基于动态量子拓扑的机器学习模型设计

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper explores the application of dynamic quantum topology learning to machine learning, aiming to enhance model generalization capabilities. Traditional machine learning often relies on fixed features, limiting the model's ability to capture complex relationships within data. We propose a novel methodology leveraging quantum topology, specifically a dynamic quantum topology learning algorithm, to represent data through the evolution of quantum states. This approach allows for a more nuanced and adaptive learning process, potentially unlocking improved predictive performance compared to conventional methods. The core mechanism focuses on utilizing quantum state dynamics to automatically extract and represent relevant features, thereby fostering a more robust and generalizable model. The paper details the proposed algorithm, its theoretical underpinnings, and preliminary experimental results demonstrating its potential for enhancing machine learning models.

DOI: 10.5281/zenodo.22157659

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