Advancing Machine Learning Applications in Quantum Few-Body Systems

Advancing Machine Learning Applications in Quantum Few-Body Systems | 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 Advancing Machine Learning Applications in Quantum Few-Body Systems Jin Ziqi, Paolo Recchia, Mario Gattobigio This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9109507/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract This paper presents a general neural network framework for solving quantum few-body systems, extending prior methods to handle diverse particle masses, interaction types, and system configurations. Our architecture, which combines an adaptive step size with the Metropolis-Adjusted Langevin Algorithm for Monte Carlo sampling, accurately approximates the ground-state wave functions of systems featuring harmonic confinement, Gaussian two-body interactions, and including three-body forces. In ten-particle systems, it achieves lower relative energy errors (with respect to the reference values) than previous machine-learning methods. Leveraging GPU-accelerated computation, the method scales favorably with system size while maintaining robust convergence, reduced hyperparameter sensitivity, and stable training. Beyond accurate energy estimation, the model captures spatial distributions and correlation structures, offering physical insights about inter-particle structure. By unifying applicability across identical and nonidentical particles, the proposed approach establishes a versatile computational tool for exploring complex few-body quantum systems, with significant implications for advancing computational models in few-body quantum systems. Neural Network Quantum State Few-body systems Monte Carlo Metropolis-Adjusted Langevin Sampling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers invited by journal 20 Mar, 2026 Editor assigned by journal 20 Mar, 2026 Submission checks completed at journal 18 Mar, 2026 First submitted to journal 12 Mar, 2026 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. 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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-9109507","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609443505,"identity":"b0f177f6-75d3-4cdc-ae3c-ddd2abc8819d","order_by":0,"name":"Jin Ziqi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYDACZgaGAww8Egz8cJEDxGqRbCBaCwwYwFUS0iLfzrzxAIOMRZ7xjQS2xzx/GOT4biTg18LYzFYAclix2Y0EdmPeNgZjSUJamJl5DEBaErcBbZHmbWBI3EBICxtMy+YZQC1Ah9UT1MID07JBAqSFjSHBgJAWCWagXxKAWmacedgmObdNwnDmmQf4tcj3H9784WNPXWJ/e/IxiTd/bOT5jhOwBQgMGBJ7QDRjA8hWgsohWhh+EKVwFIyCUTAKRioAAI1XPB+orcunAAAAAElFTkSuQmCC","orcid":"","institution":"National University of Singapore","correspondingAuthor":true,"prefix":"","firstName":"Jin","middleName":"","lastName":"Ziqi","suffix":""},{"id":609443506,"identity":"ea979de6-2552-4fe3-ac6c-256aceecba21","order_by":1,"name":"Paolo Recchia","email":"","orcid":"","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Paolo","middleName":"","lastName":"Recchia","suffix":""},{"id":609443507,"identity":"0f99607b-e4b2-402c-9e18-f8f66839117b","order_by":2,"name":"Mario Gattobigio","email":"","orcid":"","institution":"Institut de Physique de Nice","correspondingAuthor":false,"prefix":"","firstName":"Mario","middleName":"","lastName":"Gattobigio","suffix":""}],"badges":[],"createdAt":"2026-03-13 02:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9109507/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9109507/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105565403,"identity":"d4d76a23-4f46-4875-a17e-15a0b1a59c46","added_by":"auto","created_at":"2026-03-27 12:53:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1170848,"visible":true,"origin":"","legend":"","description":"","filename":"advancingfewbodyNNsubm1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9109507/v1_covered_ae07ae45-9561-495a-b0b9-286adad3f2a0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advancing Machine Learning Applications in Quantum Few-Body Systems","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"few-body-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fbsy","sideBox":"Learn more about [Few-Body Systems](http://link.springer.com/journal/601)","snPcode":"601","submissionUrl":"https://submission.nature.com/new-submission/601/3","title":"Few-Body Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Neural Network Quantum State, Few-body systems, Monte Carlo, Metropolis-Adjusted Langevin Sampling","lastPublishedDoi":"10.21203/rs.3.rs-9109507/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9109507/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper presents a general neural network framework for solving quantum few-body systems, extending prior methods to handle diverse particle masses, interaction types, and system configurations. 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