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release: cut v0.5.0 (citation audit + classifier broadening + Codecov badge)
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CHANGELOG.md

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## [Unreleased]
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## [0.5.0] - 2026-04-19
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### Added
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- Streamlit Compare page now offers a **PQQA vs SA shootout** mode that
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as superseded by `problem.score_summary`. They are kept for backward
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compatibility but are no longer used internally.
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### Documentation
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- Repo-wide audit of the QQA / CPRA paper citations. Three places had
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silently swapped the QQA paper (Ichikawa & Arai, ICLR 2025) with the
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CPRA paper (Ichikawa & Iwashita, TMLR 2025) — fixed in
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`src/qqa/__init__.py` docstring, `notebooks/cra_pignn_example.ipynb`
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and `notebooks/cpra_pignn_example.ipynb`. Adopted the TMLR-published
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title for CPRA ("Continuous Parallel Relaxation for Finding Diverse
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Solutions in Combinatorial Optimization Problems"); the older
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arXiv-preprint title ("Continuous Tensor Relaxation …") is no longer
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used.
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- Added a Codecov coverage badge to `README.md` and a placeholder for
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the Zenodo DOI badge (uncommented and DOI-substituted as soon as the
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first release is minted).
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- Fixed `CITATION.cff` `preferred-citation` block: title now correctly
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matches the URL (both point at the QQA ICLR 2025 paper); arXiv:2409.02135
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added as an explicit identifier so citation tooling (Zenodo, ORCID,
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OpenAlex) resolves to the same artefact.
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### Infrastructure
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- `publish.yml` Trusted Publishing wired up end-to-end on PyPI:
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GitHub Actions environment `pypi` is now connected to the registered
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Trusted Publisher, so future tagged releases upload automatically
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without manual `twine` invocations.
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- Broadened PyPI classifiers in `pyproject.toml`
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(`Environment :: Console`, `Environment :: GPU :: NVIDIA CUDA`,
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`Intended Audience :: Education / Developers`, OS-specific tags,
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`Topic :: Mathematics / Physics`, `Typing :: Typed`) for better PyPI
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discoverability.
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## [0.4.0] - 2026-04-19
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pyproject.toml

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[project]
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name = "qqa"
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version = "0.5.0.dev0"
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version = "0.5.0"
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description = "Quasi-Quantum Annealing (QQA): a general-purpose GPU solver for combinatorial and spin-glass optimization, with PI-GNN/CPRA and a parallel SA baseline."
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readme = "README.md"
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requires-python = ">=3.10"

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