Quantum Reinforcement Learning for 6G and Beyond Wireless Networks

TL;DR

This article surveys Quantum Reinforcement Learning (QRL) as a path to meet 6G’s demands for ultra-high data rates, low latency, and massive connectivity. It contrasts traditional DRL with QRL, arguing that quantum resources—via Variational Quantum Circuits and architectures like FQA and HQCA—can accelerate learning and better handle large state-action spaces, albeit within current hardware limits. The paper reviews QRL applications across dynamic resource allocation, UAV trajectory planning, spectrum access, task offloading with sensing, multi-agent control, and SAGIN, and presents a case study showing QRL achieving faster convergence and higher throughput than DRL in dynamic spectrum access. It concludes with future directions in 6G security, SAGIN integration, routing and massive access, URLLC, and the synergy of QRL with emerging technologies.

Abstract

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.

Figures (7)

• Figure 1: FQA and HQCA.
• Figure 2: Architecture of a quantum layer.
• Figure 3: A quantum circuit to generate the Bell state $|\Phi^+\rangle$, illustrating elementary gate operations (inspired by matrix-based approaches wang2025reinforcementlearningquantumcircuit).
• Figure 4: Quantum fast weight programmers.
• Figure 5: Applications of QRL in 6G.