A Survey of Quantum Reinforcement Learning Approaches: Current Status and Future Research Directions

Document Type

Conference Paper

Publication Date

2025

Publication Title

2025 Conference on Artificial Intelligence-CAI-Annual

Abstract

This work presents a detailed survey of Quantum Reinforcement Learning (QRL), describing its fundamental principles and applications. By illustrating several research works, we provide the advantages and disadvantages of different QRL approaches, offering efficient insights for researchers in the field. To guide researchers in selecting the best QRL approach that meets their intended tasks, we propose a method based on the type of environment as either classical or quantum. Additionally, the paper outlines future research directions, focusing on utilizing QRL to optimize Quantum Sensor Circuits (QSCs) in various quantum physics applications. The proposed survey enhances the understanding and utilization of QRL, paving the way for more efficient developments in the field.

Comments

Paper presented at: 2025 Conference on Artificial Intelligence-CAI-Annual, Santa Clara, CA, MAY 05-07, 2025

This work was supported by National Science Foundation Grant No. OMA 2231377.

DOI

10.1109/CAI64502.2025.00283

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