论文标题
使用加固学习的量子热机对功率/效率折衷的无模型优化
Model-free optimization of power/efficiency tradeoffs in quantum thermal machines using reinforcement learning
论文作者
论文摘要
量子热机是一种开放量子系统，可以在微型或纳米尺度上进行热量与工作之间的转换。最佳控制这种平衡系统是量子技术和设备应用程序的至关重要但具有挑战性的任务。我们介绍了一个基于强化学习的一般型号的框架，以识别量子加热发动机和冰箱的功率和效率之间的帕累托最佳权衡。该方法不需要对量子热机，系统模型或量子状态的任何了解。取而代之的是，它仅观察到热通量，因此它既适用于模拟和实验设备。我们基于超导量子轴的实验现实冰箱模型以及基于量子谐波振荡器的热发动机测试我们的方法。在这两种情况下，我们都会确定代表最佳功率效率折衷和相应周期的帕累托前冠。这种解决方案的表现优于文献中先前提出的提案，例如优化的奥托周期，减少了量子摩擦。
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 trade-offs 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.
