Quantum Entanglement-Based Adaptive Matrix Decomposition Method

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel quantum entanglement-based adaptive matrix decomposition method. Traditional matrix decomposition algorithms rely on fixed parameters, limiting their ability to effectively handle data variations. This work proposes a quantum-enhanced approach where entanglement is leveraged to dynamically adjust decomposition parameters, optimizing the process for diverse datasets. We demonstrate the effectiveness of this method through theoretical analysis and preliminary experimental results. The core mechanism involves quantum entanglement to create a dynamic, interconnected representation of the data, enabling adaptation during the decomposition phase. The proposed algorithm offers a significant improvement in computational efficiency and potentially enhanced accuracy compared to classical methods, particularly when dealing with complex data distributions.

DOI: 10.5281/zenodo.22321218

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