Quantum Entanglement Matrix Decomposition

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Entanglement Matrix Decomposition (QEMD) presents a novel matrix decomposition algorithm leveraging the principles of quantum entanglement. This research explores the potential of utilizing entangled states to accelerate and refine the decomposition process, offering a pathway to significantly enhance computational efficiency and accuracy, particularly in the context of large-scale data analysis and machine learning. The core mechanism centers around employing quantum entanglement to guide the computation, achieving a substantial reduction in computational complexity compared to classical methods. This paper details the theoretical framework, algorithm implementation, and preliminary results demonstrating the efficacy of QEMD.

DOI: 10.5281/zenodo.22299840

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