Quantum Machine Learning: Adaptive Gradient Descent

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) represents a paradigm shift in machine learning, leveraging quantum mechanics to address computational challenges and unlock new capabilities. This paper explores the application of adaptive gradient descent, a core technique in classical machine learning, to quantum machine learning algorithms. We propose a novel quantum gradient descent framework that dynamically adjusts the learning rate and optimization direction based on the input data's inherent features. This adaptive approach aims to significantly enhance training efficiency and model generalization performance. The core mechanism involves the introduction of a 'quantum gradient descent' process, utilizing quantum entanglement and superposition to accelerate gradient computation compared to classical methods. We analyze the potential benefits and challenges of this new algorithm, demonstrating its effectiveness through a simplified conceptual model.

DOI: 10.5281/zenodo.22219546

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