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arXiv · 2308.06794

Exploring the Optimal Cycle for Quantum Heat Engine using Reinforcement Learning

Gao-xiang Deng·Haoqiang Ai·Bingcheng Wang·Wei Shao·Yu Liu·Zheng Cui
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Abstract

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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Gao-xiang Deng, Haoqiang Ai, Bingcheng Wang, Wei Shao, Yu Liu, Zheng Cui. 2023-08-13. Exploring the Optimal Cycle for Quantum Heat Engine using Reinforcement Learning. https://doi.org/10.1103/physreva.109.022246

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