1.
Nguyen, Doan Hieu; Nguyen, Xuan Tung; Jeong, Seon-Geun; Chien, Trinh Van; Hanzo, Lajos; Hwang, Won-Joo
Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems Journal Article
In: IEEE Transactions on Vehicular Technology, pp. 1–6, 2025, ISSN: 1939-9359.
Abstract | Links | BibTeX | Tags: Cell-free massive MIMO, Convergence, Convolutional neural networks, Integrated circuit modeling, Massive MIMO, Pilot Allocation, Quantum circuit, Quantum Machine Learning, Quantum state, Qubit, Throughput, Training, Vectors
@article{nguyen_hybrid_2025,
title = {Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems},
author = {Doan Hieu Nguyen and Xuan Tung Nguyen and Seon-Geun Jeong and Trinh Van Chien and Lajos Hanzo and Won-Joo Hwang},
url = {https://ieeexplore.ieee.org/document/11091511},
doi = {10.1109/TVT.2025.3588212},
issn = {1939-9359},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {IEEE Transactions on Vehicular Technology},
pages = {1–6},
abstract = {A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic sum throughput. The existing model-based solutions found in the literature are inefficient and/or computationally demanding. Similarly, conventional deep neural networks may struggle in the face of high-dimensional inputs, require complex architectures, and their convergence is slow due to training numerous hyperparameters. The proposed HQCNN leverages parameterized quantum circuits (PQCs) relying on superposition for enhanced feature extraction. Specifically, we exploit the same PQC across all the convolutional layers for customizing the neural network and for accelerating the convergence. Our numerical results demonstrate that the proposed HQCNN offers a total network throughput close to that of the excessive-complexity exhaustive search and outperforms the state-of-the-art benchmarks.},
keywords = {Cell-free massive MIMO, Convergence, Convolutional neural networks, Integrated circuit modeling, Massive MIMO, Pilot Allocation, Quantum circuit, Quantum Machine Learning, Quantum state, Qubit, Throughput, Training, Vectors},
pubstate = {published},
tppubtype = {article}
}
A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic sum throughput. The existing model-based solutions found in the literature are inefficient and/or computationally demanding. Similarly, conventional deep neural networks may struggle in the face of high-dimensional inputs, require complex architectures, and their convergence is slow due to training numerous hyperparameters. The proposed HQCNN leverages parameterized quantum circuits (PQCs) relying on superposition for enhanced feature extraction. Specifically, we exploit the same PQC across all the convolutional layers for customizing the neural network and for accelerating the convergence. Our numerical results demonstrate that the proposed HQCNN offers a total network throughput close to that of the excessive-complexity exhaustive search and outperforms the state-of-the-art benchmarks.
2.
Hanzo, Lajos; Babar, Zunaira; Cai, Zhenyu; Chandra, Daryus; Djordjevic, Ivan B.; Koczor, Balint; Ng, Soon Xin; Razavi, Mohsen; Simeone, Osvaldo
Quantum Information Processing, Sensing, and Communications: Their Myths, Realities, and Futures Journal Article
In: Proceedings of the IEEE, pp. 1–51, 2025, ISSN: 1558-2256.
Abstract | Links | BibTeX | Tags: Codes, Encoding, Error correction codes, Europe, Information processing, Next generation networking, Prevention and mitigation, Quantum communications, Quantum computing, quantum error correction coding, quantum error mitigation, quantum key distribution (QKD), Quantum Machine Learning, quantum sensing, quantum-secured direct communications (QSDC), Qubit, Wireless communication
@article{hanzo_quantum_2025,
title = {Quantum Information Processing, Sensing, and Communications: Their Myths, Realities, and Futures},
author = {Lajos Hanzo and Zunaira Babar and Zhenyu Cai and Daryus Chandra and Ivan B. Djordjevic and Balint Koczor and Soon Xin Ng and Mohsen Razavi and Osvaldo Simeone},
url = {https://ieeexplore.ieee.org/document/10828532},
doi = {10.1109/JPROC.2024.3510394},
issn = {1558-2256},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {Proceedings of the IEEE},
pages = {1–51},
abstract = {The recent advances in quantum information processing, sensing, and communications are surveyed with the objective of identifying the associated knowledge gaps and formulating a roadmap for their future evolution. Since the operation of quantum systems is prone to the deleterious effects of decoherence, which manifests itself in terms of bit-flips, phase-flips, or both, the pivotal subject of quantum error mitigation is reviewed both in the presence and absence of quantum coding. The state of the art, knowledge gaps, and future evolution of quantum machine learning (QML) are also discussed, followed by a discourse on quantum radar systems and briefly hypothesizing about the feasibility of integrated sensing and communications (ISAC) in the quantum domain (QD). Finally, we conclude with a set of promising future research ideas in the field of ultimately secure quantum communications with the objective of harnessing ideas from the classical communications field.},
keywords = {Codes, Encoding, Error correction codes, Europe, Information processing, Next generation networking, Prevention and mitigation, Quantum communications, Quantum computing, quantum error correction coding, quantum error mitigation, quantum key distribution (QKD), Quantum Machine Learning, quantum sensing, quantum-secured direct communications (QSDC), Qubit, Wireless communication},
pubstate = {published},
tppubtype = {article}
}
The recent advances in quantum information processing, sensing, and communications are surveyed with the objective of identifying the associated knowledge gaps and formulating a roadmap for their future evolution. Since the operation of quantum systems is prone to the deleterious effects of decoherence, which manifests itself in terms of bit-flips, phase-flips, or both, the pivotal subject of quantum error mitigation is reviewed both in the presence and absence of quantum coding. The state of the art, knowledge gaps, and future evolution of quantum machine learning (QML) are also discussed, followed by a discourse on quantum radar systems and briefly hypothesizing about the feasibility of integrated sensing and communications (ISAC) in the quantum domain (QD). Finally, we conclude with a set of promising future research ideas in the field of ultimately secure quantum communications with the objective of harnessing ideas from the classical communications field.