Erdman Paolo A, Noé Frank
Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195 Berlin, Germany.
Microsoft Research AI4Science, Karl-Liebknecht Str. 32, 10178 Berlin, Germany.
PNAS Nexus. 2023 Aug 2;2(8):pgad248. doi: 10.1093/pnasnexus/pgad248. eCollection 2023 Aug.
A quantum thermal machine is an open quantum system that enables the conversion between heat and work at the micro or nano-scale. Optimally controlling such out-of-equilibrium systems is a crucial yet challenging task with applications to quantum technologies and devices. We introduce a general model-free framework based on reinforcement learning to identify out-of-equilibrium thermodynamic cycles that are Pareto optimal tradeoffs between power and efficiency for quantum heat engines and refrigerators. The method does not require any knowledge of the quantum thermal machine, nor of the system model, nor of the quantum state. Instead, it only observes the heat fluxes, so it is both applicable to simulations and experimental devices. We test our method on a model of an experimentally realistic refrigerator based on a superconducting qubit, and on a heat engine based on a quantum harmonic oscillator. In both cases, we identify the Pareto-front representing optimal power-efficiency tradeoffs, and the corresponding cycles. Such solutions outperform previous proposals made in the literature, such as optimized Otto cycles, reducing quantum friction.
量子热机是一种开放量子系统，能够在微观或纳米尺度上实现热与功之间的转换。对这种非平衡系统进行最优控制是一项至关重要但具有挑战性的任务，在量子技术和器件中有着广泛应用。我们引入一种基于强化学习的通用无模型框架，以识别非平衡热力学循环，这些循环是量子热机和制冷机在功率和效率之间的帕累托最优权衡。该方法不需要任何关于量子热机、系统模型或量子态的知识。相反，它只观测热流，因此既适用于模拟也适用于实验装置。我们在基于超导量子比特的实验现实制冷机模型以及基于量子谐振子的热机模型上测试了我们的方法。在这两种情况下，我们都识别出了代表最优功率 - 效率权衡的帕累托前沿以及相应的循环。这些解决方案优于文献中先前提出的方案，如优化的奥托循环，减少了量子摩擦。
