Anticipating Decoherence: a Predictive Framework for Enhancing Coherence in Quantum Emitters

Anticipating Decoherence: a Predictive Framework for Enhancing Coherence in Quantum Emitters | 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 Anticipating Decoherence: a Predictive Framework for Enhancing Coherence in Quantum Emitters Alexander Kildishev, Pranshu Maan, Yuheng Chen, Sean Borneman, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7313254/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Large-scale quantum systems require optical coherence between distant quantum devices, necessitating spectral indistinguishability. Scalable solid-state platforms offer promising routes to this goal. However, environmental disorders, including dephasing, spectral diffusion, and spin-bath interactions, influence the emitters’ spectra and deteriorate the coherence. Using statistical theory, we identify correlations in spectral diffusion from slowly varying environmental coupling, revealing predictable dynamics extendable to other disorders. Importantly, this could enable the development of an anticipatory framework for forecasting and decoherence engineering in remote quantum emitters. To validate this framework, we demonstrate that a machine learning model trained on limited data can accurately forecast unseen spectral behavior. Realization of such a model on distinct quantum emitters could reduce the spectral shift by factors ≈ 2.1 to 15.8, depending on emitter stability, compared to no prediction. This work presents, for the first time, the application of anticipatory systems and replica theory to quantum technology, along with the first experimental demonstration of internal prediction that generalizes across multiple quantum emitters. These results pave the way for real-time decoherence engineering in scalable quantum systems. Such capability could lead to enhanced optical coherence and multi-emitter synchronization, with broad implications for quantum communication, computation, imaging, and sensing. Physical sciences/Physics/Quantum physics/Single photons and quantum effects Physical sciences/Physics/Statistical physics, thermodynamics and nonlinear dynamics/Statistical physics Anticipatory Systems Solid State Quantum Optics Decoherence Machine Learning Artificial Intelligence Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementarymaterials1QCHN1.pdf Anticipating Decoherence: a Predictive Framework for Enhancing Coherence in Quantum Emitters Cite Share Download PDF Status: Under Review Version 1 posted 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-7313254","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":501038428,"identity":"ffdeebe1-e4ca-4d4a-bb3c-dd9c3108c6f9","order_by":0,"name":"Alexander 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Scalable solid-state platforms offer promising routes to this goal. However, environmental disorders, including dephasing, spectral diffusion, and spin-bath interactions, influence the emitters’ spectra and deteriorate the coherence. Using statistical theory, we identify correlations in spectral diffusion from slowly varying environmental coupling,\r\nrevealing predictable dynamics extendable to other disorders. Importantly, this could enable the development of an anticipatory framework for forecasting and decoherence engineering in remote quantum emitters. To validate this framework, we demonstrate that a machine learning model trained on limited data can accurately forecast unseen spectral behavior. Realization of such a model\r\non distinct quantum emitters could reduce the spectral shift by factors ≈ 2.1 to 15.8, depending on emitter stability, compared to no prediction. This work presents, for the first time, the application of anticipatory systems and replica theory to quantum technology, along with the first experimental demonstration of internal prediction that generalizes across multiple quantum emitters. These results pave the way for real-time decoherence engineering in scalable quantum systems. 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