Quantum-Enhanced Gradient Descent for Optimization of Complex Neural Networks

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-enhanced gradient descent (QED) offers a promising avenue for accelerating and improving the training of complex neural networks. This paper explores the potential of leveraging quantum superposition to enhance the optimization process, specifically focusing on its application to neural network weight adjustment. We detail a novel QED algorithm that employs quantum superposition to explore a larger search space simultaneously, thereby reducing computational costs and potentially enhancing convergence speed. We present a preliminary analysis demonstrating the benefits of this approach compared to traditional gradient descent methods. The core mechanism centers on the strategic use of quantum superposition to facilitate exploration of a wider range of potential solution pathways within the optimization landscape. This paper will provide a comprehensive overview of the proposed algorithm, its theoretical underpinnings, and preliminary experimental results.

DOI: 10.5281/zenodo.22187174

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