Quantum Neural Networks: A Novel Non-linear Activation Function Design

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum Neural Networks (QNNs) hold immense promise for revolutionizing artificial intelligence, offering the potential to tackle complex problems currently intractable for classical neural networks. A crucial component of QNNs is the network architecture and the activation functions that govern the flow of information. This paper explores the design of a novel non-linear activation function specifically tailored for QNNs, focusing on its ability to promote self-organizing connectivity and enhanced information transfer. We introduce a concept of "self-regulation" within the activation function's design, aiming to enhance the network's expressiveness and robustness. This research employs a rigorous mathematical analysis and demonstrates its potential to significantly improve the performance of QNNs across various tasks. The core mechanism involves manipulating the activation function's output to encourage specific patterns in the network's connectivity.

DOI: 10.5281/zenodo.22248233

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