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

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2023
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American Physical Society
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The 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.

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Erdman, Paolo A., Rolandi, Alberto, Abiuso, Paolo, et al.. "Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning." Physical Review Research, 5, no. 2 (2023) American Physical Society: https://doi.org/10.1103/PhysRevResearch.5.L022017.

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