Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction

[2506.11552] Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantum Physics arXiv:2506.11552 (quant-ph) [Submitted on 13 Jun 2025 ( v1 ), last revised 2 Sep 2026 (this version, v3)] Title: Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction Authors: Nico Meyer , Christopher Mutschler , Andreas Maier , Daniel D. Scherer View a PDF of the paper titled Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction, by Nico Meyer and 3 other authors View PDF HTML (experimental) Abstract: Quantum error correction is crucial for protecting quantum information against decoherence. Traditional codes like the surface code require substantial overhead, making them impractical for near-term, early fault-tolerant devices. We propose a novel objective function for tailoring error correction codes to specific noise structures by maximizing the distinguishability between quantum states after a noise channel, ensuring efficient recovery operations. We formalize this concept with the distinguishability loss function, serving as a machine learning objective to discover resource-efficient encoding circuits optimized for given noise characteristics. We implement this methodology using variational techniques, termed variational quantum error correction (VarQEC). Our approach yields codes with desirable theoretical and practical properties and outperforms standard codes in various scenarios. We also provide proof-of-concept demonstrations on IBM and IQM hardware devices, highlighting the practical relevance of our procedure. Comments: 47 pages, 24 figures, 8 tables Subjects: Quantum Physics (quant-ph) ; Machine Learning (cs.LG) Cite as: arXiv:2506.11552 [quant-ph] (or arXiv:2506.11552v3 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2506.11552 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Quantum Sci. Technol. 11, 045036 (2026) Related DOI : https://doi.org/10.1088/2058-9565/ae98b1 Focus to learn more DOI(s) linking to related resources Submission history From: Nico Meyer [ view email ] [v1] Fri, 13 Jun 2025 08:02:37 UTC (584 KB) [v2] Fri, 10 Apr 2026 07:58:08 UTC (554 KB) [v3] Wed, 2 Sep 2026 06:44:44 UTC (799 KB) Full-text links: Access Paper: View a PDF of the paper titled Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction, by Nico Meyer and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2025-06 Change to browse by: cs cs.LG References & Citations INSPIRE HEP NASA ADS Google 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? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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