热机
强化学习
量子
热力循环
计算机科学
透视图(图形)
极限(数学)
压缩(物理)
数学
数学优化
统计物理学
物理
人工智能
热力学
量子力学
数学分析
作者
Gao-xiang Deng,H. Ai,Binghe Wang,Wei Shao,Yu Liu,Cheng Zheng
出处
期刊:Cornell University - arXiv
日期:2023-08-13
标识
DOI:10.48550/arxiv.2308.06794
摘要
Quantum thermodynamic relationships in emerging nanodevices are significant but often complex to deal with. The application of machine learning in quantum thermodynamics has provided a new perspective. This study employs reinforcement learning to output the optimal cycle of quantum heat engine. Specifically, the soft actor-critic algorithm is adopted to optimize the cycle of three-level coherent quantum heat engine with the aim of maximal average power. The results show that the optimal average output power of the coherent three-level heat engine is 1.28 times greater than the original cycle (steady limit). Meanwhile, the efficiency of the optimal cycle is greater than the Curzon-Ahlborn efficiency as well as reporting by other researchers. Notably, this optimal cycle can be fitted as an Otto-like cycle by applying the Boltzmann function during the compression and expansion processes, which illustrates the effectiveness of the method.
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