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Hungarian Journal of Industry and Chemistry

Evaluation of Resource optimization based on quantum search

Sara El Gaily

Published Dec 7, 2018• 4 citations
Publisher

Abstract

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.

DOI
10.33927/hjic-2019-03

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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.
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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.
The K-SAT problem is a fundamental challenge in computational theory, widely used in artificial intelligence, cryptography, and optimization. Classical algorithms struggle with exponential time complexity, making them inefficient for large-scale instances. In this research, we propose an efficient quantum computing approach based on Grover’s algorithm to enhance the search process for satisfiable solutions in K-SAT problems. Our implementation leverages quantum superposition and amplitude amplification to explore multiple possible solutions simultaneously, reducing the search complexity from O (2ⁿ) in classical methods to O(√2ⁿ) in quantum computing. We design a quantum oracle that encodes the K-SAT clauses and integrates it into Grover’s iterative search framework. The performance is evaluated through Qiskit simulations, demonstrating a significant improvement in search efficiency compared to classical brute-force techniques. The results highlight the potential of quantum algorithms in solving complex combinatorial problems with enhanced speed and accuracy. This study contributes to the development of quantum-accelerated optimization methods, paving the way for real-world applications in machine learning, cryptanalysis, and large-scale data processing.
This paper presents a novel approach to resource allocation within distributed systems, drawing inspiration from the principles of quantum annealing and quantum tunneling. Traditional resource allocation techniques often rely on classical optimization algorithms, which can struggle with the inherent complexity and scale of modern distributed environments. We propose a framework that models resource allocation as a quantum optimization problem, specifically utilizing simulated quantum annealing (SQA) to navigate the solution space. The core idea is to translate the resource allocation challenge into an equivalent quantum problem, where the probabilistic nature of quantum mechanics can accelerate the search for optimal solutions. We define a mathematical model incorporating key elements such as node heterogeneity, task demands, and resource constraints. The simulation of SQA provides a computationally efficient means to explore a wider range of potential solutions compared to deterministic classical algorithms. The effectiveness of this approach is demonstrated through theoretical analysis and simulation results, highlighting its potential to improve resource utilization and system performance in distributed systems. Furthermore, we explore the limitations of the SQA simulation and discuss potential extensions for more accurate quantum emulation.
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Optimization involves finding the best solution from a set of possible options. Traditional computers employ classical algorithms like gradient descent and simulated annealing to solve these problems. However, as the complexity of problems increases, classical computers encounter limitations in terms of time and resources. Quantum optimization stands as a revolutionary paradigm that harnesses the unique properties of quantum mechanics to tackle complex optimization problems that are beyond the capabilities of classical computers. Quantum computers utilize qubits, the fundamental units of quantum information, which can exist in superpositions of states, enabling them to process a vast number of possibilities simultaneously. Additionally, the phenomenon of entanglement enables qubits to be correlated in ways that classical bits cannot, further enhancing their computational power. In this chapter, the authors will delve into the principles, applications, and current developments in the realm of quantum optimization.