
release: string
schema: string
condition: string
distance: int64
check_qubits: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
split: string
model_probability: null
basis: string
source_sha256: struct<circuit_ideal.stim: string, detection_events.b8: string, obs_flips_actual.01: string>
child 0, circuit_ideal.stim: string
child 1, detection_events.b8: string
child 2, obs_flips_actual.01: string
source_shot_index_zero_based: int64
selection: string
source_attribution: string
data_qubits: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
target_logical_flip: int64
source_license: string
output_note: string
rounds: int64
source_url: string
detectors: list<item: struct<id: int64, coords: list<item: double>, event: int64>>
child 0, item: struct<id: int64, coords: list<item: double>, event: int64>
child 0, id: int64
child 1, coords: list<item: double>
child 0, item: double
child 2, event: int64
to
{'schema': Value('string'), 'split': Value('string'), 'source_url': Value('string'), 'source_license': Value('string'), 'source_attribution': Value('string'), 'condition': Value('string'), 'source_shot_index_zero_based': Value('int64'), 'selection': Value('string'), 'source_sha256': {'circuit_ideal.stim': Value('string'), 'detection_events.b8': Value('string'), 'obs_flips_actual.01': Value('string')}, 'distance': Value('int64'), 'basis': Value('string'), 'rounds': Value('int64'), 'data_qubits': List(List(Value('float64'))), 'check_qubits': List(List(Value('float64'))), 'detectors': List({'id': Value('int64'), 'coords': List(Value('float64')), 'event': Value('int64')}), 'target_logical_flip': Value('int64'), 'model_probability': Value('null'), 'output_note': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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 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
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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