The Principles and Research Progress of Quantum Reinforcement Learning

DOI

10.2991/978-2-38476-475-4_80How to use a DOI?

Keywords

Machine learning; Reinforcement learning; Quantum computing

Abstract

Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning. Based on the properties of quantum superposition and quantum entanglement, quantum reinforcement learning can achieve certain quantum advantages on existing medium-scale quantum devices with noise compared to traditional reinforcement learning. This paper analyzes the fundamental principles of quantum reinforcement learning and introduces research work and progress in related fields.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Tingxuan Du
PY  - 2025
DA  - 2025/11/11
TI  - The Principles and Research Progress of Quantum Reinforcement Learning
BT  - Proceedings of the 2025 10th International Conference on Modern Management, Education and Social Sciences (MMET 2025)
PB  - Atlantis Press
SP  - 717
EP  - 726
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-475-4_80
DO  - 10.2991/978-2-38476-475-4_80
ID  - Du2025
ER  -