Quantum Reinforcement Learning for 6G and Beyond Wireless Networks
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918182212599808 |
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| author | Tran, Dinh-Hieu Nguyen, Thai Duong Nguyen, Thanh-Dao Nguyen, Ngoc-Tan Vo, Van Nhan Tran, Hung Chehaitly, Mouhamad Tun, Yan Kyaw Stefanovic, Cedomir Dac, Tu Ho Lagunas, Eva Chatzinotas, Symeon Van Huynh, Nguyen |
| author_facet | Tran, Dinh-Hieu Nguyen, Thai Duong Nguyen, Thanh-Dao Nguyen, Ngoc-Tan Vo, Van Nhan Tran, Hung Chehaitly, Mouhamad Tun, Yan Kyaw Stefanovic, Cedomir Dac, Tu Ho Lagunas, Eva Chatzinotas, Symeon Van Huynh, Nguyen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01070 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Reinforcement Learning for 6G and Beyond Wireless Networks Tran, Dinh-Hieu Nguyen, Thai Duong Nguyen, Thanh-Dao Nguyen, Ngoc-Tan Vo, Van Nhan Tran, Hung Chehaitly, Mouhamad Tun, Yan Kyaw Stefanovic, Cedomir Dac, Tu Ho Lagunas, Eva Chatzinotas, Symeon Van Huynh, Nguyen Networking and Internet Architecture 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. |
| title | Quantum Reinforcement Learning for 6G and Beyond Wireless Networks |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2511.01070 |