Quantum Adaptive Machine Learning Model Design

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper investigates the potential of quantum machine learning to enhance traditional machine learning models. The core claim is that the implementation of a quantum-enhanced learning framework utilizing quantum entanglement and state dynamics will lead to improved performance compared to classical algorithms. We explore the fundamental principles of quantum self-adaptation and how this can be leveraged to create a more robust and efficient learning process. The research focuses on a novel model design incorporating these quantum principles, aiming to overcome limitations inherent in classical approaches.

DOI: 10.5281/zenodo.22592709

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