Quantum Machine Learning: A Dynamic Probability Activation Function

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) represents a paradigm shift in artificial intelligence, leveraging the unique properties of quantum mechanics to enhance computational capabilities and solve complex problems. This paper explores the application of dynamic probability distribution activation functions within QML algorithms to address challenges related to high-dimensional data and non-linear relationships. We propose a novel activation function, termed "Quantum Self-Adaptive Adjustment" (QSA), designed to dynamically adjust parameters based on quantum computations, enabling the model to learn more complex patterns and improve overall performance. This approach contrasts with traditional activation functions that rely on deterministic rules, offering a potential pathway to more sophisticated and robust machine learning models. The study demonstrates the effectiveness of QSA in various scenarios, highlighting its potential to overcome limitations of current activation functions.

DOI: 10.5281/zenodo.22203362

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