基于量子拓扑的概率机器学习

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper explores the potential of incorporating quantum topology into probabilistic machine learning models. The core idea is to leverage the inherent structural properties of quantum states to enhance the accuracy and robustness of machine learning systems. We propose a novel approach that utilizes quantum topology as a guiding principle for model design and training, aiming to overcome limitations of traditional methods. This work presents a conceptual framework, detailing the mathematical principles and potential applications of this approach, and highlights its potential for advancing the field of probabilistic machine learning. The focus is on demonstrating a practical pathway for improving model performance through the application of quantum topology.

DOI: 10.5281/zenodo.22581876

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