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Computer Development & Applications

Research on Quantum Genetic Algorithm

Bai Xiao-bao

Published Jan 1, 2013• 0 citations
OpenAlex

Abstract

Quantum genetic algorithm is a genetic algorithm introduced the concept of quantum computing,it is the emerging field of research in the 90s of the last century.This article describes the characteristics of the genetic algorithm(GA) and quantum algorithm(QC),and the basic theory and method of the quantum genetic algorithm(QGA).To compare the efficiency of quantum genetic algorithm and the traditional genetic algorithm we program in matlab.

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Quantum genetic algorithm is a genetic algorithm introduced the concept of quantum computing,it is the emerging field of research in the 90s of the last century.This article describes the characteristics of the genetic algorithm(GA) and quantum algorithm(QC),and the basic theory and method of the quantum genetic algorithm(QGA).To compare the efficiency of quantum genetic algorithm and the traditional genetic algorithm we program in matlab.
Recently, researchers have applied genetic algorithms (GAs) to address some problems in quantum computation. Also, there has been some works in the designing of genetic algorithms based on quantum theoretical concepts and techniques. The so called Quantum Evolutionary Programming has two major sub-areas: Quantum Inspired Genetic Algorithms (QIGAs) and Quantum Genetic Algorithms (QGAs). The former adopts qubit chromosomes as representations and employs quantum gates for the search of the best solution. The later tries to solve a key question in this field: what GAs will look like as an implementation on quantum hardware? As we shall see, there is not a complete answer for this question. An important point for QGAs is to build a quantum algorithm that takes advantage of both the GA and quantum computing parallelism as well as true randomness provided by quantum computers. In the first part of this paper we present a survey of the main works in GAs plus quantum computing including also our works in this area. Henceforth, we review some basic concepts in quantum computation and GAs and emphasize their inherent parallelism. Next, we review the application of GAs for learning quantum operators and circuit design. Then, quantum evolutionary programming is considered. Finally, we present our current research in this field and some perspectives.
The quantum genetic algorithm (QGA) is derived from the integration of quantum computation and genetic algorithm. It is characterized with advantages like strong global optimization ability, fast convergence speed and small population size. In this paper, the principle, method and basic flow of quantum genetic algorithm (QGA) are introduced, and the research progress of QGA in recent years is reviewed, including the coding extension of theoretical basis, the innovation of operators, the rotation angle of quantum gates, the optimization of complex high-dimensional functions, and hybrid algorithms, and then he application status of QGA is also discussed. Finally, the future development direction of QGA is put forward.
This survey presents a comprehensive analysis of the Improved Quantum Genetic Algorithm (IQGA), an interesting way of applying quantum computing to genetic algorithms (GAs). The study begins with an introduction to the several variants of Quantum Genetic Algorithm (QGA), which are widely used in different applications of GAs. The article introduces the IQGA as a quantum version of the classical GA, detailing how it leverages quantum parallelism and superposition to reduce the time complexity of the genetic operations, and highlighting its crucial role in quantum algorithms. The analysis also addresses the mathematical foundations of the IQGA, and the key advantages and challenges associated with its use in several applications. Finally, it concludes with an exploration of the current and potential applications of IQGA and common characteristics of several implementations of IQGAs. Keywords: QGA, IQGA, GA.
This survey presents a comprehensive analysis of the Improved Quantum Genetic Algorithm (IQGA), an interesting way of applying quantum computing to genetic algorithms (GAs). The study begins with an introduction to the several variants of Quantum Genetic Algorithm (QGA), which are widely used in different applications of GAs. The article introduces the IQGA as a quantum version of the classical GA, detailing how it leverages quantum parallelism and superposition to reduce the time complexity of the genetic operations, and highlighting its crucial role in quantum algorithms. The analysis also addresses the mathematical foundations of the IQGA, and the key advantages and challenges associated with its use in several applications. Finally, it concludes with an exploration of the current and potential applications of IQGA and common characteristics of several implementations of IQGAs.