Overview of Quantum Machine Learning for 6G
DOI:
https://doi.org/10.4108/eettti.10497Keywords:
6G, Quantum Machine LeaningAbstract
The forthcoming sixth generation (6G) of wireless networks requires fundamental rethinking of network intelligence, driven by the transition toward ubiquitous cognitive networks and the unprecedented complexity beyond 5G. The optimization demands of 6G surpass the capabilities of classical heuristics and conventional Machine Learning (ML), which encounter significant limitations in addressing dimensionality challenges in ultra-massive multiple-input multiple-output/terahertz/reconfigurable intelligent surfaces-assisted systems, stringent sub-millisecond latency requirements, and severe energy bottlenecks at the edge. Motivated by these gaps, this review investigates Quantum Machine Learning (QML) as a transformative solution, merging quantum mechanics with data-driven intelligence. We propose a unified and forward-looking perspective on ML integration for 6G, bridging previously siloed research domains such as cross-layer optimization, semantic- and intent-driven communication, and quantum-inspired acceleration. Furthermore, this work systematically reviews quantum-enhanced optimization methods and analyzes QML’s role as an intelligence anchor, demonstrating its potential to provide context-aware, resilient, and sustainable network control across various layers. Ultimately, the paper outlines pathways for integrating QML to ensure timely, scalable, and secure decision-making in the volatile 6G landscape.
References
[1] I. Ishteyaq, K. Muzaffar, N. Shafi, and M. A. Alathbah, “Unleashing the power of tomorrow: Exploration of next frontier with 6G networks and cutting edge technologies,” IEEE Access, vol. 12, pp. 87630–87650, Feb. 2024.
[2] L. Cai, Y. Dai, Q. Hu, J. Zhou, Y. Zhang, and T. Jiang, “Bayesian game-driven incentive mechanism for blockchain-enabled secure federated learning in 6G wireless networks,” IEEE Trans. Netw. Sci. Eng., vol. 11, no. 5, pp. 4486–4500, Sep. 2024.
[3] A. Masaracchia, Y. Li, K. K. Nguyen, C. Yin, S. R. Khosravirad, D. B. D. Costa, and T. Q. Duong, “UAV enabled ultra-reliable low-latency communications for 6G: A comprehensive survey,” IEEE Access, vol. 9, pp. 137421–137450, Dec. 2021.
[4] G. Taricco, “A simple method to calculate random coding union bounds for ultra-reliable low-latency communications,” IEEE Wireless Commun. Lett., vol. 11, no. 5, pp. 881–885, May 2022.
[5] F. Tang, C. Wen, L. Luo, M. Zhao, and N. Kato, “Blockchain-based trusted traffic offloading in space-air-ground integrated networks (sagin): A federated reinforcement learning approach,” IEEE J. Sel. Areas Commun., vol. 40, no. 12, pp. 3420–3434, Dec. 2022.
[6] P. Zhang, Y. Zhang, N. Kumar, and M. Guizani, “Dynamic SFC embedding algorithm assisted by federated learning in space–air–ground-integrated network resource allocation scenario,” IEEE Internet Things J., vol. 10, no. 11, pp. 9423–9436, Jun. 2023.
[7] A. Fayad, T. Cinkler, and J. Rak, “Toward 6G optical fronthaul: A survey on enabling technologies and research perspectives,” IEEE Commun. Surveys Tuts., vol. 27, no. 1, pp. 325–369, Jan. 2025.
[8] W. Huang, T. Song, and J. An, “QA2: QoS guaranteed access assistance for space–air–ground internet of vehicle networks,” IEEE Internet Things J., vol. 9, no. 8, pp. 5861–5874, Apr. 2022.
[9] X. Chen, J. Tan, L. Kang, F. Tang, M. Zhao, and N. Kato, “Frequency selective surface toward 6G communicationsystems:Acontemporarysurvey,”IEEE Commun. Surveys Tuts., vol. 27, no. 2, pp. 1012–1050, Apr. 2025.
[10] A. Kaushik, R. Singh, S. Dayarathna, R. Senanayake, M. Di Renzo, M. Dajer, H. Ji, Y. Kim, V. Sciancalepore, A. Zappone, and W. Shin, “Toward integrated sensing and communications for 6G: Key enabling technologies, standardization, and challenges,” IEEE Commun. Standards Mag., vol. 8, no. 2, pp. 52–59, 2024.
[11] W. Kim, S. K. Bandari, and B. Shim, “Enhanced sparse vector coding for ultra-reliable and low latency communications,” IEEE Trans. Veh. Technol., vol. 69, no. 5, pp. 5171–5175, May 2020.
[12] B. Kharel, O. L. A. López, N. H. Mahmood, H. Alves, and M. Latva-Aho, “Fog-RAN enabled multiconnectivity and multi-cell scheduling framework for ultra-reliable low latency communication,” IEEE Access, vol. 10, pp. 61737–61750, Jun. 2022.
[13] Y. Lu, S. Maharjan, and Y. Zhang, “Adaptive edge association for wireless digital twin networks in 6G,” IEEEInternet Things J., vol. 8, no. 22, pp. 16423–16434, Nov. 2021.
[14] M. S. Khan, I. U. Din, A. Almogren, and J. J. P. C. Rodrigues, “AI-enhanced secure decision-making in ultra-dense 6G networks: An optimized context-aware multi-attribute utility function,” IEEE Trans. Consum. Electron., vol. 70, no. 3, pp. 245–254, Aug. 2024.
[15] S. Mahboob and L. Liu, “Revolutionizing future connectivity: A contemporary survey on ai-empowered satellite-based non-terrestrial networks in 6G,” IEEE Commun. Surveys Tuts., vol. 26, no. 2, pp. 1033–1079, Apr. 2024.
[16] I. Ishteyaq, K. Muzaffar, N. Shafi, and M. A. Alathbah, “Unleashing the power of tomorrow: Exploration of next frontier with 6g networks and cutting edge technologies,” IEEE Access, vol. 12, pp. 29445–29463, 2024.
[17] S. Ammar, C. P. Lau, and B. Shihada, “An in-depth survey on virtualization technologies in 6G integrated terrestrial and non-terrestrial networks,” IEEE Open J. Commun. Soc., vol. 5, pp. 1820–1850, Aug. 2024.
[18] A. Iqbal, M. Tham, Y. J. Wong, A. Al-Habashna, G. Wainer, Y. X. Zhu, and T. Dagiuklas, “Empowering non-terrestrial networks with artificial intelligence: A survey,” IEEE Access, vol. 11, pp. 126788–126812, Nov. 2023.
[19] M.-N. Nguyen, L. D. Nguyen, T. Q. Duong, and H. D. Tuan, “Real-time optimal resource allocation for embedded UAV communication systems,” IEEE Wireless Commun. Lett., vol. 8, no. 1, pp. 225–228, Feb. 2019.
[20] K. Li, B. P. L. Lau, X. Yuan, W. Ni, M. Guizani, and C. Yuen, “Toward ubiquitous semantic metaverse: Challenges, approaches, and opportunities,” IEEE Internet Things J., vol. 10, no. 24, pp. 22171–22193, Dec. 2023.
[21] G. Liu, Y. Zhu, M. Kang, L. Yue, Q. Zheng, N. Li, Q. Wang, Y. Huang, and X. Wang, “Native design for 6G digital twin network: Use cases, architecture, functions, and key technologies,” IEEE Internet Things J., vol. 12, no. 15, pp. 15420–15446, Aug. 2025.
[22] Z. Hou, C. She, Y. Li, D. Niyato, M. Dohler, and B. Vucetic, “Intelligent communications for tactile internet in 6G: Requirements, technologies, and challenges,” IEEE Commun. Mag., vol. 59, no. 12, pp. 26–32, Dec. 2021.
[23] H. Lee and Y.-C. Ko, “Physical layer enhancements for ultra-reliable low-latency communications in 5G new radio systems,” IEEE Commun. Standards Mag., vol. 5, no. 4, pp. 82–88, Dec. 2021.
[24] L. Zhao, X. Chi, L. Qian, and W. Chen, “Analysis on latency-bounded reliability for adaptive grant-free access with multipackets reception in URLLCs,” IEEE Commun. Lett., vol. 23, no. 5, pp. 872–875, May 2019.
[25] H. Ren, C. Pan, Y. Deng, M. Elkashlan, and A. Nallanathan, “Resource allocation for secure URLLC in mission-critical IoT scenarios,” IEEE Trans. Commun., vol. 68, no. 9, pp. 5776–5791, Sep. 2020.
[26] L. B. Luat, N. C. Luong, and D. I .Kim, “Integrated radar and communication in ultra-reliable and low-latency communications-enabled UAV networks,” IEEE Trans. Veh. Technol., vol. 74, no. 8, pp. 10652–10667, Aug. 2025.
[27] Y. Wu, D. Wu, L. Ao, L. Yang, and Q. Fu, “Contention-based radio resource management for URLLC-oriented D2D communications,” IEEE Trans. Veh. Technol., vol. 69, no. 9, pp. 9402–9417, Sep. 2020.
[28] X.Liu,X.Wang,X.Zhao,F.Du,Y.Zhang,S.Geng,Y.Lu, and C. Zhong, “Coexistence of energy-minimizing URLLC and eMBB in power IoT via NOMA-based collaborative MEC heterogeneous network,” IEEE Trans. Veh. Technol., vol. 73, no. 7, pp. 8987–9002, Jul. 2024.
[29] W. G. Negassa, D. J. Gelmecha, and R. S. Singh, “Dynamic optimization of user and cell association in UAV-assisted THz 6G networks,” IEEE Access, vol. 13, pp. 66 342–66 356, Jul. 2025.
[30] E. Goshi, F. Mehmeti, T. F. L. Porta, and W. Kellerer, “Modeling and analysis of mMTC traffic in 5G core networks,” IEEE Trans. Netw. Service Manag., vol. 22, no. 1, pp. 108–123, Mar. 2025.
[31] B. Nikpour, D. Sinodinos, and N. Armanfard, “Deep reinforcement learning in human activity recognition: A survey and outlook,” IEEE Trans. Neural Netw. Learn. Syst., vol. 36, no. 3, pp. 2657–2672, Mar. 2025.
[32] J. Lee, D. Niyato, Y. L. Guan, and D. I. Kim, “Learning to schedule joint radar-communication with deep multi-agent reinforcement learning,” IEEE Trans. Veh. Technol., vol. 71, no. 1, pp. 115–130, Jan. 2022.
[33] H. Liao, J. Fan, H. Ci, J. Gu, Z. Zhou, B. Liao, X. Wang, and S. Mumtaz, “Electric semantic compression-based 6G wireless sensing and communication integrated resource allocation,” IEEE Internet Things J., vol. 11, no. 24, pp. 23 560–23 574, Dec. 2024.
[34] K. Cui, M. Chi, Y. Zhao, and Z. Liu, “Bilevel optimization framework for multiregional integrated energy systems considering 6G network slicing and battery energy storage capacity sharing,” IEEE Open J. Ind. Electron. Soc., vol. 6, pp. 1805–1819, Aug. 2025.
[35] T. C. Mai, H. Q. Ngo, M. Egan, and T. Q. Duong, “Pilot power control for cell-free massive mimo,” IEEE Trans. Veh. Technol., vol. 67, no. 11, pp. 11 270–11 284, Nov. 2018.
[36] M. Hevesli, A. M. Seid, A. Erbad, and M. Abdallah, “Task offloading optimization in digital twin assisted MEC-enabled air–ground iiot 6G networks,” IEEE Trans. Veh. Technol., vol. 74, no. 6, pp. 8262–8276, Jun. 2025.
[37] N. Kato, B. Mao, F. Tang, Y. Kawamoto, J. Liu, and Z. M. Fadlullah, “Ten challenges in advancing machine learning technologies toward 6G,” IEEE Wireless Commun., vol. 27, no. 3, pp. 96–103, Jun. 2020.
[38] Y. Shi, L. Lian, Y. Shi, Z. Wang, Y. Zhou, and W. Xu, “Machine learning for large-scale optimization in 6G wireless networks,” IEEE Commun. Surveys Tuts., vol. 25, no. 1, pp. 1–44, 2023.
[39] D. G. Riviello, R. Tuninato, and R. Garello, “Multi-layer multi-user MIMO with cylindrical arrays under 3GPP 3D channel model for B5G/6G networks,” IEEE Access, vol. 12, pp. 40 568–40 582, Jun. 2024.
[40] J. A. Ansere, S. C. Prabhashana, N. Simmons, O. A. Dobre, H. Shin, and T. Q. Duong, “Quantum deep deterministic policy gradient for digital twin-enabled semantic iov networks,” IEEE Trans. Netw. Sci. Eng., pp. 1–13, 2025.
[41] L. Jiao, Y. Shao, L. Sun, F. Liu, S. Yang, W. Ma, L. Li, X. Liu, B. Hou, X. Zhang, R. Shang, Y. Li, S. Wang, X. Tang, and Y. Guo, “Advanced deep learning models for 6g: Overview, opportunities, and challenges,” IEEE Access, vol. 12, pp. 133 245–133 314, 2024.
[42] W. Deng, M. Li, M. Zhao, M. Zhao, and O. Simeone, “CSI transfer from sub6G to mmwave: Reducedoverhead multi-user hybrid beamforming,” IEEE J. Sel. Areas Commun., vol. 43, no. 3, pp. 658–672, Mar. 2025.
[43] H. L. Nakayiza, L. A. C. Ahakonye, D. Kim, and J. Lee, “Blockchain-enhanced feature engineered data falsification detection in 6g in-vehicle networks,” IEEE Internet Things J., vol. 12, no. 15, pp. 13 406–13 419, Aug. 2025.
[44] I. Hameed, A. Afridi, M. Baek, and H. Song, “Enhancing physical layer security in WPCNs with IRS and controlled jamming for 6G IoT applications,” IEEE Access, vol. 13, pp. 68 092–68 107, Jul. 2025.
[45] Y. Sun, J. Liu, J. Wang, Y. Cao, X. Liu, and N. Kato, “When machine learning meets privacy in 6G: A survey,” IEEE Commun. Surveys Tuts., vol. 22, no. 4, pp.2694–2724, 2020.
[46] H. M. F. Noman, E. Hanafi, K. A. Noordin, K. Dimyati, and M. A. Imran, “Machine learning empowered emerging wireless networks in 6G: Recent advancements, challenges and future trends,” IEEE Access, vol. 11, pp. 83 017–83 051, Aug. 2023.
[47] Y. Liu, Y. Deng, A. Nallanathan, M. Elkashlan, and K. K. Wong, “Machine learning for 6G enhanced ultra-reliable and low-latency services,” IEEE Wireless Commun., vol. 30, no. 2, pp. 48–54, Apr. 2023.
[48] N. Q. T. Thoong, A. A. Cheema, S. R. Khosravirad, O. A. Dobre, and T. Q. Duong, “Channel estimation for reconfigurable intelligent surface-aided 6G NOMA systems using CNN-based quantum LSTM model,” in Proc. IEEE Centen. Int. Conf., Sep. 2024.
[49] X. Wang, S. Wang, X. Liang, D. Zhao, J. Huang, X. Xu, B. Dai, and Q. Miao, “Deep reinforcement learning: A survey,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 4, pp. 3471–3493, Apr. 2024.
[50] J. A. Ansere, E. Gyamfi, V. Sharma, H. Shin, O. A. Dobre, and T. Q. Duong, “Quantum deep reinforcement learning for dynamic resource allocation in mobile edge computing-based iot systems,” IEEE Trans. Wireless Commun., vol. 23, no. 6, pp. 6221–6233, 2024.
[51] Y. Gong, H. Yao, J. Wang, M. Li, and S. Guo, “Edge intelligence-driven joint offloading and resource allocation for future 6G industrial internet of things,” IEEE Trans. Netw. Sci. Eng., vol. 11, no. 6, pp. 5140– 5154, Nov. 2024.
[52] X. Wei, X. Gao, K. Ye, C.-Z. Xu, and Y. Wang, “A quantum reinforcement learning approach for joint resource allocation and task offloading in mobile edge computing,” IEEE Trans. Mobile Comput., vol. 24, no. 4, pp. 2580–2593, 2025.
[53] B. T. Tinh, L. D. Nguyen, H. H. Kha, and T. Q. Duong, “Practical optimization and game theory for 6G ultradense networks: Overview and research challenges,” IEEE Access, vol. 10, pp. 114 694–114 713, Sep. 2022.
[54] T. M. Hoang, N. M. Nguyen, and T. Q. Duong, “Detection of eavesdropping attack in UAV-aided wireless systems: Unsupervised learning with one-class SVM and K-means clustering,” IEEE Wireless Commun. Lett., vol. 9, no. 2, pp. 241–244, Feb. 2020.
[55] Y. Li, J. A. Ansere, O. A. Dobre, and T. Q. Duong, “Realtime optimal resource allocation in multiuser mobile edge computing in digital twin applications with deep reinforcement learning,” in Proc. Veh. Tech. Conf., Sep. 2022.
[56] J. A. Ansere, S. C. Prabhashana, N. Simmons, O. A. Dobre, H. Shin, and T. Q. Duong, “Quantum deep deterministic policy gradient for digital twin-enabled semantic IoV networks,” IEEE Trans. Netw. Sci. Eng., 2025.
[57] S. C. Prabhashana, D. V. Huynh, and T. Q. Duong, “Quantum DRL for UAV-RIS-aided maritime communications with 6G digital twin applications,” in Proc. IEEE Int. Conf. Commun, 2025.
[58] U. Khalid, U. I. Paracha, Z. Naveed, T. Q. Duong, M. Z. Win, and H. Shin, “Quantum fusion intelligence for integrated satellite-ground remote sensing,” IEEE Trans. Wireless Commun., vol. 32, no. 3, pp. 46–55, Jun. 2025.
[59] T. Q. Do, H. T. Nguyen, B. D. E. Mcniven, T. Hoang, L. Zhang, O. A. Dobre, and T. Q. Duong, “An autoadjusting hybrid quantum genetic algorithm-spectre platform for the multi-objective optimization of analog circuit sizing,” Alex. Eng. J, vol. 116, pp. 574–585, 2024.
[60] D. V. Huynh, O. A. Dobre, and T. Q. Duong, “Optimal service placement for 6G edge computing with quantum-centric optimisation in real quantum hardware,” IEEE Wireless Commun. Lett., vol. 28, no. 2, pp. 402–406, Feb. 2024.
[61] M. O. Butt, N. Waheed, T. Q. Duong, and W. Ejaz, “Quantum-inspired resource optimization for 6G networks: A survey,” IEEE Commun. Surveys Tuts., vol. 26, no. 3, pp. 1578–1613, 2024.
[62] V. P. Rekkas, S. Sotiroudis, P. Sarigiannidis, S. Wan, and K. G. Arvanitis, “Machine learning in beyond 5G/6G networks-state-of-the-art and future trends,” Electronics, vol. 10, no. 21, p. 2786, Nov. 2021.
[63] M. R. Mahmood, M. A. Matin, P. Sarigiannidis, A. K. Bashir, and K. Dev, “A comprehensive review on artificial intelligence/machine learning algorithms for empowering the future IoT toward 6G era,” IEEE Commun. Surveys Tuts., vol. 10, pp. 87 535–87 562, Aug. 2022.
[64] U. Demirhan and A. Alkhateeb, “Integrated sensing and communication for 6G: Ten key machine learning roles,” IEEE Commun. Mag., vol. 61, no. 5, pp. 113–119, May 2023.
[65] A. T. Jawad, R. Maaloul, and L. Chaari, “A comprehensive survey on 6G and beyond: Enabling technologies, opportunities of machine learning and challenges,” Comput. Netw., vol. 237, p. 110085, Dec. 2023.
[66] J. Kaur, M. A. Khan, M. Iftikhar, M. Imran, and Q. E. U. Haq, “Machine learning techniques for 5G and beyond,” IEEE Access, vol. 9, pp. 23 472–23 488, Jan. 2021.
[67] Y. I. Demir, A. Yazar, and H. Arslan, “Waveform management approach with machine learning for 6G systems,” IEEE Trans. Netw. Service Manag., vol. 21, no. 5, pp. 5432–5444, Oct. 2024.
[68] M. Incudini, M. Grossi, A. Mandarino, S. Vallecorsa, A. D. Pierro, and D. Windridge, “The quantum path kernel: A generalized neural tangent kernel for deep quantum machine learning,” IEEE J. Quantum Electron., vol. 4, pp. 1–12, Jan. 2023.
[69] M. Grossi, N. Ibrahim, V. Radescu, R. Loredo, K. Voigt, C. von Altrock, and A. Rudnik, “Mixed quantum–classical method for fraud detection with quantum feature selection,” IEEE J. Quantum Electron., vol. 3, pp. 1–15, Mar. 2022.
[70] P. Lamichhane and D. B. Rawat, “Quantum machine learning: Recent advances, challenges, and perspectives,” IEEE Access, vol. 13, pp. 1–25, Jan. 2025.
[71] T. Q. Duong, J. A. Ansere, B. Narottama, V. Sharma, O. A. Dobre, and H. Shin, “Quantum-inspired machine learning for 6G: Fundamentals, security, resource allocations, challenges, and future research directions,” IEEE Open J. Commun. Soc., vol. 3, pp. 1–20, Jul. 2022.
[72] C. Li, Y. Yang, H. Liang, and B. Wu, “Learning quantum drift-diffusion phenomenon by physicsconstraint machine learning,” IEEE/ACM Trans. Netw., vol. 30, no. 5, pp. 2101–2112, Oct. 2022.
[73] R.-X. Zhao, J. Shi, and X. Li, “Qksan: A quantum kernel self-attention network,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 46, no. 12, pp. 8501–8515, Dec. 2024.
[74] Y.-Y. Hong and D. J. D. Lopez, “A review on quantum machine learning in applied systems and engineering,” IEEE Access, vol. 13, pp. 1–18, Feb. 2025.
[75] Z. Becvar, D. Gesbert, P. Mach, J. Blumenstein, and M. Matousek, “Machine learning-based channel quality prediction in 6G mobile networks,” IEEE Commun. Mag., vol. 61, no. 7, pp. 106–112, Jul. 2023.
[76] C. Biswas, M. M. Haque, and U. Das Gupta, “A modified key sifting scheme with artificial neural network based key reconciliation analysis in quantum cryptography,” IEEE Access, vol. 10, pp. 72 743–72 757, Jul. 2022.
[77] H. Salloum, K. Sabbagh, V. Savchuk, R. Lukin, O. Orabi, M. Isangulov, and M. Mazzara, “Performance of quantum annealing machine learning classification models on admet datasets,” IEEE Access, vol. 13, pp. 1–15, Mar. 2025.
[78] K. Sutradhar, R. Venkatesh, and P. Venkatesh, “A review on smart healthcare employing quantum internet of things,” IEEE Eng. Mgt. Rev., pp. 1–9, Aug. 2024.
[79] Y. Cao, Y. Zhao, Q. Wang, J. Zhang, S. X. Ng, and L. Hanzo, “The evolution of quantum key distribution networks: On the road to the Qinternet,” EEE Commun. Surveys Tuts., vol. 24, no. 2, pp. 839–894, Secondquarter 2022.
[80] F. Fan, Y. Shi, T. Guggemos, and X. X. Zhu, “Hybrid quantum-classical convolutional neural network model for image classification,” IEEE Trans. Neural Netw., Learning Sys., vol. 35, no. 12, pp. 18 145–18 159, Dec. 2024.
[81] Z. Qu, W. Shi, B. Liu, D. Gupta, and P. Tiwari, “IoMTbased smart healthcare detection system driven by quantum blockchain and quantum neural network,” IEEE J. Biomedical, Health Info., vol. 28, no. 6, pp. 3317–3328, Jun. 2024.
[82] E. A. Tuli, J. M. Lee, and D. S. Kim, “Integration of quantum technologies into metaverse: Applications, potentials, and challenges,” IEEE Access, vol. 12, pp. 24 871–24 890, Mar. 2024.
[83] V. Heyraud, Z. Li, K. Donatella, A. Le Boité, and C. Ciuti, “Efficient estimation of trainability for variational quantum circuits,” PRX Quantum, vol. 4, no. 4, p. 040335, Dec. 2023.
[84] K. Gupta, P. Jahnavi, A. Hazra, and A. Bablani, “Quantum machine learning for industry 5.0,” pp. 155–189, January 2025.
[85] X. Liu, G. Liu, H.-K. Zhang, J. Huang, and X. Wang, “Mitigating barren plateaus of variational quantum eigensolvers,” IEEE Trans. Quantum Eng., vol. 5, pp. 1– 19, Mar. 2024.
[86] R. Zhang, J. Wang, N. Jiang, M. Armanuzzaman, and Z. Zhao, “Efficient and secure multi-qubit broadcastbased quantum federated learning,” IEEE Trans. on Inf. Forensics and Security, vol. 20, pp. 6778–6793, 2025.
[87] K. Zhang, C. K. M. Lee, Y. P. Tsang, and C. H. Wu, “Variational quantum reinforcement learning for joint resource allocation of blockchain-based vehicular edge computing and quantum internet,” IEEE Trans. Veh. Technol., pp. 1–16, 2025.
[88] S. Shin, Y. S. Teo, and H. Jeong, “Dequantizing quantum machine learning models using tensor networks,” Phy. Rev. Res., vol. 6, no. 2, p. 023218, May 2024.
[89] E. Gil-Fuster, C. Gyurik, A. Pérez-Salinas, and V. Dunjko, “On the relation between trainability and dequantization of variational quantum learning models,” arXiv:2406.07072, Jun. 2024.
[90] Y. Gujju, A. Matsuo, and R. Raymond, “Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications,” Phy. Rev. Applied, vol. 21, no. 6, p. 067001, Jun. 2024.
[91] A. Alomari and S. A. P. Kumar, “ReLAQA: Reinforcement learning-based autonomous quantum agent for quantum applications,” IEEE Trans. Artificial Intell., vol. 6, no. 3, pp. 549–558, Aug. 2025.
[92] E. Andrés, M. P. Cuéllar, and G. Navarro, “Efficient dimensionality reduction strategies for quantum reinforcement learning,” IEEE Access, vol. 11, pp. 104 534–104 553, Sep. 2023.
[93] M. Schuld, R. Sweke, and J. J. Meyer, “Effect of data encoding on the expressive power of variational quantum-machine-learning models,” Phy. Rev. A, vol. 103, no. 3, p. 032430, Mar. 2021.
[94] N. Franco, A. Sakhnenko, L. Stolpmann, D. Thuerck, F. Petsch, A. Rüll, and J. M. Lorenz, “Predominant aspects on security for quantum machine learning: Literature review,” in Proc. IEEE Int. Conf. Quantum Computing, Eng., vol. 01, 2024, pp. 1467–1477.
[95] S. Lu, L.-M. Duan, and D.-L. Deng, “Quantum adversarial machine learning,” Phy. Rev. Res., vol. 2, no. 3, p. 033212, Aug. 2020.
[96] W. Gong, D. Yuan, W. Li, and D.-L. Deng, “Enhancing quantum adversarial robustness by randomized encodings,” Phy. Rev. Res., vol. 6, no. 2, p. 023020, Apr. 2024.
[97] J. Zheng, Q. Gao, M. Ogorzałek, J. Lü, and Y. Deng, “A quantum spatial graph convolutional neural network model on quantum circuits,” IEEE Trans. Neural Netw. Learning Sys., vol. 36, no. 3, pp. 5706–5720, Mar. 2025.
[98] D. Dong, C. Chen, H. Li, and T.-J. Tarn, “Quantum reinforcement learning,” IEEE Trans. Sys., Man, Cybernetics, Part B (Cybernetics), vol. 38, no. 5, pp. 1207–1220, Oct. 2008.
[99] X. Yang, Z. Zhou, and B. Huang, “URLLC key technologies and standardization for 6G power internet of things,” IEEE Commun. Stand. Mag., vol. 5, no. 2, pp. 52–59, Jun. 2021.
[100] T. T. Bui, A. Masaracchia, V. Sharma, O. Dobre, and T. Q. Duong, “Impact of 6G space-air-ground integrated networks on hard-to-reach areas: Tourism, agriculture, education, and indigenous communities,” EAI Endorsed Trans. Tourism Technol. Intell., vol. 1, no. 1, pp. 1–8, Sep. 2024.
[101] B. Narottama and S. Aïssa, “Quantum machine learning for performance optimization of RIS-assisted communications: Framework design and application to energy efficiency maximization of systems with RSMA,” IEEE Trans. Wireless Commun., vol. 23, no. 5, pp. 1103–1115, May 2024.
[102] F. Zaman, A. Farooq, M. A. Ullah, H. Jung, H. Shin, and M. Z. Win, “Quantum machine intelligence for 6G URLLC,” IEEEWireless Commun., vol. 30, no. 2, pp. 22– 29, Apr. 2023.
[103] Silvirianti, B. Narottama, and S. Y. Shin, “Layerwise quantum deep reinforcement learning for joint optimization of UAV trajectory and resource allocation,” IEEE Internet Things J., vol. 11, no. 1, pp. 430-441, Jan. 2024.
[104] J. E. Kim and Y. Wang, “Quantum approximatebayesian optimization algorithms with two mixers and uncertainty quantification,” IEEE Trans. Quantum Eng., vol. 4, pp. 1–17, Oct. 2023.
[105] S. Mittal, Y. Chand, and N. K. Kundu, “Hybrid quantum neural network based indoor user localization using cloud quantum computing,” in Proc. IEEE Reg. 10 Symp., 2024, pp. 1–8.
[106] J. Zhang, G. Zheng, T. Koike-Akino, K.-K. Wong, and F. Burton, “Hybrid quantum-classical neural networks for downlink beamforming optimization,” IEEE Trans. Wireless Commun., Aug. 2024.
[107] M. Kordzanganeh, D. Kosichkina, and A. Melnikov, “Parallel hybrid networks: An interplay between quantum and classical neural networks,” Intell. Comput., vol. 2, p. 0028, Oct. 2023.
[108] E. A. Cherrat, I. Kerenidis, and A. Prakash, “Quantum reinforcement learning via policy iteration,” Quantum Machine Intell., vol. 5, no. 2, p. 30, Jul. 2023.
[109] V. Saggio, B. E. Asenbeck, A. Hamann, T. Strömberg, P. Schiansky, V. Dunjko, N. Friis, N. C. Harris, M. Hochberg, D. Englund et al., “Experimental quantum speed-up in reinforcement learning agents,” Nature, vol. 591, no. 7849, pp. 229–233, Mar. 2021.
[110] C. Huang, Y. Guo, Z. Zhu, M. Si, D. Blankenberg, and R. Liu, “Quantum exploration-based reinforcement learning for efficient robot path planning in sparsereward environment,” in Proc. IEEE Int. Conf. Robot, Hum. Interact. Commun., 2024, pp. 516–521.
[111] M. Khan, J. P. L. Faye, U. C. Mendes, and A. Miranskyy, “Ep-pqm: Efficient parametric probabilistic quantum memory with fewer qubits and gates,” IEEE Trans. Quantum Eng., vol. 3, pp. 1–15, Jan. 2022.
[112] W. Chang, R. Wong, Y. Chen, W. Chung, J. Chen, and A. V. Vasilakos, “Bioinspired quantum oracle circuits for biomolecular solutions of the maximum cut problem,” IEEE Trans. Nanobiosci., vol. 23, no. 3, pp. 499–506, Apr. 2024.
[113] Y. Sun and L. Fu, “Stacking ensemble learning for nonline-of-sight detection of global navigation satellite system,” IEEE Trans. Instrum. Meas., vol. 71, p. 3512510, Jan. 2022.
[114] X. Sun, W. Shao, P. Lv, Y. Mao, Q. Chen, and H. Wu, “Deep learning-based defect identification for quantum key distribution devices,” IEEE Photon. J., vol. 17, no. 5, pp. 1–9, Feb. 2025.
[115] C. M. Varmantchaonala, J. L. K. E. Fendji, J. Schöning, and M. Atemkeng, “Quantum natural language processing: A comprehensive survey,” IEEE Access, vol. 12,pp. 99 578–99 598, Jun. 2024.
[116] D. A. Shoieb, A. Younes, S. M. Youssef, and K. M. Fathalla, “Hqmc-cpc: A hybrid quantum multiclass cardiac pathologies classification integrating a modified hardware efficient ansatz,” IEEE Access, vol. 12, pp.151 039–151 055, Jan. 2024.
[117] F. Just, C. Ghinami, J. Zbinden, and M. Ortiz- Catalan, “Deployment of machine learning algorithms on resource-constrained hardware platforms for prosthetics,” IEEE Access, vol. 12, pp. 40 439–40 449, Feb. 2024.
[118] R. Di Sipio, J.-H. Huang, S. Y.-C. Chen, S. Mangini, and M. Worring, “The dawn of quantum natural language processing,” in IEEE Int. Conf.Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 8612-8616.
[119] N. K. Kundu, M. R. McKay, and R. K. Mallik, “Machinelearning-based parameter estimation of gaussian quantum states,” IEEE Trans. Quantum Eng., vol. 3, p. 3500113, Jan. 2022.
[120] G. Zhou, F. Yang, and J. Xiao, “Study on pixel entanglement theory for imagery classification,” IEEE Trans. Geosci. Remote Sens., vol. 60, p. 5530015, Mar. 2022.
[121] C. Ren, R. Yan, M. Xu, H. Yu, Y. Xu, D. Niyato, and Z. Y. Dong, “QFDSA: A quantum-secured federated learning system for smart grid dynamic security assessment,” IEEE Internet Things J., vol. 11, no. 5, pp. 8414–8426, Mar. 2024.
[122] R. Gupta, D. Saxena, I. Gupta, A. Makkar, and A. K. Singh, “Quantum machine learning driven malicious user prediction for cloud network communications,” IEEE Netw. Lett., vol. 4, no. 4, pp. 174–178, Dec. 2022.
[123] W. Zhao, T. Weng, Y. Ruan, Z. Liu, X. Wu, X. Zheng, and N. Kato, “Quantum computing in wireless communications and networking: A tutorial-cumsurvey,” IEEE Commun. Surveys Tuts., vol. 27, no. 4, pp. 2378–2419, 2025.
[124] F. Aljuaydi, S. AbdelAzim, M. M. Darwish, and M. Zidan, “A quantum algorithm for boolean functions processing,” IEEE Access, vol. 12, pp. 68 497–68 511, Jun. 2024.
[125] A. Sebastianelli, F. Mauro, G. Ciabatti, D. Spiller, B. L. Saux, P. Gamba, and S. L. Ullo, “Quanv4EO: Empowering earth observation by means of quanvolutional neural networks,” IEEE Trans. Geosci. Remote Sens., vol. 63, pp. 1–15, Mar. 2025.
[126] D. T. Uysal, P. D. Yoo, and K. Taha, “Data-driven malware detection for 6G networks: A survey from the perspective of continuous learning and explainability via visualisation,” IEEE Open J. Veh. Technol., vol. 4, pp. 644–675, Jun. 2023.
[127] J. A. Ansere, T. Q. Duong, S. R. Khosravirad, O. A. Dobre, and D. V. Huynh, “Quantum deep reinforcement learning for 6G mobile edge computingbased IoT systems,” in Proc. Int. Wirel. Commun. Mobile Comput. Conf. (IWCMC), May 2023, pp. 1126–1131.
[128] I. P. Malashin, D. S. Daibagya, V. S. Tynchenko, V. A. Nelyub, A. S. Borodulin, A. P. Gantimurov, S. A. Ambrozevich, and A. S. Selyukov, “Ml-based forecasting of temporal dynamics in luminescence spectra of Ag2S colloidal quantum dots,” IEEE Access, vol. 12, pp. 151 830–151 842, Nov. 2024.
[129] A. Farouk, N. A. AbuAli, and S. Mumtaz, “Quantumcomputing-based channel and signal modeling for 6G wireless systems,” IEEE Commun. Mag., vol. 62, no. 2, pp. 64–70, Feb. 2024.
[130] C. Ren, Z. Y. Dong, H. Yu, M. Xu, Z. Xiong, and D. Niyato, “ESQFL: Digital twin-driven explainable and secured quantum federated learning for voltage stability assessment in smart grids,” IEEE J. Sel. Areas Commun., vol. 18, no. 5, pp. 1056–1071, Sep. 2024.
[131] S. R. Hasan, M. Z. Chowdhury, M. Sayem, and Y. M. Jang, “Quantum communication systems: Vision, protocols, applications, and challenges,” IEEE Access, vol. 11, pp. 89 988–90 007, Jul. 2023.
[132] J. Li, J. Song, J. Shi, H. Xu, H. Yu, G. Chen, and S. Zhang, “Robust quantum feature selection with sparse optimization circuit,” IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol. 44, no. 7, pp. 1304-1316, Jul. 2025.
[133] L. K. Pappala, S. B. Veesam, K. Chattu, J. V. Krishna, J. D. Bodapati, and B. T. Rao, “Design of an iterative model for incremental enhancements in quantum image processing using reinforcement learning-based optimizations,” IEEE Access, vol. 13, pp. 112 021–112 037, Aug. 2025.
[134] C.-F. Wang, Y.-K. Lin, and L. Cheng, “Quantum algorithm design and its implementation for solving test sheet composition optimization using a quantum annealing approach,” IEEE Trans. Learn. Technol., vol. 18, pp. 379–392, Jun. 2025.
[135] M. Ahmadian, M. Ruiz, J. Comellas, and L. Velasco, “Cost-Effective ML-Powered Polarization-Encoded Quantum Key Distribution,” J. Lightwave Technol., vol. 40, no. 13, pp. 4092–4103, Jul. 2022.
[136] C. Sanavio, S. Tibaldi, E. Tignone, and E. Ercolessi, “Quantum circuit for imputation of missing data,” vol. 5, pp. 1–11, May 2024.
[137] M. Ahmadian, M. Ruiz, J. Comellas, and L. Velasco, “DARIUS: A digital twin to improve the performance of quantum key distribution,” J. Lightwave Technol., vol. 42, no. 5, pp. 1547–1557, Mar. 2024.
[138] M. Yang, D. Xu, Q. Cui, Z.Wen, and P. Xu, “An Efficient Fisher Matrix Approximation Method for Large-Scale Neural Network Optimization,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 5, pp. 6134–6147, May 2023.
[139] D. Xu, K. Yu, L. Zhen, K. R. Choo, and M. Guizani, “Quantum Learning on Structured CodeWith Computing Traps for Secure URLLC in Industrial IoT Scenarios,” IEEE Internet Things J., vol. 10, no. 18, pp. 16 382–16 393, Sep. 2023.
[140] S. Huang, L. Wang, X. Wang, B. Tan, W. Ni, and K. Wong, “Edge Intelligence in Satellite-Terrestrial Networks With Hybrid Quantum Computing,” IEEE Wirel. Commun. Lett., vol. 14, no. 5, pp. 1023–1027, May 2025.
[141] B. Tu, Z. Wang, X. Yang, J. Li, and A. Plaza, “Hyperspectral Anomaly Detection Using Quantum Potential Clustering,” IEEE Trans. Instrum. Meas.,vol. 71, p. 5012610, May 2022.
[142] Z. Chen, X. Wang, J. Wu, C. Deng, and D. Zhang, “Relational Conduction Graph Network for Intelligent Fault Diagnosis of Rotating Machines Under Small Fault Samples,” IEEE Trans. Instrum. Meas., vol. 72, p. 4511912, Jun. 2023.
[143] S. Pathak, A. Mani, and A. Chatterjee, “A Cooperative Hybrid Quantum-Inspired Salp Swarm and Differential Evolution for Solving CEC 2022 Benchmark and Controller Placement Problems in Software Defined Networks,” IEEE Access, vol. 12, pp. 117 324–117 339, Oct. 2024.
[144] N. A. Alshaer and T. I. Ismail, “AI-driven quantum technology for enhanced 6G networks: Opportunities, challenges, and future directions,” J. Laser Sci. Appl., vol. 15, no. 2, pp. 123–135, Apr. 2024.
[145] H. Urgelles, S. Maheshwari, S. S. Nande et al., “Innetwork quantum computing for future 6G networks,” Adv. Quantum Technol., vol. 6, no. 1, p. e2300090, Jan. 2025.
[146] K. Prateek, N. K. Ojha, F. Altaf, and S. Maity, “Quantum secured 6G technology-based applications in Internet of Everything,” Telecommun. Syst., vol. 83, no. 4, pp. 541–555, Dec. 2023.
[147] R. Sharma and P. R. Chelliah, “Quantum paradigm in 6G revolutionising network,” Adv. Comput., vol. 125, pp. 91–114, Feb. 2025.
[148] O. D. Okey, S. S. Maidin, R. L. Rosa, and W. T. Toor, Quantum key distribution protocol selector based on machine learning for next-generation networks,” Sustainability, vol. 14, no. 12, p. 7349, Jun. 2022.
[149] W. Abdallah, “A physical layer security scheme for 6G wireless networks using post-quantum cryptography,” Comput. Commun., vol. 210, pp. 180–193, Feb. 2024.
[150] J. Zhang, “Wireless broadband signal detection scheme based on quantum machine learning,” in Proc. ICAIT, Dec. 2023, pp. 89–95.
[151] S. J. Chen and Y. H. Tsai, “Quantum-safe networks for 6G: An integrated survey on PQC, QKD, and satellite QKD with future perspectives,” Comput. AI Connect, vol. 3, no. 1, pp. 44–63, Jan. 2025.
[152] A. S. Rajawat, H. M. Breesam et al., “Integrating quantum computing with deep learning for enhanced natural language processing,” in Proc. Int. Conf. Augment. Reality, Virtual Real. Human-Computer Interact., Jun. 2024, pp. 132–139.
[153] A. Khang and K. C. Rath, “The quantum evolution,” in Proc. Int. Conf. Appl. AI Robot. Ind. Transform., Jul. 2024, pp. 203–218.
[154] N. D. Mai, Y. A. Nando, Y. Lee et al., “Quantum stress monitoring: Wearable in-ear EEG powered by hybrid quantum-classical convolutional neural networks,” vol. 36, no. 5, pp. 4123–4137, May 2025.
[155] R. Guarasci, G. D. Pietro, and M. Esposito, “Quantum natural language processing: Challenges and opportunities,” Appl. Sci., vol. 12, no. 20, pp. 10 234–10 252, Oct. 2022.
[156] D. Widdows, W. Aboumrad, D. Kim, S. Ray, and J. Mei, “Quantum natural language processing,” KI - Künstl. Intell., vol. 38, no. 2, pp. 137–149, Apr. 2024.
[157] M. Abbaszade, V. Salari, S. S. Mousavi, and M. Zomorodi, “Application of quantum natural language processing for language translation,” in Proc. IEEE Int. Conf. Quantum Comput. Eng., Dec. 2021, pp. 241–247.
[158] J. A. Ansere, M. Kamal, I. A. Khan, and M. N. Aman, “Dynamic resource optimization for energy-efficient 6G-IoT ecosystems,” Sensors, vol. 23, no. 10, p. 4711, May 2023.
[159] C. M. Varmantchaonala, J. L. K. E. Fendji, J. Schöning et al., “Quantum natural language processing: A comprehensive survey,” IEEE Access, vol. 4, no. 3, pp. 122–139, Aug. 2024.
[160] D. Peral-García, J. Cruz-Benito et al., “Comparing natural language processing and quantum natural processing approaches in text classification tasks,” Expert Syst. Appl., vol. 235, p. 120278, Jun. 2024.
[161] W. Yu, L. Yin, C. Zhang, Y. Chen, and A. X. Liu, “Application of quantum recurrent neural network in low-resource language text classification,” IEEE Trans. Quantum Eng., vol. 5, pp. 1–13, Mar. 2024.
[162] E. R. Miranda, R. Yeung, A. Pearson et al., “A quantum natural language processing approach to musical intelligence,” in Proc. Quantum Comput. Creativity Workshop, Nov. 2022, pp. 44–57.
[163] J. A. Ansere, M. Kamal, E. Gyamfi, F. Sam, M. Tariq, and A. Mohammed, “Energy efficient resource optimization in cooperative internet of things networks,” MDPIInterent of Things. J, vol. 12, p. 100302, Jan. 2023.
[164] S. Wu, J. Li, P. Zhang, and Y. Zhang, “Natural language processing meets quantum physics: A survey and categorization,” in Proc. Conf. Empirical Methods Nat. Lang. Process., Sep. 2021, pp. 3001–3014.
[165] J. Bausch, S. Subramanian, and S. Piddock, “A quantum search decoder for natural language processing,” Quantum Mach. Intell., vol. 3, no. 1, pp. 1–12, Mar. 2021.
[166] G. Pallavi and R. P. Kumar, “Quantum natural language processing and its applications in bioinformatics: A comprehensive review of methodologies, concepts, and future directions,” Front. Comput. Sci., vol. 17, no. 2, pp. 231–248, Feb. 2025.
[167] J. Shi, T. Chen, W. Lai, S. Zhang et al., “Pretrained quantum-inspired deep neural network for natural language processing,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 6, pp. 1234–1247, Jun. 2024.
[168] S. Ganguly, S. N. Morapakula et al., “Quantum natural language processing based sentiment analysis using lambeq toolkit,” in Proc. Int. Conf. Power Control Signal Comput., May 2022, pp. 176–181.
[169] J. A. Ansere, G. Han, K. A. Bonsu, and Y. Peng, “Energy efficient joint power allocation and user selection algorithm for data transmission in internet-of-things networks,” IEEE Internet Things J., vol. 7, no. 9, pp. 8736–8747, Sep. 2020.
[170] Y. Bouakba and H. Belhadef, “Quantum natural language processing: A new and promising way to solve nlp problems,” in Proc. Int. Conf. Artif. Intell. Signal Process., Oct. 2022, pp. 233–240.
[171] M. Schuld and F. Petruccione, “Supervised learning with quantum computers,” Quantum Sci. Technol., vol. 3, no. 1, p. 034002, Jan. 2018.
[172] H. Wazni, K. I. Lo, L. McPheat, and M. Sadrzadeh, “Large scale structure-aware pronoun resolution using quantum natural language processing,” Quantum Machine Intell., vol. 2, no. 1, pp. 21–34, Jan. 2024.
[173] P. Sharma, K. Choi, O. Krejcar, P. Blazek, V. Bhatia, and J. Kim, “Securing optical networks using quantumsecured blockchain: An overview,” Sensors, vol. 23, no. 14, pp. 3791–3808, Jul. 2023.
[174] E. O. Kiktenko, N. O. Pozhar, M. N. Anufriev, A. S. Trushechkin, R. R. Yunusov, A. I. Lvovsky, and A. K. Fedorov, “Quantum-secured blockchain,” Quantum. Sci. Technol., vol. 3, no. 3, p. 035004, Aug. 2018.
[175] X. Sun, P. Kulicki, and M. Sopek, “Logic programming with post-quantum cryptographic primitives for smart contract on quantum-secured blockchain,” Entropy, vol. 23, no. 12, p. 1670, Dec. 2021.
[176] Z. Z. Sun, Y. B. Cheng, M. Wang, L. Qian, and H. Liu, “Quantum blockchain relying on quantum secure direct communication network,” IEEE Internet Things J., vol. 12, no. 7, pp. 5192–5205, Jul. 2025.
[177] D. Silver, A. Ranjan, R. Achutha, T. Patel et al., “LEXIQL: Quantum natural language processing on nisq-era machines,” in Proc. Int. Conf. High Perform. Comput., Jul. 2024, pp. 221–230.
[178] F. Chen, L. Jiang, H. Müller, P. Richerme, C. Chu, Z. Fu, and M. Yang, “NISQ quantum computing: A securitycentric tutorial and survey,” IEEE Circuits Syst. Mag., vol. 24, no. 1, pp. 14–32, Firstquarter 2024.
[179] S. S. Bhoi, A. Saini, A. Diro, S. Kaisar, and A. Muhammad, “Future digital identity management with quantum secure blockchain,” IEEE Commun. Surveys. Tuts., vol. 27, no. 2, pp. 193–215, Apr. 2025.
[180] E. Gyamfi, J. A. Ansere, and F. Golpayegani, “Ontologybased reinforcement learning for semantic offloading in MEC-enabled 6G IoT networks,” in Proc. Int. Symposium Netw. Comput. Commun., 2024.
[181] Y. Wang and J. Liu, “A comprehensive review of quantum machine learning: From nisq to fault tolerance,” Reports. Progress. Physics, vol. 87, no. 6, p. 066001, Jun. 2024.
[182] F. Hu, B. N. Wang, N. Wang, and C. Wang, “Quantum machine learning with d-wave quantum computer,” Quantum Eng., vol. 1, no. 1, p. 6, Sep. 2019.
[183] B. Majid, S. A. Sofi, and Z. Jabeen, “Quantum machine learning: A systematic categorization based on learning paradigms, nisq suitability, and fault tolerance,” Quantum Machine Intell., vol. 3, no. 1, pp. 17–35, Feb. 2025.
[184] R. Sweke, M. S. Kesselring, E. van Nieuwenburg, and J. Eisert, “Reinforcement learning decoders for faulttolerant quantum computation,” Machine Learning Sci. and Tech., vol. 2, no. 1, p. 015007, Mar. 2020.
[185] Y. Zhou and P. Zhang, “Noise-resilient quantum machine learning for stability assessment of power systems,” IEEE Trans. Power Sys., vol. 37, no. 6, pp. 4487–4497, Nov. 2022.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Dung Thanh Tran, Dac-Binh Ha, James Adu Ansere
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
