基于量子纠缠的动态矩阵的自适应聚类算法

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel dynamic matrix-based clustering algorithm leveraging the unique properties of quantum entanglement. Traditional clustering algorithms often struggle with complex data patterns and require significant computational resources. This work proposes a new approach that utilizes quantum entanglement to accelerate matrix operations and improve the accuracy and diversity of the resulting clusters. The algorithm iteratively evolves a dynamic matrix representing the data, allowing for a more nuanced and adaptive clustering process. We demonstrate the effectiveness of this method through simulations and preliminary experiments, showcasing its potential to outperform conventional clustering techniques in several benchmark datasets. The core mechanism focuses on the application of entanglement to enhance the computational efficiency and feature representation within the matrix operations, ultimately leading to superior clustering outcomes.

DOI: 10.5281/zenodo.22291052

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