基于量子纠缠的自适应矩阵分解

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper presents a novel approach to matrix factorization leveraging the principles of quantum entanglement. We propose a framework centered around the instantaneous correlation between entangled states, aiming to significantly enhance the efficiency and accuracy of matrix decomposition. The core mechanism involves dynamically adjusting the entanglement parameters during the factorization process, optimizing the overall algorithm to reduce computational complexity. The study explores the potential of this approach to overcome limitations of traditional methods, particularly in scenarios involving high-dimensional data and complex eigenvalue problems. We demonstrate the framework's effectiveness through theoretical analysis and preliminary experimental validation.

DOI: 10.5281/zenodo.22320904

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