Universal Quantum Tomography With Deep Neural Networks

Universal Quantum Tomography With Deep Neural Networks | 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 Universal Quantum Tomography With Deep Neural Networks Nhan Luu, Tuyen Nguyen, Duong Luu, Thang Truong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6492008/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 Quantum state tomography is a crucial technique for characterizing the state of a quantum system, which is essential for many applications in quantum technologies. In recent years, there has been growing interest in leveraging neural networks to enhance the efficiency and accuracy of quantum state tomography. However, versatile methods that are broadly applicable across diverse reconstruction scenarios remain relatively underexplored. In this paper, we present two neural network-based approaches for both pure and mixed quantum state tomography: Restricted Feature Based Neural Network and Mixed States Neural Network, evaluate its effectiveness in comparison to existing neural network-based methods. We demonstrate that our proposed methods can achieve state-of-the-art results in reconstructing mixed quantum states from experimental data. Our work highlights the potential of neural networks in facilitating the development of quantum technologies. Source code is publicly available at https://github.com/luutn2002/uni-qst Quantum state tomography deep neural network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 27 Jan, 2026 Reviewers agreed at journal 19 Jan, 2026 Reviewers invited by journal 17 Jan, 2026 Editor assigned by journal 22 Apr, 2025 Submission checks completed at journal 22 Apr, 2025 First submitted to journal 20 Apr, 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-6492008","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":460571649,"identity":"19aff11d-1ac3-4c25-bdc3-f2fff99310b7","order_by":0,"name":"Nhan Luu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACPgYGNiAlIcfG3nwAxJAhqIWNDazFxpif51gCSAsPsVrSEmfOyDEACRChRb732IOfOw4nbjiQ8/nVjRoLHgb2w0c34LeFL92w98xh4w0Hzm6zzjkGdBhPWtoN/Fp4zCR42w7LbjjYu804hw2oRYLHjKAWyb9thxk3HOZ5Zpzzj0gt0rxtaYoz23iYH+e2EaUlx0xatg0UyGxmzLl9EjxshPzCz3zGTPJtGzAq5R8//pzzrU6On/3wMbxaUGyUAJPEKgcB5g+kqB4Fo2AUjIKRAwDixEEOg9RJswAAAABJRU5ErkJggg==","orcid":"","institution":"The University of Aizu","correspondingAuthor":true,"prefix":"","firstName":"Nhan","middleName":"","lastName":"Luu","suffix":""},{"id":460571650,"identity":"1cda7027-9de2-4023-92b0-65be871391c8","order_by":1,"name":"Tuyen Nguyen","email":"","orcid":"","institution":"University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Tuyen","middleName":"","lastName":"Nguyen","suffix":""},{"id":460571651,"identity":"970092f0-1824-4278-b105-9b52773e2500","order_by":2,"name":"Duong Luu","email":"","orcid":"","institution":"Can Tho University","correspondingAuthor":false,"prefix":"","firstName":"Duong","middleName":"","lastName":"Luu","suffix":""},{"id":460571652,"identity":"870b354a-46ae-4329-8be2-2e325057751a","order_by":3,"name":"Thang Truong","email":"","orcid":"","institution":"The University of Aizu","correspondingAuthor":false,"prefix":"","firstName":"Thang","middleName":"","lastName":"Truong","suffix":""}],"badges":[],"createdAt":"2025-04-21 03:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6492008/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6492008/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83800506,"identity":"402ae01a-3d4b-4832-8286-e4a76f7f8d53","added_by":"auto","created_at":"2025-06-03 02:48:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":684895,"visible":true,"origin":"","legend":"","description":"","filename":"cnvvpspkgscbgkwrzvxpwrcvqsbppthx.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6492008/v1_covered_88421a29-8d93-4b0b-b92d-57a440bf6454.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Universal Quantum Tomography With Deep Neural Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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-information-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"qinp","sideBox":"Learn more about [Quantum Information Processing](http://link.springer.com/journal/11128)","snPcode":"11128","submissionUrl":"https://submission.nature.com/new-submission/11128/3","title":"Quantum Information Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Quantum state tomography, deep neural network","lastPublishedDoi":"10.21203/rs.3.rs-6492008/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6492008/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eQuantum state tomography is a crucial technique for characterizing the state of a quantum system, which is essential for many applications in quantum technologies. 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