Quantum Machine Learning in Self-Organizing Structure Analysis: An Adaptive Modeling Approach

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) has emerged as a transformative field, offering unprecedented capabilities in complex data analysis and modeling. This research investigates the application of self-organizing structure analysis, a crucial technique in various domains, to QML. We propose a novel model based on self-adaptive graphs, designed to automatically adjust model parameters to enhance both accuracy and computational efficiency. The core mechanism centers around leveraging the inherent properties of self-organizing graphs to dynamically optimize model behavior. The proposed approach offers a significant advancement over traditional methods, potentially unlocking new insights and accelerating the analysis of complex systems.

DOI: 10.5281/zenodo.22296501

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