Quantum Machine Learning: A Topological Structure Analysis

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) has emerged as a rapidly growing field, offering the potential to revolutionize various computational tasks. However, the development of effective QML algorithms often relies on carefully crafted models and optimization strategies. This paper investigates a novel approach to QML – a topological structure analysis – that aims to optimize both training and inference processes, potentially unlocking new quantum machine learning models with enhanced properties. This work proposes a method that leverages the inherent topological properties of quantum systems to guide model design and refinement, leading to improved performance and a more robust understanding of the underlying quantum mechanics. The core mechanism centers on establishing a topological representation of the model's dynamics, allowing for targeted optimization strategies that are not easily achieved through traditional methods. We demonstrate the effectiveness of this approach through theoretical analysis and preliminary simulations, highlighting its potential to address limitations in current QML techniques.

DOI: 10.5281/zenodo.22635594

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