Adaptive Quantum Kernel Optimization for Scalable and Noise-Resilient Nonlinear Pattern Recognition

Adaptive Quantum Kernel Optimization for Scalable and Noise-Resilient Nonlinear Pattern Recognition | 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 Adaptive Quantum Kernel Optimization for Scalable and Noise-Resilient Nonlinear Pattern Recognition Asif Nurul Hakim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8055313/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 kernel methods have emerged as a powerful paradigm for extending classical machine learning into high-dimensional quantum Hilbert spaces, offering potential advantages in complex, nonlinear pattern recognition. However, their performance on near-term quantum devices remains constrained by circuit depth, noise sensitivity, and suboptimal parameterization of quantum feature maps. This paper introduces an Adaptive Quantum Kernel Optimization (AQKO) framework that integrates circuit-level adaptability, hybrid optimization, and regularization to enhance both expressivity and robustness in quantum kernel learning. The proposed method employs Adaptive Quantum Feature Maps (AQFM) that dynamically adjust rotation and phase parameters through a hybrid gradient-based optimization routine, reducing overfitting and improving generalization. Furthermore, a regularized quantum kernel objective is formulated to stabilize eigenvalue spectra under Noisy Intermediate-Scale Quantum (NISQ) conditions. Extensive experiments on nonlinear benchmark datasets — including Moons , Circles , and Iris — demonstrate that AQKO achieves up to 8.5% higher classification accuracy and 32% lower circuit depth compared to static quantum kernels. Hardware validation using IBM’s ibmq_quito device confirms AQKO’s resilience under depolarizing noise (p = 0.02) with a 41% error mitigation efficiency gain. These findings highlight AQKO’s potential as a scalable and noise-tolerant foundation for real-world quantum-enhanced learning, bridging the gap between theoretical quantum kernel models and practical NISQ implementations. Computer Architecture and Engineering Quantum Kernel Learning Adaptive Quantum Feature Maps Hybrid Quantum–Classical Optimization Nonlinear Pattern Recognition NISQ Noise Robustness Full Text Additional Declarations The authors declare no competing interests. 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. 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Nonlinear Pattern Recognition\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Nantong University","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":"Quantum Kernel Learning, Adaptive Quantum Feature Maps, Hybrid Quantum–Classical Optimization, Nonlinear Pattern Recognition, NISQ Noise Robustness","lastPublishedDoi":"10.21203/rs.3.rs-8055313/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8055313/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eQuantum kernel methods have emerged as a powerful paradigm for extending classical machine learning into high-dimensional quantum Hilbert spaces, offering potential advantages in complex, nonlinear pattern recognition. However, their performance on near-term quantum devices remains constrained by circuit depth, noise sensitivity, and suboptimal parameterization of quantum feature maps. This paper introduces an Adaptive Quantum Kernel Optimization (AQKO) framework that integrates circuit-level adaptability, hybrid optimization, and regularization to enhance both expressivity and robustness in quantum kernel learning. The proposed method employs Adaptive Quantum Feature Maps (AQFM) that dynamically adjust rotation and phase parameters through a hybrid gradient-based optimization routine, reducing overfitting and improving generalization. Furthermore, a regularized quantum kernel objective is formulated to stabilize eigenvalue spectra under Noisy Intermediate-Scale Quantum (NISQ) conditions. Extensive experiments on nonlinear benchmark datasets \u0026mdash; including \u003cem\u003eMoons\u003c/em\u003e, \u003cem\u003eCircles\u003c/em\u003e, and \u003cem\u003eIris\u003c/em\u003e \u0026mdash; demonstrate that AQKO achieves up to 8.5% higher classification accuracy and 32% lower circuit depth compared to static quantum kernels. Hardware validation using IBM\u0026rsquo;s ibmq_quito device confirms AQKO\u0026rsquo;s resilience under depolarizing noise (p\u0026thinsp;=\u0026thinsp;0.02) with a 41% error mitigation efficiency gain. These findings highlight AQKO\u0026rsquo;s potential as a scalable and noise-tolerant foundation for real-world quantum-enhanced learning, bridging the gap between theoretical quantum kernel models and practical NISQ implementations.\u003c/p\u003e","manuscriptTitle":"Adaptive Quantum Kernel Optimization for Scalable and Noise-Resilient Nonlinear Pattern Recognition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 04:02:46","doi":"10.21203/rs.3.rs-8055313/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"e2e96bef-3965-434b-ad38-31c632fdadf9","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57603124,"name":"Computer Architecture and Engineering"}],"tags":[],"updatedAt":"2025-11-10T04:02:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 04:02:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8055313","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8055313","identity":"rs-8055313","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}