Quantum-Adaptive Random Matrix Generation

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel quantum-adaptive random matrix generation algorithm, designed to enhance data analysis and simulation through dynamic adjustment of matrix parameters. Leveraging the principles of quantum entanglement, the algorithm generates complex matrices that are tailored to specific data analysis needs, resulting in improved accuracy and efficiency. The core mechanism centers around utilizing quantum entanglement to produce matrices with enhanced properties, optimizing the matrix generation process for a wide range of applications.

DOI: 10.5281/zenodo.22289460

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