Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning

Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning | 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 Research Article Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning Fan Fan, Yilei Shi, Mihai Datcu, Bertrand Le Saux, Luigi Iapichino, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8619748/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Machine Learning (ML) has been widely applied across numerous domains due to its ability to automatically identify informative patterns from data for various tasks. The availability of large-scale data and advanced computational power enables the development of sophisticated models and training strategies, leading to state-of-the-art performance, but it also introduces substantial challenges. Quantum Computing (QC), which exploits quantum mechanisms for computation, has attracted growing attention and significant global investment as it may address these challenges. Consequently, Quantum Machine Learning (QML), the integration of these two fields, has received increasing interest, with a notable rise in related studies in recent years. We are motivated to review these existing contributions regarding quantum circuit-based learning models for classical data analysis and highlight the identified potentials and challenges of this technique. Specifically, we focus not only on QML models, both kernel-based and neural network-based, but also on recent explorations of their integration with classical machine learning layers within hybrid frameworks. Moreover, we examine both theoretical analysis and empirical findings to better understand their capabilities, and we also discuss the efforts on noise-resilient and hardware-efficient QML that could enhance its practicality under current hardware limitations. In addition, we cover several emerging paradigms for advanced quantum circuit design and highlight the adaptability of QML across representative application domains. This study aims to provide an overview of the contributions made to bridge quantum computing and machine learning, offering insights and guidance to support its future development and pave the way for broader adoption in the coming years. Quantum Computing Machine Learning Quantum Circuit Parameterized Quantum Circuit Full Text Additional Declarations Competing interest reported. The research by F. F. and X.X.Z. are funded by German Federal Ministry for Economic Affairs and Climate Action in the framework of the "national center of excellence ML4Earth" (grant number: 50EE2201C) and by Munich Center for Machine Learning. The work of X.X.Z. is also supported by ESA $\Phi$-lab. The research by M.D. is partially funded by EuroQHPC-I and ESA $\Phi$-lab. The research by L.I. is partially funded in the context of the Munich Quantum Valley, which is supported by the Bavarian state government with funds from the Hightech Agenda Bayern Plus. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviews received at journal 16 Mar, 2026 Reviews received at journal 25 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviewers agreed at journal 14 Feb, 2026 Reviewers agreed at journal 14 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers invited by journal 11 Feb, 2026 Editor assigned by journal 19 Jan, 2026 Submission checks completed at journal 17 Jan, 2026 First submitted to journal 16 Jan, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-8619748","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592105923,"identity":"1455ad82-da0a-4d87-b425-6267fb5eaa58","order_by":0,"name":"Fan Fan","email":"","orcid":"","institution":"Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Fan","suffix":""},{"id":592105924,"identity":"7daed1b7-706b-4eb7-961c-e0c8f7663d4c","order_by":1,"name":"Yilei Shi","email":"","orcid":"","institution":"Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Yilei","middleName":"","lastName":"Shi","suffix":""},{"id":592105925,"identity":"57b0eb75-efef-4dcc-b16e-f4bb831a0207","order_by":2,"name":"Mihai Datcu","email":"","orcid":"","institution":"Polytechnic University of Bucharest","correspondingAuthor":false,"prefix":"","firstName":"Mihai","middleName":"","lastName":"Datcu","suffix":""},{"id":592105926,"identity":"1e26df1f-5de6-441e-af5a-3506a61ce97b","order_by":3,"name":"Bertrand Le Saux","email":"","orcid":"","institution":"European Commission","correspondingAuthor":false,"prefix":"","firstName":"Bertrand","middleName":"Le","lastName":"Saux","suffix":""},{"id":592105927,"identity":"e1b5dee6-6291-443c-8368-541048996401","order_by":4,"name":"Luigi Iapichino","email":"","orcid":"","institution":"Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ)","correspondingAuthor":false,"prefix":"","firstName":"Luigi","middleName":"","lastName":"Iapichino","suffix":""},{"id":592105929,"identity":"87ade6b4-0bf8-445a-b413-6b6caa7d2f65","order_by":5,"name":"Francesca Bovolo","email":"","orcid":"","institution":"Fondazione Bruno Kessler","correspondingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"","lastName":"Bovolo","suffix":""},{"id":592105932,"identity":"525c2189-d1dd-4d5c-a106-ef3f5218a51a","order_by":6,"name":"Silvia Liberata Ullo","email":"","orcid":"","institution":"University of Sannio","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"Liberata","lastName":"Ullo","suffix":""},{"id":592105933,"identity":"2ba96f8b-adb9-44a5-b728-d915772cac87","order_by":7,"name":"Xiao Xiang Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDACZhBhAGayMTBUMCC4RGo5Y0CEFiTAxsDYRoQWg+PMDx/zFNjlyTuwP3vMO+9P4toG5o0P8GmRbGYzNpxhkFxseIAh3Zh3m0HitgNsxXit4WdmMJP4YMCcuLGB4Zg0RAuPmQRe9zOzf5NIMKgHamFsk+adA9Zi/gO/LTwgWw4nzmdgZpPmbYDYgk8H0C88xUC/HE/cwMzGJjnnmLHxtsNsxXgdZnD++MbHPH+qE+e3tz+TeFMjJ7vtePPGD3itges9DGMxE6UeCOQbiFU5CkbBKBgFIw4AAN45QgsMchmfAAAAAElFTkSuQmCC","orcid":"","institution":"Technical University of Munich","correspondingAuthor":true,"prefix":"","firstName":"Xiao","middleName":"Xiang","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2026-01-16 14:12:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8619748/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8619748/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102963402,"identity":"3a94df7f-7891-4103-b100-273e05d37b3e","added_by":"auto","created_at":"2026-02-19 04:17:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4419260,"visible":true,"origin":"","legend":"","description":"","filename":"QuantumCircuitbasedLearning1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8619748/v1_covered_2afe31c3-511d-46d9-8dae-bc092f598955.pdf"}],"financialInterests":"Competing interest reported. 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