International Conference on Innovation Management and Information Systems (ICIMIS)

Quantum Reinforcement Learning: Principles and Research Progress

Yuxuan Zhao, Kuntuo Zhu

Published Mar 27, 2026• 0 citations

Abstract

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.

DOI: 10.1145/3821966.3821973

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