qector-decoder-v3 0.6.2

Source-available Rust/Python QEC R&D platform: PyMatching-compatible validation, belief-matching, BP-OSD (LDPC/qLDPC), CPU/GPU batch decoding, and artifact-hashed benchmark evidence

QECTOR Decoder v3

Source-available Rust/Python quantum error correction decoding platform.

QECTOR Decoder v3 provides a Python package backed by a native Rust extension for quantum error correction research and validation workflows. It includes PyMatching-compatible MWPM validation, Union-Find decoding, belief-matching experiments, BP-OSD/qLDPC workflows, batch decoding, and optional GPU backend checks where the release build and target machine support them.

Website: https://www.qector.store
Repository: https://github.com/GuillaumeLessard/qector-decoder
Commercial licensing: https://www.qector.store

---

Installation

pip install qector-decoder-v3

Supported package target for the public release workflow:

• Python 3.9 to 3.13
• Linux x86_64 wheels
• Windows x64 wheels
• macOS arm64 wheels
• Source distribution for custom/source builds

Optional research and validation extras:

# Stim, Sinter, PyMatching, LDPC and belief-matching ecosystem
pip install "qector-decoder-v3[stim]"

# Benchmark and plotting harness
pip install "qector-decoder-v3[bench]"

# Full validation environment
pip install "qector-decoder-v3[all]"

---

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)

fast = UnionFindDecoder(check_to_qubits, n_qubits)
print(fast.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, CUDABatchDecoder

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)

if CUDABatchDecoder.is_available():
    gpu = CUDABatchDecoder(checks, n_qubits=5)
    corrections = gpu.batch_decode(syndromes)

Stim workflow:

import stim
from qector_decoder_v3 import BlossomDecoder
from qector_decoder_v3.stim_compat import from_stim_detector_error_model

circuit = stim.Circuit.generated(
    "surface_code:rotated_memory_z",
    distance=5,
    rounds=5,
    after_clifford_depolarization=0.005,
)

dem = circuit.detector_error_model(decompose_errors=True)
checks, n_qubits = from_stim_detector_error_model(dem)
decoder = BlossomDecoder(checks, n_qubits)

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Included decoder families

Module	Primary use	Status
UnionFindDecoder	Fast approximate decoding	Stable public API
FastUnionFindDecoder	Optimized Union-Find path	Stable public API
BlossomDecoder	Exact MWPM / PyMatching-parity validation	Stable public API
SparseBlossomDecoder	Faster near-optimal matching	Experimental
BeliefMatching	Correlated-noise accuracy experiments	Research/accuracy mode
BpOsdDecoder	LDPC and qLDPC workflows	Experimental
BatchDecoder / CPUBatchDecoder	CPU Monte Carlo sweeps	Stable public API
CUDABatchDecoder	CUDA batch decoding	Runtime/build dependent
OpenCLBatchDecoder	OpenCL batch decoding	Runtime/build dependent
stim_compat	Stim circuit and DEM conversion	Stable utility
sinter_compat	Sinter custom decoder integration	Stable utility
rest_api	Local decoding service	Local/partner review only

---

Evidence-backed positioning

QECTOR Decoder v3 is positioned as a source-available QEC R&D platform, not as a blanket replacement for every mature decoder in every workload.

The repository includes public benchmark artifacts and reproduction scripts for:

• PyMatching-parity logical-error-rate checks on selected surface-code workloads
• belief-matching accuracy experiments on selected workloads
• GPU bit-identity checks against CPU output on a tested NVIDIA machine
• native memory profiling for selected decoder paths

Important boundaries:

• PyMatching remains faster for standard MWPM latency in the checked-in comparison artifacts.
• Belief-matching is an accuracy/research mode and is much slower in the provided experiments.
• GPU availability and performance depend on wheel build features, drivers, hardware, and runtime checks.
• OpenCL support must be confirmed on the target machine or built under the appropriate licensed/custom configuration.
• REST/API surfaces are for local experiments or controlled review unless separately hardened.

Full methodology, reproducibility notes, and benchmark artifacts are in the GitHub repository:

https://github.com/GuillaumeLessard/qector-decoder

---

GPU availability check

from qector_decoder_v3 import CUDABatchDecoder, OpenCLBatchDecoder

print("CUDA:", CUDABatchDecoder.is_available())
print("OpenCL:", OpenCLBatchDecoder.is_available())

Do this before making any hardware-specific performance claim.

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Licensing

QECTOR Decoder v3 is source-available.

Personal, academic, educational and non-commercial research use is allowed under the repository license. Company use, funded institutional work, SaaS, hosted API deployment, OEM integration, redistribution, paid consulting, or commercial benchmarking requires a commercial license.

Commercial licensing:

https://www.qector.store

Contact:

[e-mail]

---

Citation

@software{lessard2026qector,
  author  = {Guillaume Lessard},
  title   = {{QECTOR Decoder v3}: Rust/Python Quantum Error Correction Decoding Platform},
  year    = {2026},
  version = {0.6.2},
  url     = {https://www.qector.store},
  note    = {Source-available. Commercial license required for commercial use.}
}