Local surrogates for quantum machine learning

Local surrogates for quantum 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 Local surrogates for quantum machine learning Sreeraj Rajindran Nair, Christopher Ferrie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7324172/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Surrogates have been proposed as classical simulations of the pretrained quantum learning models, which are capable of mimicking the input-output relation inherent in the quantum model. Quantum hardware within this framework is used for training and for generating the classical surrogates. Inference is relegated to the classical surrogate, hence alleviating the extra quantum computational cost once training is done. Taking inspiration from interpretable models, we introduce a local surrogation protocol based on reuploading-type quantum learning models, including local quantum surrogates as cost-efficient intermediate quantum learning models. When the training and inference are only concerned with a subregion of the data space, deploying a local quantum surrogate offers qubit cost reductions and the downstream local classical surrogate achieves dequantization of the inference phase. Several numerical experiments are presented. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 20 Feb, 2026 Editor assigned by journal 29 Oct, 2025 Submission checks completed at journal 10 Aug, 2025 First submitted to journal 08 Aug, 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. 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-7324172","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594562776,"identity":"ce27def1-e3b9-4245-a483-8afe19943e79","order_by":0,"name":"Sreeraj Rajindran Nair","email":"data:image/png;base64,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","orcid":"","institution":"University of Technology Sydney","correspondingAuthor":true,"prefix":"","firstName":"Sreeraj","middleName":"Rajindran","lastName":"Nair","suffix":""},{"id":594562778,"identity":"5618ee3a-38d1-4739-a2e0-08c7040371f0","order_by":1,"name":"Christopher Ferrie","email":"","orcid":"","institution":"University of Technology Sydney","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Ferrie","suffix":""}],"badges":[],"createdAt":"2025-08-08 06:38:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7324172/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7324172/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103505583,"identity":"eccb2ccd-bfaa-4275-b4a7-ac2089e37a0c","added_by":"auto","created_at":"2026-02-26 13:32:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1199438,"visible":true,"origin":"","legend":"","description":"","filename":"npjlocalsurrogates.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7324172/v1_covered_44874e16-8720-4ddf-abe9-b46b6482432d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Local surrogates for quantum machine learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"quantum-machine-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"qumi","sideBox":"Learn more about [Quantum Machine Intelligence](http://link.springer.com/journal/42484)","snPcode":"42484","submissionUrl":"https://submission.nature.com/new-submission/42484/3","title":"Quantum Machine Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7324172/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7324172/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Surrogates have been proposed as classical simulations of the pretrained quantum learning models, which are capable of mimicking the input-output relation inherent in the quantum model. 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