基于动态量子态的自适应优化算法

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm based on dynamic quantum states. Traditional optimization methods are often static, limiting their effectiveness in complex and dynamic problems. We propose a new algorithm that leverages the inherent quantum state dynamics to dynamically adjust the optimization target, enabling a more efficient and adaptable optimization process. This approach aims to overcome the limitations of conventional algorithms by introducing a mechanism that continuously refines the solution space through quantum state manipulations. The core of the algorithm lies in the creation and manipulation of quantum states, which are then used to guide the optimization process. The presented method demonstrates improved performance compared to existing optimization techniques, particularly in scenarios with high dimensionality and complex objective functions. This work explores the potential of utilizing quantum phenomena to enhance optimization strategies.

DOI: 10.5281/zenodo.22465325

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