Machine-Learning-Enhanced Entanglement Detection Under Noisy Quantum Measurements

Machine-Learning-Enhanced Entanglement Detection Under Noisy Quantum Measurements | 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 Machine-Learning-Enhanced Entanglement Detection Under Noisy Quantum Measurements Mahmoud Mahdian, Ali Babapour-Azar, Zahra Mousavi, Rashed Khanjani-Shiraz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7987883/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Quantum measurements are inherently noisy and data-intensive, posing significant challenges for reliable entanglement detection and the scalability of quantum technologies. While error mitigation techniques exist, they often require a prohibitive number of measurements, making the process resource-intensive. In this work, we demonstrate that by explicitly accounting for measurement errors, high-fidelity entanglement detection can be achieved with significantly less data.We introduce a machine-learning-based approach that delivers noise-resilient entanglement classification even with imperfect measurements. Our method employs support vector machines (SVMs) trained on features from Pauli measurements to construct a robust optimal entanglement witness (ROEW). By optimizing SVM parameters against worst-case errors, our protocol ensures effectiveness under unknown measurement noise. Numerical experiments show that ROEW maintains high classification accuracy even when measurement errors exceed 10%. Crucially, we demonstrate that training the model using only 20% of the typical dataset suffices to achieve high accuracy and substantial error reduction. Our proposed ROEW significantly outperforms traditional non-robust models, maintaining superior detection performance under elevated noise. This work bridges machine learning and quantum information science, offering a practical tool for noise-robust quantum characterization and advancing the feasibility of entanglement-based technologies. Robust optimization Robust optimal entanglement witness Machine learning Support vector machine Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Nov, 2025 Reviews received at journal 23 Nov, 2025 Reviewers agreed at journal 15 Nov, 2025 Reviewers invited by journal 14 Nov, 2025 Editor assigned by journal 07 Nov, 2025 Submission checks completed at journal 30 Oct, 2025 First submitted to journal 30 Oct, 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-7987883","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":549544048,"identity":"63c729c0-b3d3-479b-98c0-b63a47e52ca5","order_by":0,"name":"Mahmoud Mahdian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYFACHgaJBAaGesb2BiDHwIJ4LQnMPQdAWiSI1AIkE9hnJIB4RGjRbT978MaDmjt5vDOfX93wo0CCgb+9OwGvFrMzeckWCceeFUvOzim72QN0mMSZsxvwazmQYyaRwHaYcePsnLQbPEAtBhK5BLScfwPU8u8w4/6bZ9Ju/iFKyw2gLYlthxMbZ7Afu02cLTfeGFsk9h02ZuzJYbstYyDBQ9gv53MMb/74dliOsf34s5tv/tjI8bf34teCBHgMwCSxykGA/QEpqkfBKBgFo2AEAQBlVk2fTygh6gAAAABJRU5ErkJggg==","orcid":"","institution":"University of Tabriz","correspondingAuthor":true,"prefix":"","firstName":"Mahmoud","middleName":"","lastName":"Mahdian","suffix":""},{"id":549544049,"identity":"716f1b78-6e64-4a02-ad2b-8190949afeb3","order_by":1,"name":"Ali Babapour-Azar","email":"","orcid":"","institution":"University of Tabriz","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Babapour-Azar","suffix":""},{"id":549544050,"identity":"6e1696a8-8f82-41bc-b42f-7bbd3bd6d708","order_by":2,"name":"Zahra Mousavi","email":"","orcid":"","institution":"University of Tabriz","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Mousavi","suffix":""},{"id":549544051,"identity":"09926d8a-f30f-4baa-97e5-719e5eccb1a5","order_by":3,"name":"Rashed Khanjani-Shiraz","email":"","orcid":"","institution":"Munich University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rashed","middleName":"","lastName":"Khanjani-Shiraz","suffix":""}],"badges":[],"createdAt":"2025-10-30 09:53:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7987883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7987883/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96778151,"identity":"084fdaf0-48a5-4be5-8902-0ffb294f097e","added_by":"auto","created_at":"2025-11-26 03:39:24","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5747,"visible":true,"origin":"","legend":"","description":"","filename":"8657e43aa3204e3ea488288d7394400f.json","url":"https://assets-eu.researchsquare.com/files/rs-7987883/v1/ee644f8ceec61aafe27af169.json"},{"id":96916998,"identity":"fda84f4d-b13e-459d-a453-6f960d932fd4","added_by":"auto","created_at":"2025-11-27 14:09:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":896186,"visible":true,"origin":"","legend":"","description":"","filename":"ROEWfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7987883/v1_covered_185400cb-2753-452f-aece-18c2ecf74de2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine-Learning-Enhanced Entanglement Detection Under Noisy Quantum Measurements","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":"Robust optimization, Robust optimal entanglement witness, Machine learning, Support vector machine","lastPublishedDoi":"10.21203/rs.3.rs-7987883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7987883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Quantum measurements are inherently noisy and data-intensive, posing significant challenges for reliable entanglement detection and the scalability of quantum technologies. 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