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ADVANCES IMPROVED QUANTUM GENETIC ALGORITHMS: METHODS, IMPACT AND FUTURE CHALLENGES

LUCAS APABLAZA, Ignacio Rojas, IAN ROSSI, Mauricio Solar

Published Nov 1, 2025• 0 citations
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Abstract

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.

DOI
10.52152/d11434

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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.
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.
An improved quantum genetic algorithm(IQGA) was proposed to overcome the shortcoming of the quantum genetic algorithm(QGA),i.e. local optimization, when it is used for the optimization of continuous functions with many extreme values. In IQGA, the strategies of updating quantum gate using the best solution obtained and population catastrophe were adopted. The test results for two typical functions show that the convergence speed of IQGA is faster than that of QGA, and IQGA can converge in a global solution space, overcoming the shortcoming of QGA. The application results indicate that IQGA is better than QGA and other genetic algorithms.
Quantum Genetic Algorithms (QGA) are an emerging field of multivariate quantum optimization that emulate Darwinian evolution and natural selection, with vast applications in chemistry and engineering. The appropriate application of fitness functions and fitness selection is the problem-encoding step and the slowest step in designing QGAs for specific physical applications. In this paper, we provide a comprehensive review of these crucial steps. Our survey maps cases of quantum advantage, classifies and illustrates QGAs and their subroutines, and discusses the two main physical problems tackled by QGAs: potential energy minimization of particles on a sphere and molecular eigensolving. We conclude that the encoding used by the Thomson problem is a decisive step toward the use of QGAs in a variety of physical applications, while Grover’s search as a selection step in Reduced QGAs is the main driver of speedup. Although the small scale of simulations and the emergent nature of QGA optimizations make their complexity analyses difficult, recent studies are focusing on strategies to scale QGAs up.
This paper presents a novel approach to optimization design leveraging the principles of quantum mechanics, specifically superposition and entanglement, through a Quantum Genetic Algorithm (QGA). Traditional Genetic Algorithms (GAs) face challenges in complex, high-dimensional optimization problems due to the exponential growth of the search space. The QGA aims to mitigate these limitations by encoding genetic information into quantum states and utilizing quantum operations for selection, crossover, and mutation. This approach theoretically utilizes quantum superposition to explore multiple solution possibilities simultaneously and quantum entanglement to correlate solutions, potentially leading to faster convergence and improved optimization performance. The paper outlines the theoretical framework, the algorithmic steps of the QGA, and discusses potential applications. The primary contribution is the conceptual framework and the proposed method, recognizing the nascent stage of QGA research and presenting a novel exploration of its potential.