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AlchemQ: Proof-Carrying Quantum Circuit Optimization with Per-Result Equivalence Certificates

Adam Laabs
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Certificates are self-contained and tamper-evident: canonical gate-canon-v1 hashes, measured residuals, tri-state verdicts (certified/rejected/inconclusive), and versioned phase-note schemas for cross-platform reproducibility. The agent aggregates three fuzzy t-norms, cannot return an uncertified circuit, and since v0.4 guarantees no componentwise regression against the original. Both were fixed; all artifact sets validate 400/400 on both platforms. A pilot run on IBM Heron r2 gives a certified circuit 78% shallower with 65% fewer two-qubit gates; output quality favors it on all three metrics but is not significant at 1024 shots.
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Quantum Physics arXiv:2609.19160 (quant-ph) [Submitted on 24 Aug 2026] Title:AlchemQ: Proof-Carrying Quantum Circuit Optimization with Per-Result Equivalence Certificates Authors:Adam Laabs View a PDF of the paper titled AlchemQ: Proof-Carrying Quantum Circuit Optimization with Per-Result Equivalence Certificates, by Adam Laabs View PDF HTML (experimental) Abstract:We present AlchemQ v0.5, a proof-of-concept system that couples an untrusted beam-search optimizer with a machine-checkable per-result certification layer and a versioned certificate protocol (0.2.0), so that every optimized circuit ships with a verifiable artifact rather than a bare claim. The certifier proves equivalence up to global phase by ZX-calculus full reduction, with a numeric-tensor fallback based on the optimal Hilbert-Schmidt overlap. Certificates are self-contained and tamper-evident: canonical gate-canon-v1 hashes, measured residuals, tri-state verdicts (certified/rejected/inconclusive), and versioned phase-note schemas for cross-platform reproducibility. The agent aggregates three fuzzy t-norms, cannot return an uncertified circuit, and since v0.4 guarantees no componentwise regression against the original. On a benchmark of 100 circuits, all 400 optimizations terminate without error, every returned circuit is certified, every mutation is detected, and a 2998-test suite passes on two platforms. The PyZX baseline is strong (21.4% mean T-count reduction over 82 circuits) and the agent is strictly better on 9/100; the three t-norms return identical circuits on all 100 standard instances, diverging only on 4/38 of an adversarial suite. Two case studies are new: a false negative root-caused to a pivot-normalization bug in PyZX's compare_tensors (pivot 4.7e-9; the optimal-overlap residual is 7.4e-11), and eight certificates rejected on macOS due to BLAS-dependent floats in phase_note. Both were fixed; all artifact sets validate 400/400 on both platforms. A pilot run on IBM Heron r2 gives a certified circuit 78% shallower with 65% fewer two-qubit gates; output quality favors it on all three metrics but is not significant at 1024 shots. We release the certificate specification and a standalone reference verifier (Apache-2.0) with data and scripts; the engine is proprietary. Comments: Subjects: Quantum Physics (quant-ph) ACM classes: F.3.1; D.2.4; I.1.2 Cite as: arXiv:2609.19160 [quant-ph] (or arXiv:2609.19160v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2609.19160 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Adam Laabs [view email] [v1] Mon, 24 Aug 2026 10:17:57 UTC (36 KB) Full-text links: Access Paper: View a PDF of the paper titled AlchemQ: Proof-Carrying Quantum Circuit Optimization with Per-Result Equivalence Certificates, by Adam LaabsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-09 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

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