Quantum Machine Learning for Adaptive Topological Structure Prediction

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper explores the application of quantum machine learning to the prediction of complex topological structures. Traditional machine learning methods often struggle with the inherent complexity and high dimensionality of topological data, requiring extensive feature engineering and potentially leading to suboptimal model performance. Quantum machine learning offers the potential to address these challenges by leveraging quantum phenomena to efficiently represent and process information, enabling the prediction of intricate topological landscapes. We propose a novel quantum algorithm designed to learn a predictive model based on the underlying topological structure, incorporating this information to enhance predictive accuracy. The core mechanism focuses on utilizing quantum state manipulation to capture the essential relationships within the topology, resulting in a more robust and accurate prediction. The research investigates the potential of this approach to significantly improve the prediction of topological structures, particularly for complex and high-dimensional datasets.

DOI: 10.5281/zenodo.22292863

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