Core Claim: A Quantum-Adaptive Dynamic Matrix Decomposition Method

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces a novel quantum-adaptive dynamic matrix decomposition method, aiming to significantly enhance computational efficiency, particularly when dealing with large matrices. Traditional matrix factorization methods often exhibit limitations in scalability and speed, especially with high-dimensional datasets. Our approach leverages the unique properties of quantum self-adapted matrix decomposition, allowing for rapid and efficient breakdown of matrices. The core mechanism involves a quantum-optimized decomposition process that adapts to the matrix's structure, leading to substantial performance gains. We present a detailed analysis of the algorithm's performance and discuss its potential impact on various matrix factorization applications. This work contributes to the advancement of efficient matrix computation techniques.

DOI: 10.5281/zenodo.22255138

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