基于自适应量子拓扑的机器学习特征提取

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum topology, a fascinating field of physics, offers a novel approach to manipulating space and time. This paper explores the potential of utilizing quantum topology to enhance machine learning, specifically focusing on feature extraction. We propose a novel algorithm based on dynamically adjusting parameters to leverage the inherent properties of quantum topology to extract more informative and robust features from data. The core mechanism involves leveraging the quantum topological structure of data to create a dynamic feature space, allowing for enhanced learning and improved model performance. The proposed method offers a fundamentally different perspective on feature extraction, potentially surpassing traditional techniques in certain scenarios. This work investigates the application of this algorithm to a variety of machine learning tasks, demonstrating its effectiveness through theoretical analysis and preliminary experimental validation.

DOI: 10.5281/zenodo.22465567

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