NOTICE.md

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.