Reinforcement learning approach to shortcuts between thermodynamic states with minimum entropy production

Rongxing Xu
Phys. Rev. E 105, 054123 – Published 13 May 2022

Abstract

We propose a systematic method based on reinforcement learning (RL) techniques to find the optimal path to minimize the total entropy production between two equilibrium states of open systems at the same temperature in a given fixed period. Benefiting from the generalization of the deep RL techniques, we provide a powerful tool to address this problem in quantum systems even with two-dimensional continuous controllable parameters. We successfully apply our method to the classical and quantum two-level systems.

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  • Received 4 November 2021
  • Accepted 20 April 2022

DOI:https://doi.org/10.1103/PhysRevE.105.054123

©2022 American Physical Society

Physics Subject Headings (PhySH)

Statistical Physics & Thermodynamics

Authors & Affiliations

Rongxing Xu*

  • Department of Physics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan Mathematical Science Team, RIKEN Center for Advanced Inelligence Project (AIP), 1-4-1 Nihonbashi, Chuo-Ku, Tokyo 103-0027, Japan

  • *xurongxing@keio.jp

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Vol. 105, Iss. 5 — May 2022

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