Continuous-variable Quantum Boltzmann Machine

Continuous-variable Quantum Boltzmann Machine | 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 Continuous-variable Quantum Boltzmann Machine Shikha Bangar, Leanto Sunny, Kubra Yeter-Aydeniz, George Siopsis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4485601/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2025 Read the published version in Quantum Machine Intelligence → Version 1 posted 7 You are reading this latest preprint version Abstract We propose a continuous-variable quantum Boltzmann machine (CVQBM) using a powerful energy-based neural network. It can be realized experimentally on a continuous-variable (CV) photonic quantum computer. We used a CV quantum imaginary time evolution (QITE) algorithm to prepare the essential thermal state and then designed the CVQBM to proficiently generate continuous probability distributions. We applied our method to both classical and quantum data. Using real-world classical data, such as synthetic aperture radar (SAR) images, we generated probability distributions. For quantum data, we used the output of CV quantum circuits. We obtained high fidelity and low Kuller-Leibler (KL) divergence showing that our CVQBM learns distributions from given data well and generates data sampling from that distribution efficiently. We also discussed the experimental feasibility of our proposed CVQBM. Our method can be applied to a wide range of real-world problems by choosing an appropriate target distribution (corresponding to, e.g., SAR images, medical images, and risk management in finance). Moreover, our CVQBM is versatile and could be programmed to perform tasks beyond generation, such as anomaly detection. quantum Boltzmann machine continuous variable quantum machine learning photonic quantum computer SAR images Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2025 Read the published version in Quantum Machine Intelligence → Version 1 posted Editorial decision: Revision requested 05 Dec, 2024 Reviews received at journal 15 Sep, 2024 Reviewers agreed at journal 15 Aug, 2024 Reviewers invited by journal 07 Aug, 2024 Editor assigned by journal 07 Jul, 2024 Submission checks completed at journal 30 May, 2024 First submitted to journal 27 May, 2024 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-4485601","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":308499518,"identity":"60efad3c-4751-468d-adf1-2c4f014fb0e5","order_by":0,"name":"Shikha Bangar","email":"data:image/png;base64,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","orcid":"","institution":"University of Tennessee at Knoxville","correspondingAuthor":true,"prefix":"","firstName":"Shikha","middleName":"","lastName":"Bangar","suffix":""},{"id":308499521,"identity":"330967b4-329b-46db-a9d6-e5f6abbce962","order_by":1,"name":"Leanto Sunny","email":"","orcid":"","institution":"University of Tennessee at Knoxville","correspondingAuthor":false,"prefix":"","firstName":"Leanto","middleName":"","lastName":"Sunny","suffix":""},{"id":308499523,"identity":"8f1bd4b2-0dba-43e7-8ba5-73934b94c946","order_by":2,"name":"Kubra Yeter-Aydeniz","email":"","orcid":"","institution":"Mitre (United States)","correspondingAuthor":false,"prefix":"","firstName":"Kubra","middleName":"","lastName":"Yeter-Aydeniz","suffix":""},{"id":308499526,"identity":"b80efe0c-4ea4-4a40-abee-4d219db069e0","order_by":3,"name":"George Siopsis","email":"","orcid":"","institution":"University of Tennessee at Knoxville","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"Siopsis","suffix":""}],"badges":[],"createdAt":"2024-05-27 14:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4485601/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4485601/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s42484-025-00259-3","type":"published","date":"2025-03-04T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78191928,"identity":"f54519eb-fd70-4f3f-8192-a273b0295d4c","added_by":"auto","created_at":"2025-03-10 20:10:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7276516,"visible":true,"origin":"","legend":"","description":"","filename":"ContinuousVariableQuantumBoltzmannMachine3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4485601/v1_covered_793cbd63-1cd5-4263-9a1f-e77a051ba2f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Continuous-variable Quantum Boltzmann Machine","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":"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":"quantum Boltzmann machine, continuous variable, quantum machine learning, photonic quantum computer, SAR images","lastPublishedDoi":"10.21203/rs.3.rs-4485601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4485601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We propose a continuous-variable quantum Boltzmann machine (CVQBM) using a powerful energy-based neural network. 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