基于量子拓扑的机器学习的特征提取

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the application of quantum topology for machine learning, specifically focusing on feature extraction. Traditional machine learning models rely heavily on manually designed features, limiting their adaptability and potential. We propose a novel framework leveraging quantum topology to automatically learn and extract robust features from data, ultimately enhancing the model's generalization capability. The core mechanism centers around the abstraction of data into quantum states, facilitated by quantum computation, enabling a more comprehensive and adaptable feature representation. The research explores the potential of this approach to improve model performance across diverse datasets.

DOI: 10.5281/zenodo.22193865

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