Quantum Machine Learning: A Self-Adaptive Gradient Descent Algorithm

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Machine Learning has emerged as a promising field, offering the potential to accelerate machine learning algorithms and enhance their capabilities. This paper introduces a novel quantum machine learning algorithm, termed "Self-Adaptive Gradient Descent," that leverages the principles of quantum mechanics to dynamically adjust gradient descent parameters, enabling more efficient and accurate learning of complex neural networks. The core mechanism involves a quantum-enhanced gradient descent process, optimizing the parameters based on the data's inherent dynamics. We demonstrate the effectiveness of this algorithm through a series of experiments on a benchmark dataset, showcasing improved accuracy and faster convergence compared to traditional gradient descent methods. The core claim is that this algorithm can effectively learn complex neural networks and achieve more precise predictions.

DOI: 10.5281/zenodo.22509411

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