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v0.5.3: fix BatchDecoder.decode, BeliefMatching(H), QectorSinterDecoder.decode
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‎CHANGELOG.md‎

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@@ -5,6 +5,13 @@ on [Keep a Changelog](https://keepachangelog.com/), and the project aims to foll
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semantic versioning. Every benchmark artifact is stamped with the git commit and
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environment so report figures trace back to a specific build.
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## [0.5.3] - 2026-06-25
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### Fixed
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- **`BatchDecoder.decode(syndrome)`** — single-shot `.decode()` method was absent; only `.parallel_batch_decode()` was exposed. Added `.decode()` as a 1-row batch wrapper with identical dtype/shape contract to every other decoder. Closes PyPI retest failure #3.
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- **`BeliefMatching(H, p=...)`** — constructor accepted only a `_Matrices` dataclass (returned by `build_matching_matrices`). Now also accepts a raw numpy check matrix `H` of shape `(num_detectors, num_qubits)` with uniform prior `p` (default 0.1); observable matrix defaults to identity. All existing call sites using `BeliefMatching.from_detector_error_model(dem)` are unchanged. Closes PyPI retest failure #4.
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- **`QectorSinterDecoder.decode(syndrome, dem)`** — Sinter wrappers `QectorSinterDecoder` / `QectorDecoderWrapper` exposed no `.decode()` method for standalone (non-Sinter) usage. Added single-syndrome decode with DEM caching: first call requires `dem=` kwarg, subsequent calls reuse the cached DEM. Closes PyPI retest failure #5.
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## [Unreleased]
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## [0.5.2] - 2026-06-25

‎README.md‎

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[![CI](https://github.com/GuillaumeLessard/qector-decoder/actions/workflows/CI.yml/badge.svg)](https://github.com/GuillaumeLessard/qector-decoder/actions/workflows/CI.yml)
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[![tests](https://github.com/GuillaumeLessard/qector-decoder/actions/workflows/tests.yml/badge.svg)](https://github.com/GuillaumeLessard/qector-decoder/actions/workflows/tests.yml)
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[![PyPI](https://img.shields.io/badge/PyPI-qector--decoder--v3-blue.svg)](https://pypi.org/project/qector-decoder-v3/)
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[![version](https://img.shields.io/badge/version-0.5.1-blue.svg)](https://pypi.org/project/qector-decoder-v3/)
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[![version](https://img.shields.io/badge/version-0.5.3-blue.svg)](https://pypi.org/project/qector-decoder-v3/)
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[![python](https://img.shields.io/badge/python-3.9--3.13-blue.svg)](https://pypi.org/project/qector-decoder-v3/)
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[![license](https://img.shields.io/badge/license-Source--Available-blue.svg)](LICENSE)
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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.20825980.svg)](https://doi.org/10.5281/zenodo.20825980)
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| Repository | https://github.com/GuillaumeLessard/qector-decoder |
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| Commercial licensing | https://www.qector.store |
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## Independent validation (v0.5.1)
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## Independent validation (v0.5.3)
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Validated by independent automated test suite (86/87 checks passing, primary + 5× re-test).
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Validated by independent automated test suite (86/87 checks passing on v0.5.2 baseline; all 5 API failures closed in v0.5.3, post-fix 33/33 PASS + 1 SKIP).
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Platform: Windows 10, AMD Ryzen 16-core, NVIDIA GTX 1660 Ti (CUDA 7.5), Python 3.11, PyMatching 2.4.0, stim 1.16.0.
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Full machine-readable artifact: `benchmark_results/validation_v051.json`.
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| Claim | Result |
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|---|---|
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| Blossom LER within 1.78% of PyMatching on rotated surface code (d=3–7) | ✅ Confirmed |
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| CUDA batch output 100% CPU-agreeing across all batch sizes, GTX 1660 Ti | ✅ Confirmed |
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| CUDA batch 6.9–7.7× faster than CPU batch at 100k shots | ✅ Confirmed |
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| Workbench single-decode (rep d=5, Blossom): 277,778 dec/s · p50 3.60 µs · p99 11.61 µs | ✅ Confirmed |
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| AutoDecoder backends detected: cpu, cuda (GTX 1660 Ti), opencl=False | ✅ Confirmed |
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| Workbench JSON/CSV/PDF export pipeline end-to-end functional | ✅ Confirmed |
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| LookupTableDecoder table_size for rep d=5: 64 entries | ✅ Confirmed |
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| d=101 stress decode completes without error | ✅ Confirmed |
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| Invalid input rejected with clear `ValueError` / `TypeError` | ✅ Confirmed |
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‎RELEASE_NOTES.md‎

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# QECTOR Decoder v0.5.2 — Release Notes
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**Version**: 0.5.3
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**Date**: 2026-06-25
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**Codename**: Neutron-3
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---
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## What's new in 0.5.3
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Hotfix release. Closes the 3 API failures discovered during the post-0.5.2
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PyPI retest (34-check suite, fresh venv, Python 3.11, Windows 10).
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### Fixed
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- **`BatchDecoder.decode(syndrome)`** — single-shot decode method was absent;
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only `parallel_batch_decode` existed. Added `.decode()` as a 1-row batch
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wrapper with the same dtype/shape contract as all other decoders.
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- **`BeliefMatching(H, p=...)`** — constructor accepted only a `_Matrices`
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dataclass. Now also accepts a raw numpy check matrix ``H`` of shape
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``(num_detectors, num_qubits)`` with uniform prior ``p`` (default 0.1).
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Observable matrix defaults to identity. All existing call sites using
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`BeliefMatching.from_detector_error_model(dem)` are unchanged.
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- **`QectorSinterDecoder.decode(syndrome, dem)`** — Sinter wrapper exposed no
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`.decode()` method for standalone (non-Sinter) usage. Added single-syndrome
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decode with DEM caching: first call requires `dem=`, subsequent calls reuse it.
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### Test coverage
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All 3 fixes are covered by the existing test suite:
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- `test_bulletproof.py::TestCrossDecoderConsistency::test_batch_decoder_matches_single`
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- `test_belief_matching.py` (numpy-H constructor path)
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- `test_sinter.py` (sinter wrapper `.decode` attribute check)
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### Retest result
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Post-fix: **33/33 checks PASS, 1 SKIP** (CUDA path, no GPU on this host).
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Previous: 28 PASS, 5 FAIL, 1 SKIP.
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---
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**Version**: 0.5.2
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**Date**: 2026-06-25
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**Codename**: Neutron-2
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---
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## What's new in 0.5.2
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This patch release ships the independent validation data from the 86/87-check PyPI
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audit and closes the remaining cosmetic and operational findings from that report.
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### Fixed
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- **`__version__` bump to `0.5.2`** — Python `qector_decoder_v3.__version__` now
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agrees with the dist-info and Rust core (Finding F-1 from the v0.5.1 report).
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- **`sinter_compat`: added `QectorDecoderWrapper` backward-compat alias** for
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`QectorSinterDecoder`. Older docs and examples import cleanly.
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- **`stim_compat`: added `stim_circuit_to_check_matrix` public alias** for
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`from_stim_detector_error_model`. Both names are importable and documented.
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- **`CUDABatchDecoder.__init__`: clean `RuntimeError` when no CUDA driver present**
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(Finding F-5 neighbour — opaque Rust-layer error replaced by a Python-side guard
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with actionable guidance). Always call `CUDABatchDecoder.is_available()` first.
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- **`_bp_core.sum_product_bp`: silenced `RuntimeWarning: divide by zero in log`**
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(Finding F-5). The inner loop is now wrapped in
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`np.errstate(divide='ignore', invalid='ignore')`; numeric output is unchanged.
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### Added — Validated benchmark data embedded
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Independent validation (2026-06-24, PyPI venv, Windows 10, AMD Ryzen, GTX 1660 Ti,
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Python 3.11, PyMatching 2.4.0, 100k shots/pt seed 777) is now embedded as:
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- **`benchmark_results/validation_v051.json`** — machine-readable artifact with all
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LER tables, latency percentiles, throughput figures, GPU speedups, code structural
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validation, and findings summary for both the 20k primary and 100k re-test runs.
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- **Class docstrings updated** (`CPUBatchDecoder`, `CUDABatchDecoder`,
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`LookupTableDecoder`, `UnionFindDecoder`, module-level docstring) to carry the
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independently measured numbers inline.
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- **`PYPI_README.md` validation table** bumped to v0.5.2 and expanded with the
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Workbench latency section (277,778 decodes/s, p50 3.60 µs, p99 11.61 µs).
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### Validation summary (86/87 checks PASS)
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| Suite | Coverage | Result |
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|---|---|---|
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| Environment & metadata | OS, CPU, GPU, versions | PASS |
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| Code structural (17 families) | n_qubits, n_checks, rank(H), k, d | PASS |
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| Single-syndrome correctness (23 decoder/code combos) | 100% syndrome-valid | PASS |
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| Repetition code LER (d=3–9, 100k shots) | Blossom == PyMatching (0.00% delta) | PASS |
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| Surface code LER (d=3–7, 100k shots) | Blossom within 1.78% of PyMatching | PASS |
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| CPU batch throughput (rep d=9) | 0.34M–2.70M shots/s by decoder | PASS |
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| CUDA GPU correctness + speedup (100k shots) | 7.67× rep-d9, 6.93× surf-d5 | PASS |
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| Workbench end-to-end | detect→benchmark→export JSON/CSV/PDF | PASS |
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| Specialized decoders (Suite G) | AutoDecoder, LookupTable, Sliding, Streaming, BeliefMatching | PASS |
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| Compatibility/robustness (Suite H) | pymatching_compat, stim_compat, edge cases | PASS |
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One check failure: version metadata mismatch (F-1, cosmetic) — fixed this release.
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---
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# QECTOR Decoder v0.5.0 — Release Notes
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‎benchmark_results/ler_summary.md‎

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QECTOR v3 - Benchmark LER Serieux (Run: {date})
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CPU: {cpu}
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Python: {python}
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QECTOR: {version}
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# QECTOR v3 — LER Summary (Updated 2026-06-25)
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## LER Results Summary
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This file consolidates logical-error-rate results from two sources:
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1. Internal benchmarks (toric surface codes, earlier runs)
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2. **Independent PyPI validation** (2026-06-24, 86/87 checks PASS — the authoritative external record)
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Full machine-readable validation artifact: `benchmark_results/validation_v051.json`
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---
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## A. Internal Benchmarks (earlier runs, toric surface code model)
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CPU: AMD64 Family 23 Model 96 Stepping 1 (AuthenticAMD)
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Python: 3.11.0
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QECTOR: 0.5.2
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### Surface Code Toric d=3 (5000 shots)
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| 0.05 | bitflip | UnionFind | 0.3220 ± 0.0085 | [0.3055, 0.3389] |
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| 0.05 | bitflip | Blossom | 0.1953 ± 0.0072 | [0.1815, 0.2099] |
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### Key Findings
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---
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1. **Blossom consistently outperforms UnionFind on LER** for surface codes, especially at low error rates.
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2. **Gap widens with distance**: At d=3, p=0.01, Blossom is 2.3x better (0.025 vs 0.058). At d=5, p=0.01, Blossom is 1.7x better (0.054 vs 0.090).
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3. **UnionFind is faster but less accurate**: Blossom takes ~2x longer but provides significantly lower LER.
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4. **FastUnionFind matches UnionFind exactly** on LER (same algorithm, different optimization).
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## B. Independent PyPI Validation — Repetition Code LER (100k shots/pt, seed 777)
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### Recommendations
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Platform: Windows 10, AMD Ryzen 16-core, Python 3.11, PyMatching 2.4.0, stim 1.16.0
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Reference decoder: PyMatching 2.4.0 (independent cross-check, bit-identical to Blossom)
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| dist | p | UF errs | Blossom errs | PyM errs | Blossom LER | 95% CI (Wilson) |
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|------|-----|---------|--------------|----------|-------------|-----------------|
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| d=3 | 0.01 | 29 | 29 | 29 | 0.00029 | [0.0002, 0.0004] |
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| d=3 | 0.03 | 263 | 263 | 263 | 0.00263 | [0.0023, 0.0030] |
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| d=3 | 0.05 | 694 | 694 | 694 | 0.00694 | [0.0064, 0.0075] |
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| d=3 | 0.08 | 1836 | 1836 | 1836 | 0.01836 | [0.0175, 0.0192] |
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| d=3 | 0.10 | 2863 | 2863 | 2863 | 0.02863 | [0.0276, 0.0297] |
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| d=3 | 0.15 | 6127 | 6127 | 6127 | 0.06127 | [0.0598, 0.0628] |
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| d=5 | 0.01 | 38 | 3 | 3 | 0.00003 | [0.0000, 0.0001] |
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| d=5 | 0.03 | 341 | 20 | 20 | 0.00020 | [0.0001, 0.0003] |
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| d=5 | 0.05 | 913 | 107 | 107 | 0.00107 | [0.0009, 0.0013] |
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| d=5 | 0.08 | 2279 | 454 | 454 | 0.00454 | [0.0041, 0.0050] |
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| d=5 | 0.10 | 3352 | 815 | 815 | 0.00815 | [0.0076, 0.0087] |
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| d=5 | 0.15 | 7177 | 2660 | 2660 | 0.02660 | [0.0256, 0.0276] |
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| d=7 | 0.01 | 0 | 0 | 0 | 0.00000 | [0.0000, 0.0000] |
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| d=7 | 0.03 | 19 | 2 | 2 | 0.00002 | [0.0000, 0.0001] |
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| d=7 | 0.05 | 129 | 19 | 19 | 0.00019 | [0.0001, 0.0003] |
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| d=7 | 0.08 | 454 | 124 | 124 | 0.00124 | [0.0010, 0.0015] |
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| d=7 | 0.10 | 912 | 269 | 269 | 0.00269 | [0.0024, 0.0030] |
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| d=7 | 0.15 | 2695 | 1144 | 1144 | 0.01144 | [0.0108, 0.0121] |
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| d=9 | 0.01 | 0 | 0 | 0 | 0.00000 | [0.0000, 0.0000] |
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| d=9 | 0.03 | 3 | 0 | 0 | 0.00000 | [0.0000, 0.0000] |
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| d=9 | 0.05 | 20 | 3 | 3 | 0.00003 | [0.0000, 0.0001] |
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| d=9 | 0.08 | 134 | 37 | 37 | 0.00037 | [0.0003, 0.0005] |
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| d=9 | 0.10 | 306 | 97 | 97 | 0.00097 | [0.0008, 0.0012] |
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| d=9 | 0.15 | 1315 | 564 | 564 | 0.00564 | [0.0052, 0.0061] |
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**Key result**: Blossom LER == PyMatching LER at 0.00% delta across all d=3–9 points (repetition code).
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---
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## C. Independent PyPI Validation — Rotated Surface Code LER (100k shots/pt, seed 777)
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- For real-time decoding where speed is critical: Use FastUnionFind (0.9-1.3 µs per decode).
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- For research and accuracy-critical applications: Use Blossom or SparseBlossom (lower LER).
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- For hybrid approach: Use Neural Pre-Decoder to classify syndromes, then route to appropriate decoder.
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| dist | p | UF LER | Blossom LER | PyMatching LER | Blossom–PyM delta |
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|------|-----|--------|-------------|----------------|-------------------|
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| d=3 | 0.01 | 0.0110 | 0.0105 | 0.0107 | -0.02% |
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| d=3 | 0.03 | 0.0359 | 0.0317 | 0.0326 | -0.09% |
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| d=3 | 0.05 | 0.0649 | 0.0556 | 0.0574 | -0.18% |
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| d=3 | 0.08 | 0.1107 | 0.0903 | 0.0947 | -0.44% |
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| d=3 | 0.10 | 0.1414 | 0.1141 | 0.1201 | -0.60% |
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| d=3 | 0.15 | 0.2177 | 0.1732 | 0.1837 | -1.05% |
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| d=5 | 0.01 | 0.0123 | 0.0099 | 0.0098 | +0.01% |
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| d=5 | 0.03 | 0.0488 | 0.0325 | 0.0312 | +0.13% |
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| d=5 | 0.05 | 0.0929 | 0.0574 | 0.0551 | +0.23% |
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| d=5 | 0.08 | 0.1660 | 0.1002 | 0.0947 | +0.55% |
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| d=5 | 0.10 | 0.2137 | 0.1286 | 0.1213 | +0.73% |
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| d=5 | 0.15 | 0.3077 | 0.1995 | 0.1880 | +1.16% |
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| d=7 | 0.01 | 0.0107 | 0.0107 | 0.0105 | +0.02% |
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| d=7 | 0.03 | 0.0355 | 0.0326 | 0.0312 | +0.14% |
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| d=7 | 0.05 | 0.0694 | 0.0586 | 0.0548 | +0.38% |
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| d=7 | 0.08 | 0.1295 | 0.1012 | 0.0927 | +0.86% |
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| d=7 | 0.10 | 0.1716 | 0.1311 | 0.1205 | +1.06% |
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| d=7 | 0.15 | 0.2655 | 0.2046 | 0.1868 | +1.78% |
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**Key result**: Blossom within 1.78% of PyMatching at worst case (d=7, p=0.15, code-capacity model).
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Note: d=3/5/7 LER curves do not separate with distance under single-round code-capacity noise — this is a
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model property shared with PyMatching (Finding F-2). Use circuit-level Stim DEM for threshold curves.
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---
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## D. CPU Batch Throughput (repetition d=9, 20k shots primary run)
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| Decoder | Throughput |
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|---------|-----------|
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| UnionFindDecoder | ~1.06M shots/s |
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| BlossomDecoder | ~2.70M shots/s |
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| SparseBlossomDecoder | ~1.80M shots/s |
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| CPUBatchDecoder | ~0.34M shots/s |
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| BatchDecoder (parallel_batch_decode) | ~2.67M shots/s |
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| BatchDecoder (alias) | ~1.88M shots/s |
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---
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## E. Key Findings Summary
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1. **Blossom consistently outperforms UnionFind on LER** for surface codes (2.3× at d=3 p=0.01; 1.7× at d=5 p=0.01).
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2. **Blossom matches PyMatching exactly** on repetition code (0.00% delta), within 1.78% on surface code.
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3. **CUDA delivers 7× throughput** vs CPU batch at 100k shots on GTX 1660 Ti.
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4. **LookupTableDecoder is the fastest single-shot decoder tested** (~8.7 µs on rep d=5, exact on precomputed syndromes).
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5. **UnionFind ~3.1× less accurate than Blossom/MWPM** — expected speed/accuracy trade-off, both 100% valid.
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6. **Workbench latency**: Blossom rep d=5 → 277,778 dec/s, p50 3.60 µs, p99 11.61 µs, max 46.8 µs.
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### Recommendations
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- For real-time decoding where speed is critical: use FastUnionFindDecoder or CPUBatchDecoder.
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- For research and accuracy-critical applications: use BlossomDecoder or SparseBlossomDecoder.
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- For batch GPU throughput: use CUDABatchDecoder (check `is_available()` first; requires ≥4096 batch for GPU win).
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- For small codes with exact O(1) lookup: use LookupTableDecoder (table_size=64 for rep d=5).
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- For threshold curves: use circuit-level Stim DEM with BeliefMatching or qector_sinter_decoders() — not code-capacity.

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