基于量子纠缠的动态几何优化算法

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper presents a novel dynamic geometric optimization algorithm based on the principles of quantum entanglement. Traditional geometric optimization methods often rely on manual parameter tuning, limiting efficiency and requiring significant human intervention. This research introduces a new algorithm that leverages quantum entanglement to dynamically adjust geometric transformations, resulting in enhanced optimization performance. The core mechanism involves creating entangled states to represent geometric parameters, enabling instantaneous adjustments and accelerating the optimization process. We detail the algorithm's design, implementation, and preliminary results, demonstrating significant improvements in optimization speed and quality compared to traditional methods. The paper concludes with a discussion of the potential implications of this approach for a wide range of geometric optimization problems.

DOI: 10.5281/zenodo.22225353

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