Quantum Machine Learning: Adaptive Learning Algorithms

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper investigates the potential of quantum machine learning to revolutionize the field of machine learning through adaptive learning algorithms. Traditional machine learning algorithms often rely on static parameter settings, limiting their adaptability and potentially hindering optimal performance. We propose a novel quantum learning framework that leverages quantum entanglement and quantum computation to dynamically adjust model parameters during the learning process, resulting in enhanced learning efficiency and generalization capabilities. The core mechanism centers around utilizing quantum entanglement to create a dynamic feedback loop that continuously refines the model's parameters based on its internal state. We present a detailed theoretical analysis and preliminary experimental validation demonstrating the effectiveness of this approach in a simplified learning scenario. The study explores the potential advantages of quantum-enhanced learning over classical methods.

DOI: 10.5281/zenodo.22222457

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