Dynamic Topology in Quantum Machine Learning: An Adaptive Algorithm Framework

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper investigates the application of dynamic topology in quantum machine learning (QML) to develop an adaptive algorithm framework. We propose a novel approach that utilizes dynamic adjustments to the topology of a quantum machine learning model, allowing for automated parameter optimization and enhanced generalization capabilities. The core mechanism centers on dynamically reshaping the quantum system's state space through a carefully designed topology, resulting in improved model performance. The paper presents a theoretical framework and preliminary experimental results demonstrating the efficacy of this approach in achieving superior results compared to traditional methods. The focus is on creating a robust and adaptable system capable of tackling complex problems within the realm of quantum machine learning.

DOI: 10.5281/zenodo.22321488

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