Self-Adaptive Quantum Topology for Machine Learning

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper investigates the potential of utilizing Self-Adaptive Quantum Topology (SAQT) to enhance machine learning algorithms. Traditional machine learning relies heavily on pre-defined parameters, limiting the algorithm's adaptability and potential for improved performance. This research proposes a novel approach that leverages the dynamic properties of quantum topology to dynamically adjust quantum system parameters, fostering automated parameter optimization and ultimately enhancing machine learning model efficacy. We present a framework for implementing SAQT, detailing its core mechanism and demonstrating its efficacy through a simulated case study involving a classification task. The results indicate a significant improvement in model accuracy compared to traditional methods, highlighting the promising potential of this algorithmic paradigm.

DOI: 10.5281/zenodo.22611800

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