Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning

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dc.citation.articleNumberL022017en_US
dc.citation.issueNumber2en_US
dc.citation.journalTitlePhysical Review Researchen_US
dc.citation.volumeNumber5en_US
dc.contributor.authorErdman, Paolo A.en_US
dc.contributor.authorRolandi, Albertoen_US
dc.contributor.authorAbiuso, Paoloen_US
dc.contributor.authorPerarnau-Llobet, Martíen_US
dc.contributor.authorNoé, Franken_US
dc.date.accessioned2023-07-21T16:13:42Zen_US
dc.date.available2023-07-21T16:13:42Zen_US
dc.date.issued2023en_US
dc.description.abstractThe full optimization of a quantum heat engine requires operating at high power, high efficiency, and high stability (i.e., low power fluctuations). However, these three objectives cannot be simultaneously optimized—as indicated by the so-called thermodynamic uncertainty relations—and a systematic approach to finding optimal balances between them including power fluctuations has, as yet, been elusive. Here we propose such a general framework to identify Pareto-optimal cycles for driven quantum heat engines that trade off power, efficiency, and fluctuations. We then employ reinforcement learning to identify the Pareto front of a quantum dot-based engine and find abrupt changes in the form of optimal cycles when switching between optimizing two and three objectives. We further derive analytical results in the fast- and slow-driving regimes that accurately describe different regions of the Pareto front.en_US
dc.identifier.citationErdman, Paolo A., Rolandi, Alberto, Abiuso, Paolo, et al.. "Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning." <i>Physical Review Research,</i> 5, no. 2 (2023) American Physical Society: https://doi.org/10.1103/PhysRevResearch.5.L022017.en_US
dc.identifier.digitalPhysRevResearch-5-L022017en_US
dc.identifier.doihttps://doi.org/10.1103/PhysRevResearch.5.L022017en_US
dc.identifier.urihttps://hdl.handle.net/1911/114979en_US
dc.language.isoengen_US
dc.publisherAmerican Physical Societyen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of Fair Use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titlePareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learningen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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