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

被引:7
作者
Chien, Su Fong [1 ]
Lim, Heng Siong [2 ]
Kourtis, Michail Alexandros [3 ]
Ni, Qiang [4 ]
Zappone, Alessio [5 ]
Zarakovitis, Charilaos C. [3 ,4 ,6 ]
机构
[1] MIMOS Berhad, Technol Pk Malaysia, Kuala Lumpur 57000, Malaysia
[2] Multimedia Univ, Fac Engn & Technol, Melaka 75450, Malaysia
[3] Natl Ctr Sci Res DEMOKRITOS NCSRD, Inst Informat & Telecommun, Athens 15310, Greece
[4] Univ Lancaster, Dept Comp & Commun, Lancaster LA1 4YW, England
[5] Univ Cassino & Southern Lazio, Dept Elect & Informat Engn, I-03043 Cassino, Italy
[6] AXON LOG PC, Innovat Dept, Athens 14231, Greece
关键词
energy efficiency; quantum computing; quantum deep neural networks; quantum entanglement; quantum machine learning; quantum superposition; resource optimization; NETWORKS;
D O I
10.3390/en14144090
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:15
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