The authors use reinforcement learning (RL), an important algorithm in machine learning, to optimize nonequilibrium quantum thermodynamics. They find the optimized evolution of the state with higher fidelity and less consumption of entropy production as well as less work cost than in the case of free evolution, highlighting the potential of RL strategies.
- Jiawei Zhang
- Jiachong Li
- Mang Feng
