Title: Quantum-Inspired Machine Learning for Topological Data Analysis

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-inspired machine learning aims to leverage the principles of quantum mechanics to enhance data analysis and pattern recognition. This paper introduces a novel framework, Quantum-Inspired Machine Learning (QIM), designed to mimic the structure of topological data analysis (TDA), enabling the identification of complex patterns and anomalies. We propose a 'topological density' metric as a core feature extraction method, driving the development of a machine learning model that explores the interconnectedness of data points. The core claim is that this approach offers a fundamentally different perspective on data, leading to the detection of hidden patterns and anomalies previously difficult to identify using traditional machine learning techniques. This research explores the potential of QIM to revolutionize data analysis by introducing a novel approach to feature representation and pattern detection.

DOI: 10.5281/zenodo.22280406

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