Quantum Discord and Entanglement in Radiative Capture Reactions | 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 Quantum Discord and Entanglement in Radiative Capture Reactions Hossein Sadeghi, Mehdi Mirzaee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6685278/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract This paper presents a comprehensive investigation of quantum correlations in radiativecapture reactions through a unified lens of quantum information theory and nuclearphysics. By combining density matrix formulations with open quantum system dynamics, we can establish universal scaling laws governing the relationship betweenquantum discord (D), entanglement entropy (S), and fundamental nuclear properties. The analysis reveals the inverse power-law dependencies D ∝ Γ−0.83±0.04 and S ∝ Γ−1.12±0.06 between these quantum metrics and the decay width (Γ), maintainedacross fourteen nuclear systems spanning six decades in Γ and masses 2 ≤ A ≤ 56. Amachine learning architecture that integrates physical constraints with deep residualnetworks achieves 92% prediction accuracy for coherence lengths Lc while identifyingshell structure effects that enhance quantum correlations near magic numbers. Thepersistent quantum discord (D > 0.3) observed in systems with sub-attometer coherence lengths suggests nuclear spins maintain non-classical correlations through internaldegrees of freedom rather than spatial coherence. The developed phase diagram categorizes nuclear reactions into three quantum technology regimes: memory (Q > 1020s-1), sensing (1018 < Q < 1020 s-1), and foundational tests (Q < 1018 s-1), with56Fe(n; γ) and 6Li(n; γ) identified as optimal candidates for applications. These resultsbridge nuclear physics with quantum information science, providing both theoreticalinsights into decoherence mechanisms and practical tools for quantum material design.The proposed methodology establishes radiative capture reactions as a novel platformfor exploring emergent quantum phenomena in strongly interacting systems. Physical sciences/Physics Physical sciences/Physics/Quantum physics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 20 Aug, 2025 Reviews received at journal 11 Aug, 2025 Reviews received at journal 05 Aug, 2025 Reviewers agreed at journal 26 Jul, 2025 Reviewers agreed at journal 24 Jul, 2025 Reviewers invited by journal 04 Jun, 2025 Editor assigned by journal 04 Jun, 2025 Editor invited by journal 30 May, 2025 Submission checks completed at journal 29 May, 2025 First submitted to journal 17 May, 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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