Exploring the Optimal Cycle for Quantum Heat Engine using Reinforcement Learning

• URL: http://arxiv.org/abs/2308.06794v2
• Date: Wed, 7 Feb 2024 00:54:23 GMT
• Title: Exploring the Optimal Cycle for Quantum Heat Engine using Reinforcement Learning
• Authors: Gao-xiang Deng, Haoqiang Ai, Bingcheng Wang, Wei Shao, Yu Liu, Zheng Cui
• Abstract summary: This study employs reinforcement learning to output the optimal cycle of quantum heat engine. The soft actor-critic algorithm is adopted to optimize the cycle of three-level coherent quantum heat engine.
• Score: 5.128039456682052
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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