Quantum Machine Learning: Adaptive Transfer Learning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers a paradigm shift in machine learning, leveraging the unique properties of quantum mechanics to potentially surpass classical algorithms. This paper explores the application of QML to adaptive transfer learning, a technique designed to dynamically adjust model parameters based on the data distribution and feature characteristics. We propose a novel quantum framework for this process, utilizing quantum entanglement to establish a stronger connection between training data and model parameters. This approach aims to address the limitations of traditional transfer learning, particularly in scenarios with complex data distributions and variable feature spaces. The core mechanism focuses on creating a dynamic, self-adjusting model that can generalize better. This work introduces a preliminary quantum algorithm demonstrating the potential for improved transfer learning performance, highlighting the benefits of quantum entanglement in this context.

DOI: 10.5281/zenodo.22190516

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