Quantum-Enhanced Gradient Descent for Deep Learning

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum-enhanced gradient descent offers a promising approach to accelerate and improve the training of deep learning models. This research investigates the potential of integrating quantum annealing techniques into the gradient descent optimization process, aiming to overcome limitations of classical methods in navigating complex loss landscapes. The core claim is that quantum annealing can significantly enhance convergence speed and robustness, particularly in deep learning scenarios. The paper presents a theoretical analysis and preliminary implementation demonstrating the effectiveness of this approach, highlighting the potential benefits and challenges of this novel methodology.

DOI: 10.5281/zenodo.22610982

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