qector-decoder-v3 (current PyPI release v0.6.6) is a source-available Rust-backed Python platform for quantum error-correction (QEC) decoding. It provides a uniform API over exact minimum-weight perfect matching (BlossomDecoder), fast approximate Union-Find and its SIMD-accelerated variant (FastUnionFindDecoder), near-optimal sparse-Blossom decoding, belief-matching, BP-OSD for LDPC/qLDPC codes, streaming/hybrid decoders, and high-throughput CPU/GPU batch decoding, with first-class Stim and Sinter integration. v0.6.6 is a critical-fix release that restores package importability after a total import failure affecting every published v0.6.5 wheel. It retains the full v0.6.4/v0.6.5 feature set. This whitepaper documents the full architecture, decoder catalogue, performance benchmarks against PyMatching (latency and logical error rate), throughput results, tail-latency analysis, and explicit accuracy/speed trade-offs across decoder families. All numbers are traceable to environment-stamped, content-hashed benchmark artifacts and are fully reproducible. The platform is designed for research, Monte Carlo validation, and Stim/Sinter-based workflows. It is not positioned as a universal replacement for PyMatching but offers measured advantages on repetition codes across tested distances and on surface codes at low-to-moderate distance.
qector-decoder-v3 (current PyPI release v0.6.6) is a source-available Rust-backed Python platform for quantum error-correction (QEC) decoding. It provides a uniform API over exact minimum-weight perfect matching (BlossomDecoder), fast approximate Union-Find and its SIMD-accelerated variant (FastUnionFindDecoder), near-optimal sparse-Blossom decoding, belief-matching, BP-OSD for LDPC/qLDPC codes, streaming/hybrid decoders, and high-throughput CPU/GPU batch decoding, with first-class Stim and Sinter integration. v0.6.6 is a critical-fix release that restores package importability after a total import failure affecting every published v0.6.5 wheel. It retains the full v0.6.4/v0.6.5 feature set. This whitepaper documents the full architecture, decoder catalogue, performance benchmarks against PyMatching (latency and logical error rate), throughput results, tail-latency analysis, and explicit accuracy/speed trade-offs across decoder families. All numbers are traceable to environment-stamped, content-hashed benchmark artifacts and are fully reproducible. The platform is designed for research, Monte Carlo validation, and Stim/Sinter-based workflows. It is not positioned as a universal replacement for PyMatching but offers measured advantages on repetition codes across tested distances and on surface codes at low-to-moderate distance.
QECTOR Decoder v3: The Official User Manual The authoritative, hands-on guide to high-performance quantum error-correction (QEC) decoding. As quantum computing architectures scale toward practical utility, mitigating physical noise remains the single greatest hurdle to achieving fault tolerance. Sitting directly on the critical path of every fault-tolerant quantum computer is the decoder—the vital classical algorithm responsible for rapidly translating noisy, measured error syndromes into valid physical corrections. QECTOR Decoder v3: The Official User Manual is the definitive engineering guide, backed by real performance artifacts, to utilizing the QECTOR software ecosystem for rapid quantum error-correction development, evaluation, and hardware acceleration. Fully aligned with the official v0.6.6 documentation package, this manual demystifies how QECTOR pairs a high-performance compiled Rust core with a developer-friendly Python API to deliver compiled-language speed without sacrificing scripting ergonomics. Whether you are looking to benchmark standard distance repetition codes or deploy cutting-edge quantum Low-Density Parity-Check (qLDPC) and bivariate bicycle decoders, this manual outlines everything you need to hit the ground running. Key Technical Features Covered: Comprehensive Decoder Library: Deep-dives into fast approximate decoders (Union-Find and SIMD-friendly FastUnionFind), exact minimum-weight perfect matching (Blossom MWPM), and near-optimal SparseBlossom. Belief Propagation & qLDPC Frameworks: Master advanced research and accuracy modes, including BP-OSD (BpOsdDecoder), batched GPU-resident belief propagation (bp_cupy), and correlated-noise BeliefMatching. Hardware-Accelerated Batching: Scale simulations to millions of shots per second using multi-threaded CPU execution (Rayon) and on-device GPU acceleration utilizing CUDA and OpenCL. Ecosystem Interoperability: Seamlessly integrate QECTOR with industry-standard QEC tools via built-in compatibility layers for Stim Detector Error Models (DEMs), PyMatching drop-in replacements, and Sinter Monte Carlo benchmarking sweeps. Automated Routing & Scale-Out: Let QECTOR’s intelligent AutoRouter dynamically select and cache the ideal decoder based on your hardware, batch size, and target code family. Streaming & Multi-Round Orchestration: Implement sliding-window decoders and streaming sessions to handle continuously active, multi-round phenomenological decoding. Inside the v0.6.6 Release: This manual includes the fully reproduced, peer-verified QECTOR Workbench Master Report (v0.6.6). It features zero mocked data. Every performance metric—spanning tail latencies (P99), cold-start times, memory footprints, and logical error rate (LER) threshold sweeps—is rigorously measured, archived under Zenodo DOI: 10.5281/zenodo.21320543, and 100% reproducible on your local machine. Perfect for the quantum software engineer who wants to be productive in an afternoon, as well as the academic researcher parsing high-throughput syndromes, this book is your blueprint for modern QEC simulation and decoding. Resources & Licensing: Project Page & PyPI Wheels: Search for qector-decoder-v3 Official Website & Licensing Portal: www.qector.store Source Repository: github.com/GuillaumeLessard/qector-decoder Commercial Licensing & Inquiries: admin@qector.store (Free for personal, academic, and non-commercial research; commercial R&D and product integration require a paid license).
QECTOR Decoder Benchmark Datasets & Empirical Reports This repository hosts the evolving collection of reproducible benchmark datasets, JSON measurement artifacts, and empirical performance reports for the QECTOR Decoder v3 platform (Rust/Python quantum error-correction decoder). Each versioned deposit contains environment-stamped, content-hashed JSON artifacts together with the corresponding publication-grade report. All numerical results are fully regenerable from the published wheels and the scripts listed in the reproducibility sections. Scope across versions Code-capacity and circuit-level (Stim rotated_memory_z) evaluations Throughput (shots/s, measure-s/shot), logical error rates with 95 % Wilson intervals, syndrome faithfulness (GF(2) exact check), and CUDA-vs-CPU bit identity Decoder families: FastUnionFind, UnionFind, Blossom (exact MWPM), SparseBlossom, Batch/CPUBatch, BPOSD, HybridCascade, AmbiguityCluster, LookupTable, and CUDABatch (unweighted + DEM-weighted) Hardware provenance, licence-tier disclosure (GPU gated behind Enterprise), and SHA-256 inventories of every backing artifact Version history (selected) v0.6.6 (July 2026) – Workbench Benchmark Master Report: 1 858 decode-timing measurements from 105 runs, six code topologies (Heavy Hex, Repetition, Ring, Rotated Surface, Toric, Unrotated Surface), fixed p = 0.05, distances d = 3–19. Focus on raw latency, harmonic-mean throughput and peak memory. v0.6.8 - Empirical edge-hardware benchmarks. v0.7.0 (August 2026) – Full Empirical Decoder Benchmark Report on the published wheel: nine CPU families + CUDA batch path, circuit-level LER parity with PyMatching 2.4.0, exact syndrome faithfulness, DEM-weighted GPU scaling restoration, and complete artifact inventory (Appendix A). All figures and tables in the accompanying reports are regenerated from the JSON files listed with their SHA-256 hashes; no pre-release numbers are carried forward. Related resources PyPI: qector-decoder-v3 Repository: https://github.com/GuillaumeLessard/qector-decoder Website: https://www.qector.store
QECTOR-Decoder-v3 is a high-throughput, multi-backend quantum error correction (QEC) decoding suite implemented in compiled native Rust with native Python bindings via PyO3. Designed for high-performance computing (HPC) environments, low-latency quantum hardware pipelines, and offline zero-egress research, the engine bridges theoretical stabilizer formalisms with hardware-level decoding execution. This archival release includes the formal technical specification, architectural reference manual, cryptographic proof suite, and reproducible Linux/x86_64 verification harness for version 1.0.0. Key Architectural Components Native Compiled Core: High-performance Rust backend exposing zero-copy FFI bindings to Python (CPython 3.9–3.13) via PyO3. Multi-Backend Decoding Library: Includes implementations of Blossom Minimum Weight Perfect Matching (MWPM), FastUnionFind, Belief Propagation with Ordered Statistics Decoding (BP-OSD), Belief-Matching, and an AutoDecoder multi-tier fallback architecture. Hardware Interoperability: Drop-in compatibility with standard QEC formats and frameworks (Stim, PyMatching, Sinter) with Rayon multi-threading and CUDA/OpenCL acceleration hooks. Model Context Protocol (MCP) Integration: Native stdio JSON-RPC tool surfaces supporting automated syndrome decoding, check-matrix validation, and real-time telemetry profiling. Included Proof Artifacts & Verification Bundle This release contains the complete, cryptographic proof suite validating numerical correctness, memory layout, and statistical bounds: certified_proof.json & certified_proof.json.sha256: Tamper-evident execution logs confirming 100% syndrome faithfulness (H⋅c=s) across all evaluation checkpoints. wilson_interval.png / .svg: Exact 95% Wilson score confidence interval statistical sweeps across code distance ranges (d=3 through d=7). syndrome_faithfulness.png / .svg: High-resolution visual parity validation and check-matrix verification charts. workspace_strides.png / .svg: Cache-line memory alignment and SIMD stride profiling diagnostics. certified_report.pdf & certified_report.md: Formal verification report and reproduction methodology. verify_artifacts.py & test_qector_decoder_v3_proofs_linux.py: Automated Python/Linux validation scripts to independently recompute and verify SHA-256 signatures and artifact integrity. Reproduction & Verification To verify the included artifact bundle locally: Bash python3 verify_artifacts.py python3 test_qector_decoder_v3_proofs_linux.py Licensing & Metadata License: PolyForm Noncommercial / Proprietary Commercial (iD01t Productions) PyPI Distribution: qector-decoder-v3 Keywords: Quantum Error Correction, Surface Codes, Rust, PyO3, Stabilizer Decoders, MWPM, BP-OSD, Union-Find, Fault-Tolerant Quantum Computing, Model Context Protocol (MCP). License terms. The software, scripts, and code in this record (including the Python verification scripts and the inlined pure-Python ground truth) are licensed under the PolyForm Noncommercial License 1.0.0: noncommercial use only. Any commercial use requires a separate commercial license from iD01t Productions. Documentation, reports, and figures in this record are made available under the Creative Commons Attribution 4.0 International License (CC-BY-4.0) with credit to Guillaume Lessard / iD01t Productions. Copyright (c) 2026 Guillaume Lessard / iD01t Productions. All rights reserved except as expressly granted above.
QECTOR Decoder v3: Syndrome Faithful Decoding, Foundations, Algorithms, and Architecture of a Fifteen Backend Quantum Error Correction Engine QECTOR Decoder v3 is a quantum error correction decoding engine written in Rust with PyO3 Python bindings. It provides fifteen specialized decoding backends unified by a single invariant enforced on every call: syndrome faithfulness, the requirement that H c = s (mod 2) for any correction the engine returns. Scope of this manual This reference manual documents implementation version 1.0.0 of qector decoder v3. Every mathematical statement in it is anchored to source modules and regression tests in the frozen 1.0.0 workspace. Hardware dependent measurements such as latency, throughput, and memory are intentionally excluded from the manual; the methodology for producing and reporting such figures is specified instead, so any future claim is reproducible and auditable. Backend families covered Exact minimum weight perfect matching using Edmonds' blossom algorithmEvent driven Sparse Blossom using radix heap region growthZero allocation Union Find (UF 01)Belief propagation with ordered statistics (BP OSD) for quantum LDPC codesAmbiguity clustering with learned predecodersSpace time and streaming decoding across multiple roundsTwo stage CSS decoding for correlated X and Z noiseCUDA and OpenCL batch kernels, proven bit identical to the CPU reference on tested configurationsA native routing and orchestration layer with a seven tier fallback chain What the manual includes The manual develops the binary field mathematics and stabilizer formalism from first principles, proves the core correctness theorems shared by every backend including syndrome faithfulness, the logical error criterion, and path flipping faithfulness of MWPM, and walks through worked numerical examples including the Steane [[7,1,3]] code, the rotated surface code at distance 3, and a Wilson confidence interval computed by hand. It documents the public API surface, both stable and provisional, the detector error model pipeline, the licensing model and tier enforcement, ecosystem integrations such as a PyMatching compatible shim, Sinter decoders, and a Qiskit plugin, and the testing and validation strategy across correctness, property based, cross decoder equivalence, and GPU bit identity layers. Reproducibility policy Claims in this manual are explicitly scoped: exactness, bit identity, and accuracy statements are attached only to the workloads and configurations on which they were established. No universal performance claim is made anywhere in the document, and prior benchmark artifacts that did not survive a core fingerprint change were formally withdrawn. Appendix D provides the exact reproduction commands, and Appendix C maps every claim to its evidence anchor in the source tree. Keywords quantum error correction, QEC, decoder, MWPM, Union Find, BP OSD, LDPC, qLDPC, surface code, Stim, Sinter, PyMatching, Rust, Python, PyO3, CUDA, batch decoding, reproducible research, benchmarking Author Guillaume Lessard, iD01t Productions, ORCID 0009 0000 3465 3753 Companion document (ground-truth verification campaign, 2026-09-27). PROOFS.pdf, attached to this version, gives the complete re-derived proofs for the decoder theorems of this manual (syndrome fidelity Hc = s, MWPM path-flipping, Union-Find parity algebra and peeling, OSD, AmbiguityCluster, TwoStage, LP optimality certificate), with explicit scope limits and a separate section listing excluded conjectures. See the record errata manifest for retractions applied across the corpus.
QECTOR MEGA v2.5.1: High-Performance Quantum Error Correction Decoding Engine Author: Guillaume Lessard, iD01t Productions (ORCID: 0009-0000-3465-3753) Release date: September 20, 2026 Software version: QECTOR MEGA v2.5.1 DOI: 10.5281/zenodo.22863432 What this deposit contains This record archives the extended technical report and the raw benchmark datasets for QECTOR MEGA v2.5.1, a quantum error correction decoding engine. Files: QECTOR_MEGA_v2_5_1_Extended_Zenodo_Report.pdf (39-page extended report: methodology, telemetry tables, threats to validity) and QECTOR_MEGA_v2_5_1_Zenodo.zip (throughput_v11.csv/json with 81 configurations, all_decoders_benchmark_results.json with 18 decoder benchmark entries, maths_physics_production_report.json with 12 mathematical invariant checks, 13 physics entries and 3 OpenCL bit-exactness checks, 8 figures, certification ledger, engineering log, pinned requirements). Measured topologies (as present in the deposited data) The datasets cover 14 topologies: planar surface codes from distance d=3 to d=21, ring codes at n=128, Q70, Q102, Gross [[144,12,12]], Bivariate Bicycle [[288,12]], and regular Gallager ensembles (j=2,k=4) and (j=3,k=6). Note: earlier text mentioned Surface d=25, BB [[400,200]] and Ring n=64; these configurations are NOT present in the deposited datasets and have been removed from this description. Key measured results Peak syndrome extraction throughput: 483,143,722 syndromes/second on Surface d=3 at batch size 1,048,576 (NVIDIA Tesla T4, single run). Decoding: 100.0% faithful recovery (Hc = s mod 2, zero residual violations) on 5/5 configurations at 512 shots each; peak decode throughput 61,927 syndromes/second on Gallager (j=2,k=4). Bit-exactness: 0 mismatches between OpenCL GPU kernels and NumPy references on Surface d=3, 5, 7 (batch 8,192). Mathematical invariants: 12/12 checks passed; physical linear response within 3% relative error for p in [0.001, 0.08]. Measurement caveats. All figures are single-machine measurements (Google Colab, one Tesla T4); headline figures are single runs without quantified uncertainty, and CPU speedup references measured on shared hardware are noisy. Third-party baselines present in the raw JSON (BP-OSD, PyMatching) are not discussed in the report narrative; consult the deposited JSON files for the full picture. Licensing Report, datasets and figures in this record are shared under CC-BY-4.0 (record license). QECTOR MEGA software itself is distributed separately under its own commercial terms (see https://qector.store). Citation Lessard, Guillaume. QECTOR MEGA v2.5.1: High-Performance Quantum Error Correction Decoding Engine. Zenodo, September 2026. doi:10.5281/zenodo.22863432. iD01t Productions.
Reliable large-scale quantum computation relies on fault-tolerant architectures, where quantum error correction (QEC) continuously extracts and decodes error syndromes in real time. A critical component in QEC is the decoder, a classical subsystem that must simultaneously deliver high logical accuracy and ultra-low latency. This paper presents a novel algorithm-hardware co-design that improves the accuracy-latency trade-off over existing approaches such as vanilla Minimum-Weight Perfect Matching (MWPM) and Union-Find (UF) decoders. At the algorithmic level, we introduce coset ensemble decoding, which improves UF decoding by explicitly exploiting logically equivalent cosets. Our method performs ensemble forest exploration to generate multiple coset-consistent candidates and aggregates them to approximate coset-level maximum-likelihood decoding. We further reduce computational and memory complexity via reverse-order elimination and lossless graph compression, without sacrificing accuracy. At the hardware level, we design a domain-specific architecture that temporally reuses resources, avoiding the code-distance-proportional resource growth in prior spatial architectures. Several optimizations, such as multi-bank memory hashing and hierarchical ID mapping, are proposed to mitigate pipeline stalls and memory conflicts under highly concurrent access patterns. Under a circuit-level depolarizing noise model, our co-design approach achieves a better accuracy-latency trade-off than prior MWPM- and UF-based decoders, while reducing FPGA LUT consumption by up to 8.2 times compared with reported UF-based decoder resources. The tunable candidate number further exposes a flexible design knob, enabling users to tailor decoding performance to the requirements of different fault-tolerant workloads. Our implementation is publicly available at https://github.com/IMSeonL/coset-ensemble-decoder.