Quantum Machine Learning: Topological Embedding of Feature Representations

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper investigates the application of topological embedding techniques to enhance the performance and interpretability of machine learning models. The core premise is that inherent relationships within data can be better captured and exploited by representing features within a topological framework. We propose a method for constructing a topological feature graph based on the feature representations learned by machine learning models. This graph then serves as a structured representation, allowing the model to leverage the connectivity and resilience properties of topological spaces. Preliminary theoretical analysis suggests that this approach can improve generalization capabilities and provide a more intuitive understanding of model decisions. The key innovation lies in bridging the gap between the representational power of machine learning and the robust, network-based insights offered by topology. We explore potential applications and highlight the potential for future research in this emerging field.

DOI: 10.5281/zenodo.22268625

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