Empirical Benchmarks, Accuracy, and Decoding Throughput Datasets for qector-decoder-v3 (v0.6.8) on Low-Resource Edge Hardware

Empirical Benchmarks, Accuracy, and Decoding Throughput Datasets for QECTOR Decoder v3 (v0.6.8) on Low-Resource Edge Hardware

Guillaume Lessard (qector.store)

Software Platform:

qector-decoder-v3 (Rust/Python Quantum Error Correction Decoding Platform)

Resource Type:

Dataset / Software Benchmark Artifacts

Licensing & Availability:

• Benchmark Datasets & Reports: Creative Commons Attribution 4.0 International (CC-BY-4.0)

• Software Core (qector-decoder-v3): Source-Available (Free for non-commercial research, academic, and personal use. Commercial license required for commercial/enterprise deployment.)

Abstract

This record contains the complete empirical dataset, raw benchmark files, high-resolution vector figures, and the scientific PDF report for the release of QECTOR Decoder v3 (v0.6.8) by Guillaume Lessard.

The primary objective of this benchmarking campaign was to evaluate the algorithmic accuracy, decoding throughput, and memory scaling of Minimum Weight Perfect Matching (MWPM) and Union-Find (UF) quantum error correction decoders operating in a resource-constrained edge environment. All empirical tests were conducted on an edge machine featuring an HP Dual-Core x86_64 CPU, 3.1 GB RAM, and no local hard drive (booted live from an AntiX Linux USB drive without swap memory).

Key Benchmark Findings

• Exact Mathematical Parity: The compiled C++/Rust qector_blossom_weighted decoder achieves exact parity in Logical Error Rate (LER) and failure counts when compared against PyMatching (v2.4.0) across all tested physical noise rates ($p \in [0.002, 0.008]$) and distances $d \in \{3, 5, 7, 9\}$.

• 100% Algorithmic Faithfulness: All CPU decoders (FastUnionFind, UnionFind, Blossom, SparseBlossom, BatchDecoder, and CPUBatchDecoder) achieved 100% parity verification ($H \cdot \hat{e} \pmod 2 = s$) across odd distances $d \in [3, 19]$.

• Topological Speed Advantage at High Distance: While exact Blossom MWPM scales with graph complexity ($1.77 \times 10^4$ shots/s at $d=9$), qector_union_find maintains $1.62 \times 10^5$ shots/s at $d=9$ ($9.1\times$ faster), demonstrating the speed utility of topological decoders on low-power host CPUs.

Complete File Manifest & Checksums

Environment & Software Specifications

• CPU: HP Dual-Core x86_64 (2 logical threads)

• RAM: 3.1 GB Available

• Operating System: Linux 5.10.240-antix.1-amd64-smp-x86_64 (Live USB Boot)

• Python Version: 3.13.5

• Software Version: qector-decoder-v3 v0.6.8

• Key Dependencies: pymatching==2.4.0, stim==1.16.0, sinter==1.16.0, numpy==2.2.6, scipy==1.18.0, ldpc==2.4.1, beliefmatching==0.2.0

   

Reprodicibility Steps

1. Extract the repository archive files.

2. Initialize a clean virtual environment:

   Bash

    

   python3 -m venv qector_env
   source qector_env/bin/activate
   pip install qector-decoder-v3==0.6.8 pymatching==2.4.0 stim==1.16.0 numpy scipy
   

3. Run the analysis scripts:

   Bash

    

   python qload.py
   python bench_main.py
   python bench_circuit.py
   

Official Software & Dataset Citations

Software Citation 

@software{lessard2026qector,
  author  = {Guillaume Lessard},
  title   = {{QECTOR Decoder v3}: Rust/Python Quantum Error Correction Decoding Platform},
  year    = {2026},
  version = {0.6.8},
  url     = {https://www.qector.store},
  note    = {Source-available. Commercial license required for commercial use.}
}

Dataset Citation 

@dataset{lessard2026qector_benchmarks,
  author       = {Guillaume Lessard},
  title        = {{Empirical Benchmarks, Accuracy, and Decoding Throughput Datasets 
                   for QECTOR Decoder v3 (v0.6.8) on Low-Resource Edge Hardware}},
  month        = jul,
  year         = 2026,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.21501377},
  url          = {https://doi.org/10.5281/zenodo.21501377}
}