surface-code
willow-surface-code-detection-events
Willow Surface-Code Detection Events (ingested)
Detection events and logical-observable flips derived from Google's Willow
below-threshold surface-code dataset (Zenodo
10.5281/zenodo.13273331), rotated
surface code at distances 3, 5, 7 in X and Z memory.
Each row is one experimental shot. Detection events are derived from the raw
device measurement records with Stim's measurement-to-detector converter, using
the per-shot sweep bits, and validated to reproduce the dataset's… See the full description on the dataset page: https://huggingface.co/datasets/ShayManor/willow-surface-code-detection-events.surface-code-syndromes
Surface code syndromes, ML-ready
Real quantum error correction data from Google Quantum AI's Sycamore processor, reshaped so
you can train a model on it without knowing what a detector error model is.
This is a reformatting, not new data. The measurements are Google's, released under
CC-BY-4.0 alongside their 2023 Nature paper. What is added here is structure: a flat schema,
fixed splits, reshaping metadata, and the published decoder predictions bundled per shot.
Why… See the full description on the dataset page: https://huggingface.co/datasets/Bauxitiego/surface-code-syndromes.ai4sci-surface-code-decoding
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… See the full description on the dataset page: https://huggingface.co/datasets/sunweiwei/ai4sci-surface-code-decoding.ai4sci-surface-code-decoding
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… See the full description on the dataset page: https://huggingface.co/datasets/Corning/ai4sci-surface-code-decoding.Surface_code_and_Toric_code