| # Attribution and provenance |
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| 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 |
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| 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. |
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| 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. |
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