Quantum-Enhanced Evolutionary Algorithms for Complex Parameter Space

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper investigates the application of quantum-enhanced evolutionary algorithms to complex parameter space optimization. Traditional evolutionary algorithms, while powerful, often struggle with the vastness and non-linearity of these spaces, leading to slow convergence and limited solution quality. We propose a novel approach leveraging quantum annealing and quantum sampling techniques to enhance exploration and optimization capabilities, addressing these limitations. The core claim is that quantum-enhanced evolutionary algorithms offer a significant speedup and improved solution quality for complex parameter space problems, particularly when traditional methods fail to perform adequately. This work introduces a framework for utilizing quantum phenomena to guide the evolutionary process, offering a potential paradigm shift in optimization algorithms.

DOI: 10.5281/zenodo.22272549

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