Empirical Benchmarks, Accuracy, and Decoding Throughput Datasets for qector-decoder-v3 (v0.6.8) on Low-Resource Edge Hardware | Zenodo
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QECTOR Research Archive
Published July 23, 2026
| Version v1
Dataset
Open
Empirical Benchmarks, Accuracy, and Decoding Throughput Datasets for qector-decoder-v3 (v0.6.8) on Low-Resource Edge Hardware
Authors/Creators
Lessard, Guillaume<br>(Researcher)1
Show affiliations
1.
iD01t Productions
Description
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
Filename<br>Size<br>MD5 Checksum<br>Description
qector_decoder_v068_report.pdf<br>158.07 KB<br>8722ed2deab761f1ad01aa9966b54277
Compiled 3-page scientific PDF benchmark report.
accuracy_circuit.json<br>3.54 KB<br>6a892a304cffaadb2568cd89a2aded2e
Circuit-level LER datasets ($p=0.003$, 100k shots).
faithfulness.json<br>7.99 KB<br>32b728d09c6bf238fff8cd2ff7de0621
Parity checks across distances $d=3 \dots 19$.
manifest.json<br>2.03 KB<br>f674dcf8c2816022be549bbdfcb639fb
Machine architecture, dependencies, and wheel SHA256 hashes.
throughput.json<br>29.67 KB<br>d5fee43963f0a7922d8cf72e6952305c
Phenomenological syndrome throughput sweeps ($N=1\text{k}\dots200\text{k}$).
throughput_circuit.json<br>2.15 KB<br>7ead0ec636b3cc17544554aff9101ce3
Circuit-level memory decoding throughput dataset.
ler_vs_distance.svg<br>62.05 KB<br>6268ab777313ac38457af93ae1789035<br>Vector graphic: Logical Error Rate vs Distance $d$.
throughput_scaling_d5.svg<br>61.01 KB<br>f6740958b7dcf1c3a725881c8189a3fa<br>Vector graphic: Throughput scaling at code distance $d=5$.
throughput_vs_distance_50000.svg<br>56.23 KB<br>96b38ae4c4158c33da37cb6832bc57b9<br>Vector graphic: Throughput vs distance at 50,000 shots.
throughput_vs_distance_200000.svg<br>54.44 KB<br>70a4a7ffed6140d4f605cdc5f83def70<br>Vector graphic: Throughput vs distance at 200,000 shots.
us_per_shot_200000.svg<br>53.98 KB<br>9b319c1e62f8bccd7082afcdfa0fcd65<br>Vector graphic: Latency ($\mu\text{s/shot}$) at 200,000 shots.
bench_main.py<br>21.98 KB<br>0882f49a4ef39a337dee1344ace6b8a7<br>Main phenomenological benchmark suite script.
bench_circuit.py<br>8.92 KB<br>75fb3a983de9220a9bdb0030f94f90e4<br>Stim-integrated circuit-level benchmark script.
calibrate.py<br>4.32 KB<br>eeb5b06a9ba3be1030f01a0471e208fc<br>Quick execution calibration script.
probe.py<br>1.76 KB<br>66324b75f34526c510ea0fb8e92bf04e<br>Native extension C++/Rust bindings diagnostic script.
stim_probe.py<br>1.80 KB<br>474f935b61b423aa228db1b9107b4e5a<br>Stim DEM integration diagnostic script.
qload.py<br>3.33 KB<br>df6768750f70680db3fa2006e37e7a69<br>Python helper module to load and parse JSON datasets.
BENCHMARK.md<br>2.37 KB<br>9b80add4ec5d03f2cf27b57ac116a936<br>User guide for reproducing benchmark runs.
ddr2.txt<br>24.88 KB<br>19e9ad34f8d4829f5d98c029d1875deb
Terminal execution log and console output capture.
Environment & Software...