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Open Access Journal Article

Advances in Quantum Computing Algorithms for Optimization Problems

by Daniel White 1,*
1
Daniel White
*
Author to whom correspondence should be addressed.
ISTI  2019 1(1):4; https://doi.org/10.69610/j.isti.20191130
Received: 27 September 2019 / Accepted: 30 October 2019 / Published Online: 30 November 2019
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Abstract

This paper presents a comprehensive review of the latest advancements in quantum computing algorithms designed to address optimization problems. Optimization is a broad field with applications ranging from logistics and finance to machine learning and artificial intelligence. Traditional computing approaches often face limitations when dealing with complex and large-scale optimization problems, leading to computational bottlenecks. Quantum computing, with its potential to process vast amounts of data simultaneously, offers a promising solution to these challenges. We first discuss the fundamental principles of quantum computing and how they can be leveraged to optimize complex problems. Next, we delve into the development of quantum algorithms specifically tailored for optimization, such as the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE). We analyze the performance of these algorithms on various benchmark problems and compare them with classical counterparts. Additionally, we explore the potential impact of quantum optimization algorithms on real-world applications and highlight the current limitations and future research directions. The findings indicate that while quantum optimization algorithms are still in their infancy, they have the potential to revolutionize the field of optimization by providing efficient solutions to previously intractable problems.

Keywords: Quantum Computing, Optimization Algorithms, Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), Algorithmic Advances.;
Copyright: © 2019 by White. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
White, D. Advances in Quantum Computing Algorithms for Optimization Problems. Information Sciences and Technological Innovations, 2019, 1, 4. https://doi.org/10.69610/j.isti.20191130
AMA Style
White D. Advances in Quantum Computing Algorithms for Optimization Problems. Information Sciences and Technological Innovations; 2019, 1(1):4. https://doi.org/10.69610/j.isti.20191130
Chicago/Turabian Style
White, Daniel 2019. "Advances in Quantum Computing Algorithms for Optimization Problems" Information Sciences and Technological Innovations 1, no.1:4. https://doi.org/10.69610/j.isti.20191130
APA style
White, D. (2019). Advances in Quantum Computing Algorithms for Optimization Problems. Information Sciences and Technological Innovations, 1(1), 4. https://doi.org/10.69610/j.isti.20191130

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