Quantum-Enhanced Non-Linearity in Neural Networks (QNEN)

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum-Enhanced Non-Linearity in Neural Networks (QNEN) addresses the longstanding challenge of vanishing gradients in deep neural networks, a pervasive issue hindering training stability and performance. This research proposes a novel technique, leveraging quantum-inspired gradient estimation and activation functions, to mitigate this problem. We introduce a Q-network architecture incorporating quantum-enhanced non-linearities, designed to enhance signal propagation and improve training resilience. The core mechanism centers around the judicious application of quantum-derived gradient estimation, aiming to maintain signal propagation throughout the network, thereby stabilizing training and accelerating convergence. This paper presents a comprehensive analysis of the proposed Q-network, detailing its architecture, implementation, and experimental results demonstrating its effectiveness in mitigating the vanishing gradient problem across various neural network architectures. The ultimate goal is to offer a significant improvement in training stability and performance, particularly for large and complex networks.

DOI: 10.5281/zenodo.22296866

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