Guided Reward Design in Continuous Reinforcement Learning for Quantum Control

Reinforcement learning has been intensively applied to tackle complex quantum control problems owing to its adaptability in dynamic environments. However, commonly used reinforcement learning algorithms are restricted to selecting actions from a discrete action space, which may result in inaccurate control. In order to surpass this constraint, we propose an improved continuous reinforcement learning algorithm that can generate control policies in a continuous action space, enabling precise control of quantum systems. Moreover, a guided reward function design method is proposed to guide the learning process toward higher fidelity. Numerical results demonstrate that the proposed continuous reinforcement learning algorithm with a guided reward function can capably prepare states on one-qubit and two-qubit systems.