Systematic Literature Review of Quantum Convolutional Neural Networks and Circuit Optimization

Systematic Literature Review of Quantum Convolutional Neural Networks and Circuit Optimization | 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 Literature Review of Quantum Convolutional Neural Networks and Circuit Optimization Aksultan Mukhanbet, Paulo Trigo, Beimbet Daribayev This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8945562/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 Quantum convolutional neural networks (QCNNs) are gaining increasing attention as a new class of models in quantum machine learning, especially for image classification and other computer vision tasks. Recent advances include the development of hybrid classical quantum models, novel quantum coding schemes, and innovative circuit architectures that enable efficient processing of high-dimensional visual data under the constraints of Noisy Intermediate-Scale Quantum (NISQ) devices. Despite the progress in QCNN design, practical deployment of these models is hampered by the complexity of quantum circuit implementation. Circuit depth, gate count, qubit connectivity, and hardware noise pose significant challenges to scalability and performance. As a result, quantum circuit optimization has become a critical area of research aimed at improving the efficiency of QCNNs, reducing quantum resource requirements, and improving classification accuracy. Among various approaches, heuristic and metaheuristic optimization methods such as genetic algorithms and evolutionary strategies have shown notable promise in addressing the high-dimensional and non-convex nature of the optimization space. This study presents a systematic literature review of 40 key research papers published between 2014 and 2025, 1 selected through strict inclusion and quality assessment criteria. The review covers key aspects of QCNN development, including architectures, coding strategies, and application areas, followed by an in-depth analysis of optimization method-ologies, objectives, and evaluation metrics. The results reveal emerging trends in hybrid quantum-classical integration, the widespread use of metaheuristic algorithms for circuit tuning, and the importance of multi-objective optimization frameworks tailored to the constraints of quantum computing. This review highlights critical research gaps and outlines future directions for advancing QCNN models and their practical implementation on quantum devices in the near future. Full Text Additional Declarations No competing interests reported. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8945562","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":602234986,"identity":"db1aeb63-3b71-4361-a440-7856593d00f8","order_by":0,"name":"Aksultan Mukhanbet","email":"data:image/png;base64,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","orcid":"","institution":"Al-Farabi Kazakh National University","correspondingAuthor":true,"prefix":"","firstName":"Aksultan","middleName":"","lastName":"Mukhanbet","suffix":""},{"id":602234987,"identity":"eb20c9be-881e-4ed8-9196-4871266bf1b1","order_by":1,"name":"Paulo Trigo","email":"","orcid":"","institution":"Instituto Superior De Engenharia De Lisboa - Isel","correspondingAuthor":false,"prefix":"","firstName":"Paulo","middleName":"","lastName":"Trigo","suffix":""},{"id":602234988,"identity":"2a0e197a-cc59-458b-9644-caf670f249ae","order_by":2,"name":"Beimbet Daribayev","email":"","orcid":"","institution":"Shakarim State University Of Semey","correspondingAuthor":false,"prefix":"","firstName":"Beimbet","middleName":"","lastName":"Daribayev","suffix":""}],"badges":[],"createdAt":"2026-02-23 09:53:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8945562/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8945562/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108181177,"identity":"e6610922-6caf-4329-9508-f8561b8e6f27","added_by":"auto","created_at":"2026-04-30 08:58:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1394371,"visible":true,"origin":"","legend":"","description":"","filename":"ArticleV4new.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8945562/v1_covered_d4a6f1fb-3ca5-4540-bd61-f3e2f471a477.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Systematic Literature Review of Quantum Convolutional Neural Networks and Circuit Optimization","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8945562/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8945562/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Quantum convolutional neural networks (QCNNs) are gaining increasing attention as a new class of models in quantum machine learning, especially for image classification and other computer vision tasks. Recent advances include the development of hybrid classical quantum models, novel quantum coding schemes, and innovative circuit architectures that enable efficient processing of high-dimensional visual data under the constraints of Noisy Intermediate-Scale Quantum (NISQ) devices. Despite the progress in QCNN design, practical deployment of these models is hampered by the complexity of quantum circuit implementation. Circuit depth, gate count, qubit connectivity, and hardware noise pose significant challenges to scalability and performance. As a result, quantum circuit optimization has become a critical area of research aimed at improving the efficiency of QCNNs, reducing quantum resource requirements, and improving classification accuracy. Among various approaches, heuristic and metaheuristic optimization methods such as genetic algorithms and evolutionary strategies have shown notable promise in addressing the high-dimensional and non-convex nature of the optimization space. 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