Quantum Simulation of Evolutionary Algorithms

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum simulation has emerged as a powerful tool for optimization and exploration across diverse fields. This paper investigates the application of quantum mechanics to enhance evolutionary algorithms, proposing a novel hybrid quantum-classical simulation framework designed to accelerate the search for optimal solutions. We introduce a quantum-enhanced mutation operator, integrated with a classical optimization algorithm, to exploit quantum effects to expedite the exploration of evolutionary parameter spaces. The core mechanism focuses on utilizing quantum phenomena – specifically superposition and entanglement – to improve the representation and manipulation of evolutionary trajectories, ultimately leading to faster convergence and the identification of optimal solutions. The research explores the potential of this approach to overcome limitations in traditional evolutionary algorithm implementation, offering a promising pathway for enhanced optimization capabilities.

DOI: 10.5281/zenodo.22268272

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