Learning to extract work optimally from unknown quantum states with exponentially reduced dissipation

Learning to extract work optimally from unknown quantum states with exponentially reduced dissipation | 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 Learning to extract work optimally from unknown quantum states with exponentially reduced dissipation Josep Lumbreras, Ruo Cheng Huang, Yanglin Hu, Mile Gu, Marco Tomamichel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9431975/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 A pure qubit is a source of non-equilibrium free energy, but this energy cannot be fully extracted when the qubit state is unknown. Here we consider an agent tasked with harvesting work from a sequence of $N$ identically prepared pure qubits, without quantum memory to store them for joint processing. The agent must balance two competing objectives: extracting as much work as possible from each qubit given its current knowledge, while learning more about the state to improve extraction in subsequent rounds. We show that this learning cost can be reduced exponentially relative to existing approaches. By leveraging the exploration-exploitation trade-off from reinforcement learning, we construct fully adaptive work-extraction protocols whose cumulative dissipation scales logarithmically in $N$, compared to the square-root scaling of existing strategies. Our results identify a concrete setting in which the thermodynamic cost of learning an unknown quantum state can be sharply reduced, and reveal a precise connection between reinforcement learning and quantum thermodynamics. Physical sciences/Physics/Quantum physics Physical sciences/Energy science and technology/Energy harvesting Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SI.pdf Supplemental information 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-9431975","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":633112367,"identity":"06faa1bd-c6f3-4048-aa88-27f186f051a7","order_by":0,"name":"Josep 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