Quantum-Enhanced Gradient Descent (QED)

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-Enhanced Gradient Descent (QED) is a novel approach to machine learning that integrates quantum computing principles with gradient descent algorithms. The core idea is to leverage the unique capabilities of quantum mechanics, specifically quantum entanglement and superposition, to accelerate the training process of machine learning models, particularly deep neural networks. This paper explores the potential of QED by investigating how quantum-enhanced gradient descent can significantly reduce training time and improve model parameter accuracy compared to classical gradient descent. We present a detailed theoretical framework and preliminary experimental results demonstrating the efficacy of QED in optimizing specific machine learning models. The paper also discusses the challenges and future directions of this research.

DOI: 10.5281/zenodo.22281013

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