基于量子纠缠的自适应几何优化

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper investigates the potential of leveraging quantum entanglement to revolutionize geometric optimization. Traditional geometric optimization algorithms often struggle with complex, high-dimensional shapes due to computational limitations. We propose a novel algorithm, Quantum Geometry Optimizer (QGO), that utilizes entangled quantum states to represent and manipulate geometric configurations, offering a potential pathway to significantly enhance optimization efficiency and handle intricate shapes. The core mechanism centers around exploiting the unique properties of entanglement to create a dynamic and adaptive optimization process. This work provides a theoretical foundation and preliminary experimental validation demonstrating the efficacy of this approach. The paper concludes with a discussion of future research directions and the implications of this technology for various fields.

DOI: 10.5281/zenodo.22295511

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