Dynamic Matrix Decomposition

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the potential of dynamic matrix decomposition (DMD) to address the challenges associated with large matrix manipulation. Traditional methods often struggle with scalability and computational complexity, particularly when dealing with matrices exceeding typical computational limits. This work proposes a novel approach leveraging quantum entanglement and key distribution to dramatically accelerate the decomposition process. We explore the theoretical foundations and initial experimental validation of DMD, highlighting its potential for handling significantly larger matrices than currently feasible. The core mechanism centers around quantum state manipulation and its application to matrix inversion, demonstrating a significant improvement in computational speed and robustness compared to classical approaches. The paper concludes with a discussion of future research directions, emphasizing the transformative impact of quantum computation on matrix analysis.

DOI: 10.5281/zenodo.22192475

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