Harnessing Deep Reinforcement Learning to Construct Time-Dependent Optimal Fields for Quantum Control Dynamics

Abstract

We present an efficient deep reinforcement learning (DRL) approach to automatically construct time-dependent optimal control fields that enable desired transitions in reduced-dimensional chemical systems. Our DRL approach gives impressive performance in autonomously and efficiently constructing optimal control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide a detailed description of the algorithms and hyperparameters as well as performance metrics for our DRL-based approach. Our results demonstrate that DRL can be employed as an effective artificial intelligence approach to efficiently and autonomously design control fields in continuous quantum dynamical chemical systems.