Identifying optimal cycles in quantum thermal machines with reinforcement-learning
| dc.citation.articleNumber | 1 | en_US |
| dc.citation.journalTitle | npj Quantum Information | en_US |
| dc.citation.volumeNumber | 8 | en_US |
| dc.contributor.author | Erdman, Paolo A. | en_US |
| dc.contributor.author | NoƩ, Frank | en_US |
| dc.date.accessioned | 2022-01-27T20:24:14Z | en_US |
| dc.date.available | 2022-01-27T20:24:14Z | en_US |
| dc.date.issued | 2022 | en_US |
| dc.description.abstract | The optimal control of open quantum systems is a challenging task but has a key role in improving existing quantum information processing technologies. We introduce a general framework based on reinforcement learning to discover optimal thermodynamic cycles that maximize the power of out-of-equilibrium quantum heat engines and refrigerators. We apply our method, based on the soft actor-critic algorithm, to three systems: a benchmark two-level system heat engine, where we find the optimal known cycle; an experimentally realistic refrigerator based on a superconducting qubit that generates coherence, where we find a non-intuitive control sequence that outperforms previous cycles proposed in literature; a heat engine based on a quantum harmonic oscillator, where we find a cycle with an elaborate structure that outperforms the optimized Otto cycle. We then evaluate the corresponding efficiency at maximum power. | en_US |
| dc.identifier.citation | Erdman, Paolo A. and NoƩ, Frank. "Identifying optimal cycles in quantum thermal machines with reinforcement-learning." <i>npj Quantum Information,</i> 8, (2022) Springer Nature: https://doi.org/10.1038/s41534-021-00512-0. | en_US |
| dc.identifier.digital | s41534-021-00512-0 | en_US |
| dc.identifier.doi | https://doi.org/10.1038/s41534-021-00512-0 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/111956 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Springer Nature | en_US |
| dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the articleās Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the articleās Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.title | Identifying optimal cycles in quantum thermal machines with reinforcement-learning | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |
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