TY - JOUR

T1 - Quantum-driven energy-efficiency optimization for next-generation communications systems

AU - Chien, S.F.

AU - Lim, H.S.

AU - Kourtis, M.A.

AU - Ni, Q.

AU - Zappone, A.

AU - Zarakovitis, C.C.

PY - 2021/7/6

Y1 - 2021/7/6

N2 - 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.

AB - 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.

KW - Energy efficiency

KW - Quantum computing

KW - Quantum deep neural networks

KW - Quantum entanglement

KW - Quantum machine learning

KW - Quantum superposition

KW - Resource optimization

KW - Computer hardware

KW - Computer networks

KW - Computer software

KW - Deep learning

KW - Green computing

KW - Learning algorithms

KW - Power control

KW - Communications systems

KW - Energy efficiency optimizations

KW - Generalization ability

KW - Optimal power control

KW - Quantum neural networks

KW - Spectral efficiencies

KW - Wireless communications

KW - Wireless resource control

KW - Quantum efficiency

U2 - 10.3390/en14144090

DO - 10.3390/en14144090

M3 - Journal article

VL - 14

JO - Energies

JF - Energies

SN - 1996-1073

IS - 14

M1 - 4090

ER -