Title: Adaptive Topology-Based Quantum Machine Learning

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum machine learning (QML) is a rapidly developing field with the potential to revolutionize various computational tasks. This paper introduces an innovative approach to QML, leveraging adaptive topology to dynamically adjust machine learning model parameters. We propose a novel framework that constructs a quantum machine learning model based on the concept of quantum geometry, allowing it to adapt to the inherent structure of input data. The core mechanism centers around a quantumization process that maps input data into a dynamically evolving topological structure. This transformation enables the model to learn effectively from complex, non-linear data. This work addresses a critical gap in QML by incorporating geometric transformations, aiming to enhance predictive accuracy compared to traditional approaches. The proposed framework demonstrates promising results through theoretical analysis and preliminary experimental validation, suggesting a significant advancement in the capabilities of QML.

DOI: 10.5281/zenodo.22621206

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