Quantum-Inspired Optimization of Genetic Algorithms for Complex Search Spaces

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper explores the integration of quantum-inspired concepts into Genetic Algorithms (GAs) to improve their performance in complex search spaces. Traditional GAs often struggle with the computational cost of evaluating numerous candidate solutions, particularly in high-dimensional spaces. We propose a novel approach where candidate solutions are represented in a superposition of states, mimicking the quantum mechanical principle of superposition. Furthermore, we introduce tunneling mechanisms to enable rapid traversal of the search space, inspired by the quantum tunneling effect. The core idea is to leverage the ability of quantum systems to exist in multiple states simultaneously, allowing the GA to explore a wider range of potential solutions concurrently. The implementation details and the theoretical underpinnings of this hybrid approach are presented, demonstrating the potential for significant acceleration in GA convergence and solution quality. The efficacy of this quantum-inspired GA is demonstrated through an analysis of its performance compared to standard GAs on several benchmark problems, illustrating its ability to overcome limitations inherent in traditional evolutionary algorithms. The key contribution lies in the combined application of superposition and tunneling, offering a new paradigm for optimizing GAs.

DOI: 10.5281/zenodo.22342131

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