Quantum Machine Learning Dynamic Topology Adjustment

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) has emerged as a promising field, offering the potential to revolutionize machine learning through novel algorithms and techniques. This paper introduces a new framework, Quantum Machine Learning Dynamic Topology Adjustment (QMLDA), which leverages dynamic topology adjustments to enhance the modeling capabilities of quantum machine learning models. We propose a novel approach that dynamically adjusts model parameters based on the inherent topological structure of the data, enabling the algorithm to better represent complex data distributions and improve predictive accuracy. The core mechanism focuses on adaptive learning strategies that facilitate robustness and adaptability. This work addresses a critical challenge in QML: the need for models that can effectively handle high-dimensional and non-linear data. The proposed framework exhibits improved performance in numerous benchmark datasets, demonstrating a significant advancement in the design and implementation of QML.

DOI: 10.5281/zenodo.22201644

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