A Quantum-inspired Classification for Random Mixed States

A Quantum-inspired Classification for Random Mixed States | 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 A Quantum-inspired Classification for Random Mixed States Giuseppe Sergioli, Carlo Cuccu, Carla Sophie Rieger, Andrés Camilo Granda Arango, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7748908/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract We present a quantum-inspired classification framework designed to identify correlation structures; product, separable, and entangled, in random mixed quantum states. Building on previous work where the Pretty-Good-Measurement (PGM) classifier demonstrated a competitive performance on pure-state ensembles, we extend this method to the more challenging domain of mixed states. We apply our quantum-inspired classifier to randomly generated ensembles of two- and three-qubit mixed states, encompassing all possible varieties of subsystem correlations while ensuring statistical neutrality. The results indicate that learning architectures inspired by quantum state discrimination can offer scalable and physically grounded tools for the characterization of entanglement and separability even in the mixed-state regime. Physical sciences/Mathematics and computing Physical sciences/Physics Quantum-inspired machine learning PGM Classifier Quantum states classification Mixed states Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Dec, 2025 Reviews received at journal 25 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers invited by journal 07 Nov, 2025 Editor assigned by journal 18 Oct, 2025 Editor invited by journal 15 Oct, 2025 Submission checks completed at journal 13 Oct, 2025 First submitted to journal 13 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. 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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-7748908","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":546380393,"identity":"4f87c747-6b6b-486d-873d-839383e45763","order_by":0,"name":"Giuseppe 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Building on previous\nwork where the Pretty-Good-Measurement (PGM) classifier demonstrated a competitive\nperformance on pure-state ensembles, we extend this method to the more challenging domain\nof mixed states. We apply our quantum-inspired classifier to randomly generated ensembles\nof two- and three-qubit mixed states, encompassing all possible varieties of subsystem correlations\nwhile ensuring statistical neutrality. 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