Exploring the Optimal Cycle for Quantum Heat Engine using Reinforcement Learning

TL;DR

This paper uses reinforcement learning, specifically the soft actor-critic algorithm, to find the optimal cycle for a quantum heat engine, achieving higher power and efficiency than traditional cycles.

Contribution

It introduces a reinforcement learning approach to optimize quantum heat engine cycles, demonstrating significant improvements in power and efficiency over existing methods.

Findings

Optimal cycle's power is 1.28 times higher than the steady limit.

The optimal cycle surpasses the Curzon-Ahlborn efficiency.

The cycle can be approximated as an Otto-like cycle using a Boltzmann function.

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.