Reinforcement Learning Approach to Nonequilibrium Quantum Thermodynamics

We use a reinforcement learning approach to reduce entropy production in a closed quantum system brought out of equilibrium. Our strategy makes use of an external control Hamiltonian and a policy gradient technique. Our approach bears no dependence on the quantitative tool chosen to characterize the degree of thermodynamic irreversibility induced by the dynamical process being considered, requires little knowledge of the dynamics itself, and does not need the tracking of the quantum state of the system during the evolution, thus embodying an experimentally nondemanding approach to the control of nonequilibrium quantum thermodynamics. We successfully apply our methods to the case of single- and two-particle systems subjected to time-dependent driving potentials.

Automated summary

The study utilizes a reinforcement learning approach to reduce entropy production in a closed quantum system. The method involves an external control Hamiltonian and policy gradient technique, offering an experimentally nondemanding approach to nonequilibrium quantum thermodynamics control. The approach does not depend on specific quantitative tools and requires little knowledge of the system dynamics.