基于量子计算的非线性自适应进化算法

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper introduces a novel quantum computing-based evolutionary algorithm designed for rapid exploration of complex environments. Traditional evolutionary algorithms often struggle with the complexity of such environments, necessitating computationally intensive and potentially suboptimal solutions. We propose a quantum-enhanced evolutionary process that leverages quantum simulation and entanglement to accelerate the search for optimal solutions. This algorithm utilizes a quantum-inspired approach to represent and manipulate evolutionary parameters, allowing for more efficient exploration of the solution space. The core mechanism involves quantum entanglement to facilitate rapid pattern recognition and adaptation, ultimately leading to faster convergence and improved performance in challenging scenarios. We demonstrate the effectiveness of this approach through simulations, showcasing its ability to outperform traditional evolutionary algorithms in a range of complex problem domains. The paper concludes with a discussion of the potential impact of this technology on various fields, emphasizing its promise for tackling problems currently intractable with classical methods.

DOI: 10.5281/zenodo.22154041

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