Engines for predictive work extraction from memoryful quantum stochastic processes
Quantum information-processing techniques enable work extraction from a system's inherently quantum features, in addition to the classical free energy it contains. Meanwhile, the science of computational mechanics affords tools for the predictive modeling of non-Markovian classical and quantum...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
2023-12-01
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| Series: | Quantum |
| Online Access: | https://quantum-journal.org/papers/q-2023-12-11-1203/pdf/ |
| _version_ | 1827589561387057152 |
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| author | Ruo Cheng Huang Paul M. Riechers Mile Gu Varun Narasimhachar |
| author_facet | Ruo Cheng Huang Paul M. Riechers Mile Gu Varun Narasimhachar |
| author_sort | Ruo Cheng Huang |
| collection | DOAJ |
| description | Quantum information-processing techniques enable work extraction from a system's inherently quantum features, in addition to the classical free energy it contains. Meanwhile, the science of computational mechanics affords tools for the predictive modeling of non-Markovian classical and quantum stochastic processes. We combine tools from these two sciences to develop a technique for predictive work extraction from non-Markovian stochastic processes with quantum outputs. We demonstrate that this technique can extract more work than non-predictive quantum work extraction protocols, on the one hand, and predictive work extraction without quantum information processing, on the other. We discover a phase transition in the efficacy of memory for work extraction from quantum processes, which is without classical precedent. Our work opens up the prospect of machines that harness environmental free energy in an essentially quantum, essentially time-varying form. |
| first_indexed | 2024-03-09T01:04:04Z |
| format | Article |
| id | doaj.art-bf5194dbe99f403cade786bfb5edea23 |
| institution | Directory Open Access Journal |
| issn | 2521-327X |
| language | English |
| last_indexed | 2024-03-09T01:04:04Z |
| publishDate | 2023-12-01 |
| publisher | Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften |
| record_format | Article |
| series | Quantum |
| spelling | doaj.art-bf5194dbe99f403cade786bfb5edea232023-12-11T13:26:11ZengVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenQuantum2521-327X2023-12-017120310.22331/q-2023-12-11-120310.22331/q-2023-12-11-1203Engines for predictive work extraction from memoryful quantum stochastic processesRuo Cheng HuangPaul M. RiechersMile GuVarun NarasimhacharQuantum information-processing techniques enable work extraction from a system's inherently quantum features, in addition to the classical free energy it contains. Meanwhile, the science of computational mechanics affords tools for the predictive modeling of non-Markovian classical and quantum stochastic processes. We combine tools from these two sciences to develop a technique for predictive work extraction from non-Markovian stochastic processes with quantum outputs. We demonstrate that this technique can extract more work than non-predictive quantum work extraction protocols, on the one hand, and predictive work extraction without quantum information processing, on the other. We discover a phase transition in the efficacy of memory for work extraction from quantum processes, which is without classical precedent. Our work opens up the prospect of machines that harness environmental free energy in an essentially quantum, essentially time-varying form.https://quantum-journal.org/papers/q-2023-12-11-1203/pdf/ |
| spellingShingle | Ruo Cheng Huang Paul M. Riechers Mile Gu Varun Narasimhachar Engines for predictive work extraction from memoryful quantum stochastic processes Quantum |
| title | Engines for predictive work extraction from memoryful quantum stochastic processes |
| title_full | Engines for predictive work extraction from memoryful quantum stochastic processes |
| title_fullStr | Engines for predictive work extraction from memoryful quantum stochastic processes |
| title_full_unstemmed | Engines for predictive work extraction from memoryful quantum stochastic processes |
| title_short | Engines for predictive work extraction from memoryful quantum stochastic processes |
| title_sort | engines for predictive work extraction from memoryful quantum stochastic processes |
| url | https://quantum-journal.org/papers/q-2023-12-11-1203/pdf/ |
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