Hybrid Quantum Inception-Inspired Convolutional Neural Network for Image Classification

Hybrid Quantum Inception-Inspired Convolutional Neural Network for Image Classification | 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 Hybrid Quantum Inception-Inspired Convolutional Neural Network for Image Classification Wanqing Wu, Yvxiang Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6929117/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Quantum Neural Networks (QNNs) present a promising research direction in image recognition, demonstrating significant potential for various applications. However, QNNs with a fixed circuit topology structure are prone to encountering the barren plateaus during training when processing exponential number of qubits, which limit their performance and scalability. In this paper, a novel Hybrid Quantum-Classical Inception-inspired Convolutional Neural Networks (HQCNN) is proposed. The HQCNN enhances feature learning capability and mitigates the barren plateau issue by integrating quantum convolutional kernels with diverse circuit topologies across multiple channels. The architecture employs an amplitude encoding layer to map multi-channel classical data into quantum states, followed by diverse quantum convolutional kernels that extract feature representations. Furthermore, Hadamard gates are used to intergrate multi-channel features and incorporate quantum pooling to reduce feature dimensionality, with a fully connected layer mapping the quantum-enhancing features into class labels for classification tasks. Finally, by analyzing the influence of the circuit structures and quantum gate combinations on the models' performance, the optimal HQCNN architecture is constructed. Experimental results demonstrate that HQCNN achieves 98.5% accuracy on the MNIST-4 classification task and 92.5% accuracy on the MNIST-10 classification task, which indicates that the proposed model outperforms state-of-the-art HQCNN models and classical CNN in image classification with about 10% parameters. This study provides an effective approach for alleviating the barren plateau issue and designing multi-channel quantum convolutional neural networks for processing big classic data with fewer quantum resources. Hybrid Quantum Convolutional Neural Networks Quantum circuits Image classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Nov, 2025 Reviews received at journal 01 Nov, 2025 Reviews received at journal 24 Oct, 2025 Reviews received at journal 07 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers invited by journal 03 Sep, 2025 Editor assigned by journal 03 Sep, 2025 Submission checks completed at journal 21 Jun, 2025 First submitted to journal 19 Jun, 2025 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. 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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-6929117","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":474502423,"identity":"ab1e2f2a-e8bd-4d0a-b0d6-ad14aadef1b7","order_by":0,"name":"Wanqing Wu","email":"","orcid":"","institution":"Hebei University","correspondingAuthor":false,"prefix":"","firstName":"Wanqing","middleName":"","lastName":"Wu","suffix":""},{"id":474502424,"identity":"edcca009-8491-4cbc-ac9c-d462c3bd42d8","order_by":1,"name":"Yvxiang Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYLCCBCDmZ2Y+/IA0LZLtbGkGpNlkcJ5HQYIolfIzcswkHtTcsdt8mIfBgKHGJpqgFsYZOcYGCceeJW87zHvgAcOxtNwGQlqYJdISHySwHU42O8yXYMDYcJiwFjaJtIQDCf8OJxs38xhIEKWFRyL54IPEtsN2BszEapHgeXzYILHvcILEYWAgJxDjF/n2xDbJH98O2/P3Hz784EONDWEtDAIJYCoRrDKBoHIQ4D8ApuyJUjwKRsEoGAUjEwAA5YE+5TYbRWQAAAAASUVORK5CYII=","orcid":"","institution":"Hebei University","correspondingAuthor":true,"prefix":"","firstName":"Yvxiang","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-06-19 08:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6929117/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6929117/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86818917,"identity":"f4c7c0cc-7fef-4cfd-9442-a829c4b53f2d","added_by":"auto","created_at":"2025-07-16 01:52:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1974322,"visible":true,"origin":"","legend":"","description":"","filename":"HQICNN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6929117/v1_covered_933ea97f-e6ab-4b8b-9a7a-1c8878488846.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid Quantum Inception-Inspired Convolutional Neural Network for Image Classification","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hybrid Quantum Convolutional Neural Networks, Quantum circuits, Image classification","lastPublishedDoi":"10.21203/rs.3.rs-6929117/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6929117/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e Quantum Neural Networks (QNNs) present a promising research direction in image recognition, demonstrating significant potential for various applications. 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