Quantum Adaptive Machine Learning

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum Adaptive Machine Learning (QAML) presents a novel approach to machine learning by integrating quantum computing's inherent adaptability with traditional machine learning paradigms. This paper explores the development of a quantum-enhanced self-adaptive learning algorithm designed to dynamically adjust model parameters based on both data characteristics and the learning process itself. We propose a mechanism leveraging quantum computation to optimize model performance through continuous parameter refinement. The core claim is that this algorithm significantly enhances the model's generalization ability by responding to evolving data and learning dynamics.

DOI: 10.5281/zenodo.22252735

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