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.
Highlights
The intelligence of resolving complex problems in engineering, science, finance, etc., has become increasingly dependent on machine learning methods and, especially, deeplearning approaches driven by artificial neural networks (ANNs)
Classical ANN training can exploit the advantages of quantum information, its complexity can be further improved upon embedding it into quantum neural network (QNN) formalism [13,14,15,16,17], which dictates that training can be performed using a layer-by-layer step and without having to consider all layers, as done with classical ANNs
Recalling that one of the merits of stochastic gradient descent (SGD) rationale is that the parameter matrices K can be computed layer-by-layer without any need to rely on additional unitary operators of the full quantum system [14], Algorithm 1 has a smaller step size than the ANN training in (6), which speeds up QNN convergence toward deriving the optimal weighted sum energy efficiency (WSEE) power allocations
Summary
The intelligence of resolving complex problems in engineering, science, finance, etc., has become increasingly dependent on machine learning methods and, especially, deeplearning approaches driven by artificial neural networks (ANNs). The recent study in [16] examines the complexity of the machine learning dynamics in QNNs by proposing parameterized quantum circuits (e.g., hybrid quantum–classical frameworks) that can approximate the deviation from the actual optimal points explicitly and establish the ANN complexity as a function of the parameters of QNN Another recent study in [17] focuses on inverted harmonic oscillators to analyze the behavior of QNN complexity at different quantum timescales encoded in dissipation, scrambling and asymptotic regimes, which serve as useful guidelines for generalizing ANNs and evaluating QNNs. Bearing the quantum technology landscape and its perspective, this paper introduces QNN modeling tailored to optimize the power control of the energy-efficiency problem in wireless communications.
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