Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning

Abstract

A framework to fully optimize driven quantum heat engines is presented that allows identification of Pareto-optimal cycles that trade-off power, efficiency, and power fluctuations. The framework is then applied to a quantum-dot-based heat engine using reinforcement learning and analytical methods based on the slow- and fast-driving dynamical regimes.