Learning quantum phase transition in parametrized quantum circuits with an attention mechanism

Learning quantum phase transition in parametrized quantum circuits with an attention mechanism | 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 Article Learning quantum phase transition in parametrized quantum circuits with an attention mechanism Zhang-Qi Yin, Li Xin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7019666/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Learning many-body quantum states and quantum phase transitions remains a major challenge in quantum many-body physics. Classical machine learning methods offer certain advantages in addressing these difficulties. In this work, we propose a novel framework that bypasses the need to measure physical observables by directly learning the parameters of parameterized quantum circuits. By integrating the attention mechanism from large language models (LLMs) with a variational autoencoder (VAE), we efficiently capture hidden correlations within the circuit parameters. These correlations allow us to extract information about quantum phase transitions in an unsupervised manner. Moreover, our VAE acts as a classical representation of parameterized quantum circuits and the corresponding many-body quantum states, enabling the efficient generation of quantum states associated with specific phases. We apply our framework to a variety of quantum systems and demonstrate its broad applicability, with particularly strong performance in identifying topological quantum phase transitions. Physical sciences/Physics/Quantum physics Physical sciences/Mathematics and computing/Statistics Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review 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-7019666","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":488744932,"identity":"026d021a-3a30-4d04-812b-bc32ee15f56c","order_by":0,"name":"Zhang-Qi Yin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYJCCAwwVB3hADAkStJw5wMNDkhYGxrYDDMRrkW/PMTxcOO+OjD0D88HbPAx2eYQt6HljcHjmtmdAh7ElW/MwJBcT1MIskWNwmHfbYaAWHjNpHoYDiQ2EtLCBtcwBaeH/RpwWHrCWBrAtbMRpkeB5VnCY5xjQL4fZjC3nGCQT1iLfnrz5M0/NHXv29uaHN95U2BHWwsCQYQChmUGEAWH1QJD+gChlo2AUjIJRMIIBACbSNZ4zKSY/AAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Zhang-Qi","middleName":"","lastName":"Yin","suffix":""},{"id":488744933,"identity":"7ff1314e-68ca-4395-b4dd-de03444adbb1","order_by":1,"name":"Li Xin","email":"","orcid":"","institution":"Beijing Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Xin","suffix":""}],"badges":[],"createdAt":"2025-07-01 11:17:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7019666/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7019666/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92275682,"identity":"81cbada2-6c6d-4a5d-9649-9bf0a0c42075","added_by":"auto","created_at":"2025-09-26 15:28:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8915484,"visible":true,"origin":"","legend":"Article File","description":"","filename":"manuscriptmainnsupp.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7019666/v1_covered_531feb8b-9dc0-4270-ad05-9e94eca8436a.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Learning quantum phase transition in parametrized quantum circuits with an attention mechanism","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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7019666/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7019666/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Learning many-body quantum states and quantum phase transitions remains a major challenge in quantum many-body physics. 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