Quantum-Driven Energy-Efficiency Optimization for Next-Generation Communications Systems

Su Fong Chien, Heng Siong Lim, Michail Alexandros Kourtis, Qiang Ni, Alessio Zappone and Charilaos C. Zarakovitis
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Su Fong Chien: MIMOS Berhad, Technology Park Malaysia, Kuala Lumpur 57000, Malaysia
Heng Siong Lim: Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia
Michail Alexandros Kourtis: National Centre for Scientific Research ”DEMOKRITOS” (NCSRD), Institute of Informatics and Telecommunications, 153 10 Athens, Greece
Qiang Ni: Department of Computing and Communications, Lancaster University, Lancaster LA1 4YW, UK
Alessio Zappone: Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy
Charilaos C. Zarakovitis: National Centre for Scientific Research ”DEMOKRITOS” (NCSRD), Institute of Informatics and Telecommunications, 153 10 Athens, Greece

Energies, 2021, vol. 14, issue 14, 1-15

Abstract: The advent of deep-learning technology promises major leaps forward in addressing the ever-enduring problems of wireless resource control and optimization, and improving key network performances, such as energy efficiency, spectral efficiency, transmission latency, etc. Therefore, a common understanding for quantum deep-learning algorithms is that they exploit advantages of quantum hardware, enabling massive optimization speed ups, which cannot be achieved by using classical computer hardware. In this respect, this paper investigates the possibility of resolving the energy efficiency problem in wireless communications by developing a quantum neural network (QNN) algorithm of deep-learning that can be tested on a classical computer setting by using any popular numerical simulation tool, such as Python. The computed results show that our QNN algorithm can be indeed trainable and that it can lead to solution convergence during the training phase. We also show that the proposed QNN algorithm exhibits slightly faster convergence speed than its classical ANN counterpart, which was considered in our previous work. Finally, we conclude that our solution can accurately resolve the energy efficiency problem and that it can be extended to optimize other communications problems, such as the global optimal power control problem, with promising trainability and generalization ability.

Keywords: energy efficiency; quantum computing; quantum deep neural networks; quantum entanglement; quantum machine learning; quantum superposition; resource optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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