Quantum Machine Learning: Dynamic Geometric Optimization

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is a rapidly evolving field exploring the potential of quantum computing to revolutionize machine learning. This paper investigates the application of QML to dynamic geometric optimization, a crucial task in many machine learning algorithms, particularly in the design of neural networks and support vector machines. We propose a novel framework leveraging quantum algorithms to dynamically adjust model parameters, leading to enhanced efficiency and adaptability. The core claim of this work is that the developed framework enables automatic optimization of machine learning models through quantum-enhanced parameter adjustments, resulting in improved performance compared to traditional methods.

DOI: 10.5281/zenodo.22489293

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