While 5G is being deployed worldwide, 6G is receiving increasing attention from researchers to meet the growing demand for higher data rates, lower latency, higher density, and seamless communications worldwide. To meet the stringent requirements of 6G wireless communications networks, AI-integrated communications have become an indispensable part of supporting 6G systems with intelligence, automation, and big data training capabilities. However, traditional artificial intelligence (AI) systems are difficult to meet the stringent latency and high throughput requirements of 6G with limited resources. In this article, we summarize, analyze, discuss the potential, and benefits of Quantum Reinforcement Learning (QRL) in 6G. As an example, we show the superiority of QRL in dynamic spectrum access compared to the conventional Deep Reinforcement Learning (DRL) approach. In addition, we provide an overview of what DRL has accomplished in 6G and its challenges and limitations. From there, we introduce QRL and potential research directions that should continue to be of interest in 6G. To the best of our knowledge, this is the first review and vision article on QRL for 6G wireless communication networks.

翻译：随着5G在全球范围内部署，为满足日益增长的高数据速率、低延迟、高密度和全球无缝通信需求，6G正受到研究者越来越多的关注。为满足6G无线通信网络的严格要求，集成人工智能的通信已成为支撑6G系统具备智能化、自动化及大数据训练能力不可或缺的部分。然而，传统人工智能系统在资源受限条件下难以满足6G对延迟和吞吐量的严苛要求。本文总结、分析并探讨了量子强化学习在6G中的潜力与优势。作为示例，我们展示了在动态频谱接入场景中，量子强化学习相较于传统深度强化学习方法的优越性。此外，本文概述了深度强化学习在6G领域已取得的成果及其面临的挑战与局限，进而引入量子强化学习及其在6G中值得持续关注的研究方向。据我们所知，这是首篇针对6G无线通信网络的量子强化学习综述与展望文章。