Quantum Reinforcement Learning

Leonhard Kunczik

Barry C. Sanders, Pantita Palittapongarnpim, Seyed Shakib Vedaie

Yuxuan Zhao, Kuntuo Zhu

Quantum reinforcement learning (QRL) is a burgeoning area that lies at the convergence of quantum computing and machine learning. Based on the characteristics of quantum superposition and quantum entanglement, quantum reinforcement learning can achieve some quantum advantages on existing noisy intermediate-scale quantum devices compared to traditional reinforcement learning. Within the field of information systems, reinforcement learning techniques have been extensively explored for applications including intelligent decision support mechanisms, resource distribution strategies, and system enhancement under conditions of uncertainty. This paper analyzes the fundamental principles of quantum reinforcement learning and introduces research work and progress in related areas. This study examines the foundational concepts underlying quantum reinforcement learning and provides an overview of notable algorithmic strategies documented in contemporary research.

Yi-Pei Liu, Qing‐Shan Jia, Xu Wang

The Multi-Arm bandit(MAB) problem is a classical problem in the field of reinforcement learning with only one state. The Grid problem is a multi-state problem for reinforcement learning. In this work, we focus on how to combine the classical value function method to quantum computation, and we propose three novel quantum reinforcement learning(QRL) algorithms for the MAB problem and one novel QRL algorithm, which is combined with the quantum random walk and Grover algorithm, for the Grid problem. From the experiments, the learning process is speed-up by combining the value function with quantum computation.

Joongheon Kim

Leonhard Kunczik