Quantum-Accelerated Evolutionary Algorithms for Parameter Space Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-accelerated evolutionary algorithms (QEA) represent a novel approach to optimization, utilizing quantum mechanics to enhance exploration and convergence speed. This paper explores the potential of QEA to address challenges inherent in traditional evolutionary algorithms, particularly in parameter space optimization. We detail the implementation of quantum annealing and quantum-inspired optimization as core mechanisms, demonstrating their efficacy in accelerating the search process. The core claim posits that QEA's quantum properties will yield superior performance compared to classical algorithms. This research investigates the feasibility of leveraging quantum phenomena to improve the efficiency of evolutionary optimization, ultimately offering a pathway toward more robust and rapid solution discovery.

DOI: 10.5281/zenodo.22264773

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