Quantum Random Matrix Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum Random Matrix Optimization (QROM) presents a novel approach to machine learning and data analysis by leveraging the principles of quantum mechanics, specifically quantum superposition and entanglement, to optimize the parameters of random matrices. This research explores a quantum algorithm designed to enhance the performance of machine learning models through strategically designed random matrices, aiming to outperform classical optimization techniques in specific scenarios. The core mechanism relies on manipulating the state space of random matrices to guide the learning process, potentially leading to more efficient and accurate results. The study investigates the potential benefits and challenges of employing quantum algorithms in this context, highlighting the need for further exploration and refinement of the proposed framework.

DOI: 10.5281/zenodo.22281334

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