Quantum Computing Random Matrix Generator Design

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel approach to random matrix generation utilizing quantum computation. Traditional random matrix generators, reliant on classical algorithms, often suffer from limitations in speed and scalability. We design a quantum random matrix generator (QRMG) capable of producing high-quality random matrices with specific properties, specifically tailored for accelerating machine learning training and inference. The core mechanism leverages quantum phenomena to achieve significantly improved generation speed compared to existing classical methods. This work explores the potential of quantum mechanics to fundamentally alter the process of generating random matrices, offering a promising pathway for enhancing the performance of computationally intensive machine learning tasks. The generated matrices will be used to simulate machine learning algorithms, showcasing the practical benefits of this new technique.

DOI: 10.5281/zenodo.22266148

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