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Computers

Quantum Genetic Algorithms for Computer Scientists

Rafael Lahoz-Beltrá

Published Oct 15, 2016• 124 citations
Publisher

Abstract

Genetic algorithms (GAs) are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical processes such as reproduction, selection, etc. Over the last decade, the possibility to emulate a quantum computer (a computer using quantum-mechanical phenomena to perform operations on data) has led to a new class of GAs known as “Quantum Genetic Algorithms” (QGAs). In this review, we present a discussion, future potential, pros and cons of this new class of GAs. The review will be oriented towards computer scientists interested in QGAs “avoiding” the possible difficulties of quantum-mechanical phenomena.

DOI
10.3390/computers5040024

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Genetic algorithms (GAs) are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical processes such as reproduction, selection, etc. Over the last decade, the possibility to emulate a quantum computer (a computer using quantum-mechanical phenomena to perform operations on data) has led to a new class of GAs known as “Quantum Genetic Algorithms” (QGAs). In this review, we present a discussion, future potential, pros and cons of this new class of GAs. The review will be oriented towards computer scientists interested in QGAs “avoiding” the possible difficulties of quantum-mechanical phenomena.
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This article describes optimization processes of functions and their solutions using quantum genetic algorithms. These are continuous heuristic optimization methods based on simulated genetic mechanisms, i.e., on dynamic processes in a population such as mutation, crossover, selection, and so on. This process leads to the emergence of a new class called quantum genetic algorithms. In this article, we have presented a discussion of new classes of quantum genetic algorithms, future capabilities, and benefits.
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.
Abstract: Evolving solutions rather than computing them certainly represents a promising programming approach. Evolutionary computation has already been known in computer science since more than 4 decades. More recently, another alternative of evolutionary algorithms was invented: Quantum Genetic Algorithms (QGA). In this paper, we outline the approach of QGA by giving a comparison with Conventional Genetic Algorithm (CGA). Our results have shown that QGA can be a very promising tool for exploring search spaces.
The developments in the area of Evolvable Quantum Hardware (QEHW) are based on successful quantum genetic algorithms (QGAs) that take advantage of both the Genetic Algorithms (GAs) and Quantum Computation (QC) parallelism. This paper presents a new Quantum Inspired Genetic Algorithm (QIGA) based on the evolution of a single chromosome. A simple combinational circuit has been implemented using a conventional GA (CGA), a single chromosome QGA (SCQGA), and the new QIGA. In the last case, the total evolution time has been considerably reduced, by changing the population size used in the evolution.