Quantum-Enhanced Evolutionary Algorithms for Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper explores the integration of quantum computation with evolutionary algorithms to accelerate the search for optimal solutions to complex optimization problems. We propose a novel hybrid framework that utilizes quantum circuits to enhance the exploration of the solution space, enabling parallel processing and potentially dramatically improving convergence rates compared to traditional evolutionary algorithms. The core mechanism focuses on leveraging superposition and entanglement to represent and manipulate the solution space, leading to a significant acceleration of optimization processes. The potential impact of this approach is substantial for tackling challenging problems across diverse domains, including logistics, finance, and materials science.

DOI: 10.5281/zenodo.22303650

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