A quantum optimization algorithm is used to solve a problem of optimization. From a set of possible solutions, mathematical optimization aims to find the most optimal solution. Most optimization problems are presented as minimization problems, where one attempts to minimize an error which is a function of the solution: the optimal solution has the smallest error. There are many optimization techniques used in fields like mechanics, economics, and engineering, and as the complexity and amount of data involved rise, it becomes increasingly important to find more efficient solutions to optimization problems. There is a possibility that quantum computing may allow problems which are beyond the capabilities of classical computers to be resolved, or may suggest a significant speedup over the fastest known classical algorithm.
Published in Chapter:
A Taxonomy of Quantum Computing Algorithms: Advancements and Anticipations
Lopamudra Hota (National Institute of Technology, Rourkela, India) and Prasant Kumar Dash (C. V. Raman Global University, India)
Copyright: © 2022
|Pages: 21
DOI: 10.4018/978-1-7998-9183-3.ch004
Abstract
Quantum computing exploits quantum-mechanical principles such as entanglement and superposition to offer significant computational advantages over conventional classical computing. Many complex and computationally challenging problems are expected to be solved by quantum computing in a number of fields, such as data science, industrial chemistry, smart energy, finance, secure communications, and many others. In order to understand the current status of quantum computing and identify its challenges, a systematic review of the existing literature will be valuable. An overview of quantum computing literature and its taxonomy is presented in this chapter. Further, the proposed taxonomy aims to identify research gaps by mapping various related studies. There is a detailed analysis of quantum technologies with the most current state of the art. Finally, the chapter presents a highlight of open challenges and future research directions.