Quantum Machine Learning: A Novel Non-Linear Activation Function

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) presents a paradigm shift in computational power, offering the potential for breakthroughs across various fields, including machine learning. This paper investigates the application of a novel non-linear activation function, designed specifically for QML, to enhance the training efficiency and generalization capabilities of quantum machine learning models. The core mechanism leverages quantum computing's unique properties – superposition, entanglement, and interference – to achieve non-linearity that can significantly improve model performance. We explore the theoretical implications and preliminary experimental results demonstrating the effectiveness of this activation function.

DOI: 10.5281/zenodo.22631566

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