{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T12:56:55Z","timestamp":1781614615900,"version":"3.54.5"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"3-4","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Wireless Pers Commun"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The evolution of 6G wireless networks demands highly efficient beamforming strategies to optimize spectral and energy efficiency in massive MIMO systems. This study introduces a Quantum-Driven Reinforcement Learning (QDRL) framework for Spectral Energy Optimization in Massive MIMO Hybrid Beamforming for 6G, leveraging Quantum Deep Q-Networks (Q-DQN), Quantum Policy Gradient (QPG), and Quantum Approximate Optimization Algorithm (QAOA). The framework integrates mruby-based lightweight scripting for efficient deployment in edge-AI environments, enhancing computational flexibility and resource efficiency. Performance evaluations demonstrate that the Hybrid Quantum Model achieves 11.21 bps\/Hz spectral efficiency, 97% resource utilization efficiency, and reduces energy consumption to 0.50 Joules\/bit, outperforming classical models. The Bit Error Rate (BER) is minimized to 0.0025, and the convergence time is 48.7\u00a0s, significantly improving computational efficiency. Comparative analysis with conventional Deep Reinforcement Learning (DRL) techniques shows that the proposed quantum-enhanced model provides a 32% improvement in energy efficiency and a 21% reduction in computational complexity. The integration of mruby enhances the adaptability of the system in low-power and embedded environments, making it a viable solution for real-time 6G hybrid beamforming. This research highlights the transformative potential of quantum-assisted AI frameworks for scalable, high-speed, and energy-efficient wireless communication.<\/jats:p>","DOI":"10.1007\/s11277-025-11855-8","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T13:19:23Z","timestamp":1760447963000},"page":"405-434","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Quantum-Driven Reinforcement Learning for Spectral Energy Optimization in Massive MIMO Hybrid Beamforming for 6G"],"prefix":"10.1007","volume":"144","author":[{"given":"R.","family":"Krishnamoorthy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. 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IEEE Journal on Selected Areas in Communications. https:\/\/doi.org\/10.1109\/JSAC.2025.3531551","journal-title":"IEEE Journal on Selected Areas in Communications"},{"key":"11855_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2025.3542439","author":"Shuxun","year":"2025","unstructured":"Shuxun, Li, et al. (2025). Precoding design of beam squint compensation for massive MIMO-OFDM in non-terrestrial networks. IEEE Transactions on Cognitive Communications and Networking. https:\/\/doi.org\/10.1109\/TCCN.2025.3542439","journal-title":"IEEE Transactions on Cognitive Communications and Networking"},{"key":"11855_CR7","doi-asserted-by":"publisher","DOI":"10.3390\/jsan14010020","author":"Adeb","year":"2025","unstructured":"Adeb, Salh (2025). Deep Reinforcement Learning-Driven Hybrid Precoding for Efficient Mm-Wave Multi-User MIMO Systems, Journal of Sensor and Actuator Networks, 14, 1, Article 20, https:\/\/doi.org\/10.3390\/jsan14010020."},{"key":"11855_CR8","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2025.3544397","author":"Siyi","year":"2025","unstructured":"Siyi, Li, et al. (2025). Secure hybrid beamforming design for MmWave integrated sensing and communication systems. IEEE Transactions on Vehicular Technology. https:\/\/doi.org\/10.1109\/TVT.2025.3544397","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"11855_CR9","doi-asserted-by":"publisher","unstructured":"Balaji, C. G., et al. (2025). Adaptive beamforming and Energy-Efficient resource allocation for sustainable 6G THz networks. IETE Journal of Research, 1\u201315. https:\/\/doi.org\/10.1080\/03772063.2025.2460672","DOI":"10.1080\/03772063.2025.2460672"},{"key":"11855_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.icte.2025.02.001","author":"Chihyun","year":"2025","unstructured":"Chihyun, Song, et al. (2025). Deep Learning-Based Energy-Efficient transmission control for STAR-RIS aided Cell-Free massive MIMO networks. ICT Express. https:\/\/doi.org\/10.1016\/j.icte.2025.02.001","journal-title":"ICT Express"},{"issue":"4","key":"11855_CR11","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1109\/MNET.2024.3382097","volume":"38","author":"Fei","year":"2024","unstructured":"Fei, Qi, et al. (2024). Enhancing IoT services in 6G non-terrestrial networks with multicast massive MIMO. 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