Learning Better Error Correction Codes with Hybrid Quantum-Assisted Machine Learning
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Quantum Physics arXiv:2601.08014 (quant-ph) [Submitted on 12 Jan 2026] Title:Learning Better Error Correction Codes with Hybrid Quantum-Assisted Machine Learning Authors:Yariv Yanay View a PDF of the paper titled Learning Better Error Correction Codes with Hybrid Quantum-Assisted Machine Learning, by Yariv Yanay View PDF HTML (experimental) Abstract:Quantum error correction is one of the fundamental building blocks of digital quantum computation.
The Quantum Lego formalism has introduced a systematic way of constructing new stabilizer codes out of basic lego-like building blocks, which in previous work we have used to generate improved error correcting codes via an automated reinforcement learning process. Here, we take this a step further and show the use of a hybrid classical-quantum algorithm. We combine classical reinforcement learning with calls to two commercial quantum devices to search for a stabilizer code to correct errors specific to the device, as well as an induced photon loss error. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2601.08014 [quant-ph] (or arXiv:2601.08014v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2601.08014 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yariv Yanay [view email] [v1] Mon, 12 Jan 2026 21:32:11 UTC (2,744 KB) Full-text links: Access Paper: View a PDF of the paper titled Learning Better Error Correction Codes with Hybrid Quantum-Assisted Machine Learning, by Yariv YanayView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-01 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?)