Sycamore surface-code decoding release v1.1 (development without trained weights)

NOTICE.md

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+# 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.

README.md

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+---
+license: cc-by-4.0
+pretty_name: Sycamore Surface-code Decoding — Private Evaluator Labels
+tags:
+- quantum-error-correction
+- benchmark-evaluation
+---
+
+# Trusted evaluator distribution
+
+This repository contains `grader-labels.tar.gz`, a private operator distribution
+for the [public materialized benchmark](https://huggingface.co/datasets/sunweiwei/ai4sci-surface-code-decoding),
+and `reference-model.tar.gz`, the frozen reference AlphaQubit submission operators
+replay as a known answer (P=0.75506 on the full test). Neither is ever mounted for agents.
+It holds official-path binary logical-flip labels and opaque sample IDs for
+3,250,000 odd-indexed Sycamore shots across 130 fixed conditions. Its manifest
+binds labels to the exact input-only verifier manifest via SHA-256.
+
+Only the trusted grader may mount the extracted `grader/` directory. The
+prediction container and development agent must never receive it. The grader
+never imports submitted model source or executes submitted code. This private
+packaging controls runtime access; the upstream historical data is public.
+
+Original source: [Google Quantum AI, Zenodo 6804040](https://zenodo.org/records/6804040),
+CC-BY-4.0. Attribute Google Quantum AI and retain the source citation when
+redistributing derived data. The fixed score configuration uses same-test
+MWPM/TN anchors, preserves raw performance, and clips normalized score to [0,1].
+Use the immutable revision and archive SHA-256 pinned in the GitHub task.

grader-labels.tar.gz

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+size 26738048

reference-model.tar.gz

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+oid sha256:16df96c11d826eaf88b4dbd8c2010558171b958512cb221135303a4551b46db2
+size 313866583

release-manifest.json

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+      "role": "grader",
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+      "role": "verifier",
+      "sha256": "e3be733f50de3dfb798d95661c5a9d7128787c2eee57f85b60c7abc004fab636"
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+  "release": "sycamore-hard-v1",
+  "revision": "v1.1",
+  "schema": "surface-code-decoding-release/v1"
+}