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IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

Quantum Optimization in Large Resource Management Systems

Sara El Gaily, Sándor Imre

Published Jul 1, 2019• 10 citations
Publisher

Abstract

Classical computing methods do not enable to withstand the cumbersome computational operations of complex applications and a bunch of untouched problems that still exist until today. The proposed quantum method tackles the computational complexity problem by exploiting the parallel processing techniques of quantum computing. This paper aims to take advantage of quantum computing power in resource distribution management for a large scale of resources. To achieve this, we use quantum extreme value searching algorithm as a minimum searching algorithm. Moreover, to prove the efficiency of the quantum strategy, we compare the quantum method and the randomized one.

DOI
10.1109/spawc.2019.8815470

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Classical computing methods do not enable to withstand the cumbersome computational operations of complex applications and a bunch of untouched problems that still exist until today. The proposed quantum method tackles the computational complexity problem by exploiting the parallel processing techniques of quantum computing. This paper aims to take advantage of quantum computing power in resource distribution management for a large scale of resources. To achieve this, we use quantum extreme value searching algorithm as a minimum searching algorithm. Moreover, to prove the efficiency of the quantum strategy, we compare the quantum method and the randomized one.
The cloud computing field suffers from the heavy processing caused by the exponentially increasing data traffic. Therefore, optimizing the network performance and achieving a better quality of service (QoS) became a central goal. In cloud computing, the problem of energy consumption of resource distribution management system (RDMS) is presented as an optimization problem. Most of the existing classical optimization approaches, such as heuristic and metaheuristic have high computational complexity. In this work, we proposed a quantum optimization strategy that executes the tasks exponentially faster and with high accuracy named constrained quantum optimization algorithm (CQOA). We exploit the CQOA in RDMS as a toy example for pointing out the efficiency of the proposed quantum strategy in reducing energy consumption and computational complexity. Following that, we investigate the CQOA's implementation, setup, and computational complexity. Finally, we create a simulation environment to evaluate the efficiency of the suggested implemented constrained quantum strategy.
The article examines methods for applying quantum computing to solve optimization problems, which are critical in various fields such as logistics, finance, resource management, energy, and others. The potential of quantum algorithms, such as Grover's algorithm and Variational Quantum Algorithms (VQA), is explored for enhancing computational efficiency compared to classical methods. Quantum computing enables significant acceleration of optimal solution search processes and reduces the complexity of problems through parallel computing and quantum superposition.
Quantum computing and communications try to identify more efficient solutions to the most challenging classical problems such as optimization, secure information transfer, etc. This paper will describe a new quantum method for the distribution of resources in computing platforms that consist of a large number of computing units. Furthermore, a simulation environment was developed and the performance of the new method compared to a classical reference strategy will be demonstrated. Moreover, it will be proven that the proposed solution tackles the problems of computational complexity, computing units that are time-consuming and slow to process, as well as the accuracy in determining the optimum result.
In the recent decades, the ubiquity of computational complexity in the process of a large scale of resource distribution management became one of the key interest of scientific research community, so to solve this large computationally hard problem, it requires selecting the best and the optimum computing framework. In this paper we will present a resource distribution strategy based on the quantum method, where we explained how we can use the quantum algorithm as a computational platform for resource distribution, then a mathematical model has been built, in order to define the stochastic parameters of the quantum strategy related to the given resource distribution model, finally.
Quantum computing leverages the principles of quantum mechanics to process information in ways that classical computers cannot. This capability is particularly advantageous for solving complex optimization problems that are common in supply chain management. Quantum algorithms, such as the quantum approximate optimization algorithm (QAOA) and quantum annealing, have shown promise in efficiently solving these problems by exploring numerous potential solutions simultaneously and identifying optimal strategies. The purpose of this chapter is to investigate the rapidly developing topic of quantum computing and its potential applications in managing sustainable resources within supply chains. Traditional resource allocation methods often struggle to maximize efficiency while minimizing environmental impact. However, new developments in quantum computing have opened up potentially fruitful pathways for addressing these issues. This study aims to explore how quantum computing can revolutionize through an examination of quantum algorithms, optimization approaches, and case studies.