Systematic Review of Artificial Intelligence Decoders for Topological Quantum Error Correction

Systematic Review of Artificial Intelligence Decoders for Topological Quantum Error Correction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Systematic Review of Artificial Intelligence Decoders for Topological Quantum Error Correction Elliot Amponsah, Justice Owusu Agyemang, Godfred Manu Addo Boakye, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9435708/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Efficient, low-latency decoding of topological quantum error correction (QEC) codes is a central challenge on the road to fault-tolerant quantum computing. This systematic review synthesizes findings from 108 peer-reviewed studies (2017–2026), selected per the PRISMA 2020 framework, evaluating artificial intelligence (AI) and machine learning (ML) architectures for decoding surface, toric, color, and related topological stabilizer codes used in near-term and fault-tolerant quantum computing systems. We find that AI decoders frequently outperform classical baselines under correlated and hardware-realistic noise: graph neural networks achieve error thresholds up to \(\:{p}_{th}\approx\:13.8\%\) , transformer-based models such as AlphaQubit reduce logical error rates by 24–31% over minimum weight perfect matching (MWPM) on distance-3 and distance-5 surface codes benchmarked on Google’s Sycamore superconducting processor. Meanwhile, classical co-processor implementations using field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs) reach inference latencies as low as 2.3 ns. However, robustness to calibration drift and the ability to generalize across different quantum hardware platforms remain open challenges. This review provides a structured decoder taxonomy, comparative performance tables, and an evidence-based roadmap for deploying AI-enhanced QEC in utility-scale quantum systems. AI decoder deep reinforcement learning fault-tolerant quantum computing graph neural networks logical error rate MWPM quantum error correction surface code topological codes transformer Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryTableDataExtraction.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9435708","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":624176820,"identity":"4aba41c3-2719-49d4-b35d-e3d19cbb0199","order_by":0,"name":"Elliot 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deep reinforcement learning, fault-tolerant quantum computing, graph neural networks, logical error rate, MWPM, quantum error correction, surface code, topological codes, transformer","lastPublishedDoi":"10.21203/rs.3.rs-9435708/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9435708/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEfficient, low-latency decoding of topological quantum error correction (QEC) codes is a central challenge on the road to fault-tolerant quantum computing. This systematic review synthesizes findings from 108 peer-reviewed studies (2017\u0026ndash;2026), selected per the PRISMA 2020 framework, evaluating artificial intelligence (AI) and machine learning (ML) architectures for decoding surface, toric, color, and related topological stabilizer codes used in near-term and fault-tolerant quantum computing systems. We find that AI decoders frequently outperform classical baselines under correlated and hardware-realistic noise: graph neural networks achieve error thresholds up to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{th}\\approx\\:13.8\\%\\)\u003c/span\u003e\u003c/span\u003e, transformer-based models such as AlphaQubit reduce logical error rates by 24\u0026ndash;31% over minimum weight perfect matching (MWPM) on distance-3 and distance-5 surface codes benchmarked on Google\u0026rsquo;s Sycamore superconducting processor. 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