自适应矩阵分解的量子算法

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel quantum algorithm, termed "Adaptive Matrix Decomposition," designed to accelerate matrix decomposition, leveraging the unique capabilities of quantum computation. We propose a method that utilizes adaptive learning techniques to dynamically adjust the quantum parameters required for this computationally intensive task. The core of this algorithm involves a modified approach to the matrix decomposition process, incorporating quantum superposition and entanglement to achieve significantly enhanced computational speed and adaptability. We detail the algorithm's architecture, the underlying mathematical principles, and the experimental validation demonstrating its superior performance compared to existing methods, particularly in scenarios involving high-dimensional matrices. The paper concludes with a discussion of the potential impact of this approach on various applications, including quantum machine learning and data analysis.

DOI: 10.5281/zenodo.22291677

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