Quantum Machine Learning: A Probabilistic Activation Function Design

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) has emerged as a transformative field, offering the potential to revolutionize various computational tasks. This paper explores the design of a novel quantum activation function based on probabilistic graphs, aiming to enhance machine learning model performance, particularly when dealing with high-dimensional data and non-linear relationships. We introduce a "probability graph embedding" mechanism to simplify computation and boost model expressiveness. The core claim is that this approach allows for more accurate representation of complex system dynamics and improved performance in challenging scenarios. This work proposes a new method for encoding and processing data, leveraging the inherent properties of probabilistic graphs to achieve a more robust and adaptable quantum machine learning system.

DOI: 10.5281/zenodo.22203311

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