Quantum-Guided Evolutionary Algorithms (QGEA)

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum-Guided Evolutionary Algorithms (QGEA) represent a novel approach to optimization by integrating quantum mechanics into the evolutionary algorithm framework. This paper proposes a method utilizing quantum simulations to enhance the search process, aiming to accelerate the discovery of optimal solutions to complex problems. We detail the core mechanism, the quantum-guided evolution process, and the expected benefits of this hybrid approach. The research explores the potential of leveraging quantum phenomena to identify subtle patterns and optimization strategies previously inaccessible to traditional evolutionary algorithms. This work introduces a framework for accelerating the solution of challenging problems across diverse fields, including machine learning, materials science, and logistics. We present a comprehensive analysis of the algorithm's performance, including simulations demonstrating its effectiveness in tackling specific optimization challenges.

DOI: 10.5281/zenodo.22498799

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