基于量子纠缠的矩阵变换的几何优化

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper presents a novel geometric optimization algorithm based on quantum entanglement, leveraging the unique properties of entangled states to achieve a more intelligent optimization process. We introduce a 'quantum entanglement degree' metric to quantify the entanglement strength within the optimization landscape, allowing for targeted adjustments to steer the optimization towards desired solutions. Traditional matrix factorization methods struggle with complex, high-dimensional spaces, often requiring extensive parameter tuning. Our proposed algorithm offers a more robust and adaptable approach, potentially surpassing the capabilities of existing methods, particularly in scenarios involving intricate geometric constraints. The algorithm's effectiveness is demonstrated through a series of numerical examples, showcasing improved convergence and solution quality compared to established optimization techniques. The core mechanism centers around utilizing entanglement to identify and manipulate the solution space in a more nuanced manner.

DOI: 10.5281/zenodo.22604911

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