Topological Quantum Machine Learning Algorithm (TQMA)

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces the Topological Quantum Machine Learning Algorithm (TQMA), a novel approach to machine learning that leverages the inherent properties of topology and quantum mechanics to enhance model identification and prediction capabilities. Traditional machine learning methods often rely on extensive parameter tuning, hindering model adaptability and robustness. TQMA overcomes this limitation by incorporating a dynamically evolving, topologically structured representation within the model, enabling it to better capture complex relationships within data. We present a framework that integrates a quantum-inspired optimization process within a topological graph, allowing for exploration of intricate feature spaces and improved generalization. The algorithm's core mechanism centers around the construction and application of a quantum-enhanced topological graph, which facilitates the identification of subtle patterns and correlations often missed by conventional methods. We demonstrate the efficacy of TQMA through a series of benchmark datasets, showcasing significant improvements in classification accuracy and predictive performance compared to existing state-of-the-art techniques. This paper provides a comprehensive investigation into the potential of TQMA as a paradigm shift in machine learning, offering a pathway towards more intelligent and adaptable models.

DOI: 10.5281/zenodo.22290686

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