Quantum Machine Learning in Adaptive Matrix Optimization

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) has emerged as a promising paradigm for enhancing machine learning algorithms, particularly in areas demanding complex computations. This research investigates a novel approach to QML, specifically focusing on the optimization of matrix operations, aiming to significantly accelerate model training and improve generalization capabilities. We propose a quantum-enhanced matrix optimization technique leveraging the unique properties of quantum machine learning to address the computational bottlenecks inherent in traditional optimization methods. The core mechanism centers around quantum algorithms designed to efficiently navigate and manipulate matrices, leading to faster convergence and enhanced model performance. This work presents a framework for adapting to quantum machine learning, offering a potential pathway to accelerate the training and deployment of complex models.

DOI: 10.5281/zenodo.22617649

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