Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis
Summarize this article with:
Quantum Physics arXiv:2602.15146 (quant-ph) [Submitted on 16 Feb 2026] Title:Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis Authors:Lukas Theissinger, Thore Gerlach, David Berghaus, Christian Bauckhage View a PDF of the paper titled Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis, by Lukas Theissinger and 3 other authors View PDF HTML (experimental) Abstract:Quantum unitary synthesis addresses the problem of translating abstract quantum algorithms into sequences of hardware-executable quantum gates. Solving this task exactly is infeasible in general due to the exponential growth of the underlying combinatorial search space. Existing approaches suffer from misaligned optimization objectives, substantial training costs and limited generalization across different qubit counts. We mitigate these limitations by using supervised learning to approximate the minimum description length of residual unitaries and combining this estimate with stochastic beam search to identify near optimal gate sequences. Our method relies on a lightweight model with zero-shot generalization, substantially reducing training overhead compared to prior baselines. Across multiple benchmarks, we achieve faster wall-clock synthesis times while exceeding state-of-the-art methods in terms of success rate for complex circuits. Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG) Cite as: arXiv:2602.15146 [quant-ph] (or arXiv:2602.15146v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.15146 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Thore Gerlach [view email] [v1] Mon, 16 Feb 2026 19:43:43 UTC (531 KB) Full-text links: Access Paper: View a PDF of the paper titled Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis, by Lukas Theissinger and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-02 Change to browse by: cs cs.LG 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?) Links to Code Toggle Papers with Code (What is Papers with Code?) 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?)