Quantum-Enhanced Gradient Descent for Neural Network Training

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum-enhanced gradient descent offers a potential paradigm shift in neural network training, promising faster convergence and improved generalization compared to traditional methods. This paper explores the implementation of a novel quantum-enhanced gradient descent algorithm, utilizing quantum tunneling to accelerate gradient updates. We analyze the theoretical implications, proposed implementation details, and preliminary experimental results, demonstrating the potential for significant performance gains. The core mechanism revolves around strategically introducing quantum tunneling into the gradient update process, aiming to bypass local minima and explore the parameter space more effectively. This paper provides a comprehensive overview of the proposed algorithm, including mathematical formulations and experimental validation.

DOI: 10.5281/zenodo.22221213

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