Quantum Reinforcement Learning for 6G and Beyond Wireless Networks

2025-11-02

AI Summary

To address the intelligent decision-making challenges in 6G networks—characterized by ultra-low latency, high throughput, and stringent resource constraints—this paper pioneers a systematic investigation of quantum reinforcement learning (QRL) for critical communication tasks such as dynamic spectrum access. We propose a novel integrated framework synergizing quantum computation with reinforcement learning, achieving significantly faster convergence and higher computational resource efficiency compared to conventional deep reinforcement learning (DRL). Through rigorous modeling and simulation, we demonstrate that QRL outperforms DRL in spectrum allocation tasks in terms of both performance and robustness. Beyond the technical framework, this work establishes the first QRL-driven intelligent communication model tailored for 6G, accompanied by the inaugural comprehensive survey and technology roadmap for QRL in wireless networking. Collectively, our study lays foundational theoretical groundwork and provides a practical paradigm for quantum-AI-enabled future wireless systems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Addressing 6G's stringent latency and throughput requirements
• Enhancing wireless network intelligence with quantum reinforcement learning
• Overcoming limitations of traditional AI in resource-constrained 6G systems

Innovation

• Quantum Reinforcement Learning for 6G networks
• QRL outperforms DRL in spectrum access
• First review of QRL applications in 6G