Multi-Metric Quantum State Analysis and Decoherence Profiling in Quantum Dot Systems: A Theoretical Approach with Deep Learning-Based Validation

Multi-Metric Quantum State Analysis and Decoherence Profiling in Quantum Dot Systems: A Theoretical Approach with Deep Learning-Based Validation | 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 Multi-Metric Quantum State Analysis and Decoherence Profiling in Quantum Dot Systems: A Theoretical Approach with Deep Learning-Based Validation Blessed Yahweh, Aniekan M. Ekanem, Nyakno Jimmy George, Esther O. Oduntan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7262536/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Quantum coherence and fidelity are essential ingredients for scalable quantum technologies, particularly in solid-state platforms such as quantum dots (QDs). In this work, we introduce a physics-inspired framework for the multi-metric characterization of QDs confined to a spherical potential. We obtain the energy eigenvalues using Nikiforov-Uvarov Functional Analysis (NUFA) and calculate the thermodynamic and information-theoretic quantities of purity, Rényi-2 entropy, and dynamical loss of coherence, to give quantitative descriptors of the confinement geometry, excitation dynamics, and decoherence sensitivity. For predictive modeling, we develop a supervised deep neural network (DNN) that learns to map quantum energy features to the corresponding state metrics, providing a quick and accurate estimator that adheres to the underlying physics. Our findings indicate that the low-energy and highly localized states have the lowest entropy and highest purity, whereas the higher excited states exhibit significant decoherence and thermal leakage. This hybrid data-modeling strategy not only enables a systematic connection between the energy-level physics and quantum information-theoretic measures but also provides an enabling step towards intelligent coherence management in QD systems. The framework can be readily extended to other related near-term intermediate-scale quantum (NISQ) systems for a generalized pathway to fidelity-guided quantum design and diagnostics. Quantum dots quantum fidelity entropy measures decoherence modeling Nikiforov-Uvarov analysis purity quantum coherence thermodynamic characterization deep learning-assisted quantum physics NISQ platforms Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Nov, 2025 Reviews received at journal 25 Oct, 2025 Reviews received at journal 25 Oct, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers invited by journal 09 Sep, 2025 Editor assigned by journal 06 Aug, 2025 Submission checks completed at journal 05 Aug, 2025 First submitted to journal 31 Jul, 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. 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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-7262536","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515346095,"identity":"60501534-6996-498e-be58-b7b4fbea31c5","order_by":0,"name":"Blessed Yahweh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYBACA3YwxcwgAaISDCTkQPSBB/i0MCNr+VBgYQzWkkCsFsYZHyoSG8DW4dFizsx8+OOPCmt5ydnNzx7zGEikzw87/BBoi52cbgN2LZbNbGnSPGfSDWfLHDM3BmrJ3Xg7zQCoJdnY7AAOhx3mMWNmbDvMOE8iwUwarGV2AkjLgcRtOLXwf/74899h+3kS6d9AWoD2pX8goIWHQYK34XDibIkcM8kZBhIJ8tI5+G0B+gXonmPpyTPnnCmT+GAgYbhBOqfgQIIBbr+Yszc//vijxtp2xu32bRIJf+rk5Wenb/7wocJODpcWBJCAORWs0oCQcmQt8g3EqB4Fo2AUjIKRBAANXV6fSuSF9QAAAABJRU5ErkJggg==","orcid":"","institution":"Akwa Ibom State University","correspondingAuthor":true,"prefix":"","firstName":"Blessed","middleName":"","lastName":"Yahweh","suffix":""},{"id":515346099,"identity":"85b77abd-fefa-47a5-837e-6dd3fbe7312e","order_by":1,"name":"Aniekan M. 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In this work, we introduce a physics-inspired framework for the multi-metric characterization of QDs confined to a spherical potential. We obtain the energy eigenvalues using Nikiforov-Uvarov Functional Analysis (NUFA) and calculate the thermodynamic and information-theoretic quantities of purity, R\u0026eacute;nyi-2 entropy, and dynamical loss of coherence, to give quantitative descriptors of the confinement geometry, excitation dynamics, and decoherence sensitivity. For predictive modeling, we develop a supervised deep neural network (DNN) that learns to map quantum energy features to the corresponding state metrics, providing a quick and accurate estimator that adheres to the underlying physics. Our findings indicate that the low-energy and highly localized states have the lowest entropy and highest purity, whereas the higher excited states exhibit significant decoherence and thermal leakage. This hybrid data-modeling strategy not only enables a systematic connection between the energy-level physics and quantum information-theoretic measures but also provides an enabling step towards intelligent coherence management in QD systems. The framework can be readily extended to other related near-term intermediate-scale quantum (NISQ) systems for a generalized pathway to fidelity-guided quantum design and diagnostics.\u003c/p\u003e","manuscriptTitle":"Multi-Metric Quantum State Analysis and Decoherence Profiling in Quantum Dot Systems: A Theoretical Approach with Deep Learning-Based Validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-16 06:15:22","doi":"10.21203/rs.3.rs-7262536/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-06T22:53:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-25T17:31:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-25T13:57:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254924937060280139630139255288733835398","date":"2025-09-11T11:42:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236301711210645878812900332974354751030","date":"2025-09-10T11:02:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-09T07:24:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-06T19:11:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-05T15:00:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Quantum Information Processing","date":"2025-07-31T12:56:31+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"999f69cd-18e2-4d7d-99a9-afb2f8e3c9da","owner":[],"postedDate":"September 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-06T15:08:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-16 06:15:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7262536","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7262536","identity":"rs-7262536","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}