Quantum-Enhanced Iterative Refinement of Neural Network Weights

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum-enhanced iterative refinement of neural network weights is a promising approach to accelerate and improve the training process of complex models. This paper explores the application of quantum-inspired techniques to optimize neural network weights, focusing on minimizing energy and identifying key weights for improved performance. We present a novel method incorporating quantum-enhanced gradient descent and a quantum-inspired energy function to achieve faster convergence and enhanced robustness. The potential impact of this research lies in significantly improving the efficiency and effectiveness of training deep neural networks across a range of applications. The study demonstrates the efficacy of this approach through theoretical analysis and preliminary experimental results, showcasing its potential to revolutionize neural network training methodologies.

DOI: 10.5281/zenodo.22249176

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