Title: Quantum-Guided Neural Network Propagation (QGNNP)

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-Guided Neural Network Propagation (QGNNP) is a novel technique for accelerating neural network training by leveraging quantum mechanics to enhance gradient calculations. Traditional methods often struggle with the computational complexity of training large neural networks, particularly during optimization. This paper introduces a QGNNP architecture that utilizes quantum gates to significantly speed up gradient computations, leading to improved learning speed and generalization capabilities. We demonstrate the effectiveness of QGNNP through theoretical analysis and empirical testing on a diverse set of neural network architectures. This research addresses a critical bottleneck in neural network development, offering a promising path towards more efficient and robust machine learning models.

DOI: 10.5281/zenodo.22198370

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