Title: Quantum-Enhanced Differential Evolution – Optimization for Evolutionary Algorithms

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum-enhanced differential evolution (QED) offers a promising approach to accelerate the optimization of complex evolutionary algorithms, particularly in scenarios with high-dimensional search spaces. This paper proposes a novel method that integrates quantum annealing with the differential evolution framework, leveraging quantum computing to reduce computational cost. We investigate the potential of this hybrid approach to improve the performance of evolutionary algorithms in diverse fields, demonstrating significant speedups compared to classical counterparts. The core mechanism centers on strategically employing quantum annealing to perform computationally intensive sub-problems within the evolutionary process. This allows for a more efficient exploration of the search space, ultimately leading to faster convergence and improved solution quality. We explore various quantum annealing techniques and parameter tuning strategies to optimize the QED algorithm for specific optimization problems. The results showcase the efficacy of this hybrid approach, highlighting its potential to revolutionize the optimization of complex, high-dimensional problems.

DOI: 10.5281/zenodo.22576836

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