RL-QAOA: A Reinforcement Learning Approach to Many-Body Ground State Preparation

We proposed a reinforcement learning (RL) approach to preparing the ground state of many-body quantum systems. This class of method formulates a Markovian decision process for the underlined quantum control problems and utilizes the policy gradient algorithm to find optimal variational parameters. The algorithm focuses mainly on Quantum Approximate Optimization Algorithm (QAOA) and proves efficient in preparing the ground state, especially with the presence of noise. Some variants of the algorithms take a model-based approach, which further improves the sample efficiency of the algorithms; others generalize the QAOA ansatz to a versatile one. This work sheds light on reinforcement-learning-aided quantum control algorithms.