qector-decoder-v3 0.5.5

Source-available Rust/Python quantum error correction decoder package for QEC research, PyMatching-compatible validation, belief-matching, BP-OSD/qLDPC workflows, CPU/GPU batch decoding, and reproducible benchmark evidence

QECTOR Decoder v3

Python and Rust quantum error correction decoder package.

QECTOR Decoder v3 helps researchers and developers test quantum error correction decoder workflows from Python, with native Rust performance paths and reproducible benchmark artifacts.

Common search terms: quantum error correction, QEC decoder, quantum decoder Python, Rust quantum error correction, PyMatching-compatible decoder, Stim workflow, Sinter benchmark, BP-OSD, LDPC, qLDPC, surface code decoder, MWPM decoder, union find decoder, belief matching, PyO3, maturin, QECTOR.

Website: https://www.qector.store
PyPI: https://pypi.org/project/qector-decoder-v3/
DOI: https://doi.org/10.5281/zenodo.20825980
Repository: https://github.com/GuillaumeLessard/qector-decoder
Commercial licensing: https://www.qector.store

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Installation

Recommended command:

pip install qector-decoder-v3

Verify the install:

python -c "from qector_decoder_v3 import UnionFindDecoder, BlossomDecoder; print('QECTOR OK')"

Optional extras:

pip install "qector-decoder-v3[stim]"
pip install "qector-decoder-v3[bench]"
pip install "qector-decoder-v3[all]"

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Windows note

Use the pip bound to your active standard Python environment. Do not force py -m pip unless you have checked which interpreter the Windows launcher selected.

On some systems, py can select a free-threaded interpreter such as python3.13t.exe. QECTOR v0.5.x publishes standard CPython wheels, not cp313t free-threaded wheels.

Working Windows command:

pip install qector-decoder-v3
python -c "from qector_decoder_v3 import UnionFindDecoder, BlossomDecoder; print('QECTOR OK')"

Check launcher targets with:

py -0p

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Supported public wheels

Platform	Wheel status
Linux x86_64	Published
Windows x64	Published
macOS arm64 / Apple Silicon	Published
macOS Intel x86_64	Not published in v0.5.x public CI
CPython free-threaded builds such as cp313t	Not published in v0.5.x

Supported Python classifiers are standard CPython 3.9 to 3.13.

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Quick start

import numpy as np
from qector_decoder_v3 import UnionFindDecoder, BlossomDecoder

check_to_qubits = [[0, 1], [1, 2], [2, 3], [3, 4]]
n_qubits = 5
syndrome = np.array([0, 1, 0, 0], dtype=np.uint8)

uf = UnionFindDecoder(check_to_qubits, n_qubits)
print(uf.decode(syndrome))

mwpm = BlossomDecoder(check_to_qubits, n_qubits)
print(mwpm.decode(syndrome))

Batch decoding:

import numpy as np
from qector_decoder_v3 import BatchDecoder

checks = [[0, 1], [1, 2], [2, 3], [3, 4]]
syndromes = np.random.randint(0, 2, size=(4096, 4), dtype=np.uint8)

cpu = BatchDecoder(checks, n_qubits=5)
corrections = cpu.parallel_batch_decode(syndromes)
# single-shot also works (v0.5.3+):
corrections_single = cpu.decode(syndromes[0])
print(corrections.shape)

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API surface

Stim / DEM integration

import stim
from qector_decoder_v3.stim_compat import (
    from_stim_detector_error_model,
    stim_circuit_to_check_matrix,  # identical alias
    to_stim_decoder,
    stim_decoder_from_dem,
)

dem = stim.Circuit.generated(
    "surface_code:rotated_memory_x", distance=5
).detector_error_model(decompose_errors=True)

c2q, nq = from_stim_detector_error_model(dem)
# or equivalently: stim_circuit_to_check_matrix(dem)

decoder = stim_decoder_from_dem(dem)

Sinter integration

import sinter
from qector_decoder_v3.sinter_compat import (
    QectorSinterDecoder,
    QectorDecoderWrapper,   # backward-compat alias for QectorSinterDecoder
    qector_sinter_decoders,
)

samples = sinter.collect(
    num_workers=4,
    tasks=tasks,
    decoders=["qector_belief", "qector_blossom", "qector_unionfind"],
    custom_decoders=qector_sinter_decoders(),
)

# standalone single-syndrome decode (v0.5.3+, no Sinter required):
dec = QectorSinterDecoder("blossom")
obs = dec.decode(syndrome, dem=dem)

BeliefMatching raw H constructor (v0.5.3+)

import numpy as np
from qector_decoder_v3.belief_matching import BeliefMatching

# From a Stim DEM (recommended for circuit-level noise):
bm = BeliefMatching.from_detector_error_model(dem)

# From a raw check matrix H (uniform prior p=0.1):
H = np.array([[1, 1, 0], [0, 1, 1]], dtype=np.uint8)
bm = BeliefMatching(H, p=0.05)
obs = bm.decode(syndrome)

CUDA / GPU

from qector_decoder_v3 import CUDABatchDecoder

# Always check availability before constructing
if CUDABatchDecoder.is_available():
    dec = CUDABatchDecoder(check_to_qubits, n_qubits)
    corrections = dec.batch_decode(syndromes)
else:
    print("No CUDA GPU detected — use BatchDecoder for CPU batch decoding")

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Independent validation (v0.5.3)

Validated by independent automated test suite (87/87 checks PASS on v0.5.3). Platform: Windows 10, AMD Ryzen 16-core, NVIDIA GTX 1660 Ti (CUDA 7.5), Python 3.11, PyMatching 2.4.0. Full artifact: benchmark_results/results_v053_retest.json.

v0.5.3 API fixes (all 3 verified):

Fix	Before	After
BatchDecoder.decode(syndrome)	absent (only parallel_batch_decode)	present, 1-row batch wrapper
BeliefMatching(H, p=0.1)	TypeError (expected _Matrices)	accepts raw numpy H, uniform prior
QectorSinterDecoder.decode(syndrome, dem)	absent	present, DEM cached on first call

Claim	Result
30 decoder x code combinations, 100% syndrome-valid corrections	Confirmed
pymatching_compat bit-identical to PyMatching 2.4.0	Confirmed
Blossom LER within 0.00% of PyMatching on repetition code d=3-9	Confirmed
Blossom LER within 1.78% of PyMatching on rotated surface code d=3-7	Confirmed
CUDA batch 100% CPU-agreeing at all tested batch sizes (GTX 1660 Ti)	Confirmed
CUDA batch 6.9-7.7x faster than CPU batch at 100k shots	Confirmed
Workbench single-decode rep d=5 Blossom: 285,713 dec/s, p50 3.50 us, p99 9.50 us	Confirmed
AutoDecoder backends: cpu, cuda GTX 1660 Ti, opencl=False	Confirmed
Workbench JSON/CSV/PDF export pipeline end-to-end	Confirmed
LookupTableDecoder table_size for rep d=5: 64 entries	Confirmed
d=101 stress decode completes without error	Confirmed
Invalid input rejected with clear ValueError / TypeError	Confirmed

Single-shot latency reference (us/decode, 2000 samples, independently validated)

Decoder	rep d=5	rep d=9	surf d=3	surf d=5
UnionFindDecoder	9.3	10.0	12.2	10.1
FastUnionFindDecoder	9.5	10.2	11.4	12.1
BlossomDecoder	10.6	10.6	14.8	16.8
SparseBlossomDecoder	11.8	10.6	11.5	29.2
CPUBatchDecoder	11.2	9.7	9.5	10.7
LookupTableDecoder	8.7	10.7	9.5	—

LookupTableDecoder is the fastest single-shot decoder on rep d=5 (precomputed table, 64 entries, O(1) lookup).

Known limitations

• Union-Find is ~3x less accurate than MWPM — expected speed/accuracy trade-off.
• Single-round code-capacity noise does not produce surface-code distance scaling. Use circuit-level Stim DEM with qector_sinter_decoders() for threshold curves.
• SparseBlossom batch may return different (but valid) corrections than single-shot on degenerate syndromes. Benign matching degeneracy.
• CUDABatchDecoder raises RuntimeError cleanly when no CUDA GPU is present. Use CUDABatchDecoder.is_available() to check first.

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License

Source-available. Commercial use requires written licensing through https://www.qector.store.