Production-Grade QEC Decoding for Python
QECTOR Decoder v3 is a Rust-core Python library with 15 concrete quantum error correction decoder configurations, from Blossom and Union-Find to BP-OSD, space-time, and optional batch paths. Workbench v1.0.7 lists 19 named kinds because it also includes the AutoDecoder and Auto Router orchestration entries. Version 1.0.0 defines API stability tiers, a qector CLI, and a qector-doctor diagnostic.
pip install qector-decoder-v3==1.0.0 import numpy as np from qector_decoder_v3 import BlossomDecoder decoder = BlossomDecoder([[0, 1], [1, 2], [2, 3], [3, 4]], n_qubits=5) correction = decoder.decode(np.array([0, 1, 0, 0], dtype=np.uint8))
v1.0.0: first stable release
- Semantic-versioning frozen; the public API is governed by documented stability tiers (Stable / Workload-sensitive / Experimental).
- Sinter entry points (qector_blossom, qector_belief, qector_unionfind, and more) and a qiskit-qec plugin registered: sinter.collect() works with no custom_decoders=.
- New decoder families: AmbiguityClusterDecoder, TwoStageDecoder, ColourCodeDecoder (opt-in cluster_bposd). Relay-BP schedules, CS-OSD(lambda, w) and LLR damping in BP-OSD.
- qector decode / qector bench / qector serve CLI and qector-doctor (15-check environment diagnostic).
- Hardware-specific benchmark data is not published here. The package and manual document how to perform a scoped local measurement with the required environment and artifact metadata.
The decoders
UnionFind, FastUnionFind, Blossom MWPM, SparseBlossom, BP-OSD for qLDPC, batch decoders, AutoDecoder, space-time, streaming, two-stage, ambiguity-cluster, lookup-table, and learned research surfaces.
Evidence
- PyPI package: qector-decoder-v3 1.0.0
- Artifacts and reproduction harness: github.com/GuillaumeLessard/qector-decoder
- Normative reference manual DOI 10.5281/zenodo.22244510
- v1.0.0 user manual DOI 10.5281/zenodo.21611214
- Certification and proof bundle DOI 10.5281/zenodo.22046403
- Validation reports and SHA-256 sealed manifests, archived with the decoder source on GitHub.