Dynamic Topology Quantum Machine Learning Framework

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning framework based on dynamic topology. We propose a method for learning and optimizing machine learning models by leveraging the inherent topological properties of a dynamically evolving quantum structure. The framework utilizes a continuous, self-organized topology to guide the learning process, resulting in enhanced efficiency and potentially improved performance compared to traditional approaches. This work explores the potential of dynamically structured quantum systems to accelerate the training and inference of complex machine learning models. The core mechanism revolves around constructing and manipulating a quantum state that evolves through a defined topology, enabling the model to explore a vast solution space more effectively. The study highlights the advantages of this dynamic topology approach in accelerating training and improving model accuracy.

DOI: 10.5281/zenodo.22480750

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