量子机器学习的非线性激活函数设计

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers a paradigm shift in artificial intelligence, potentially surpassing classical machine learning through novel algorithms and architectures. This paper investigates the application of quantum mechanics to enhance machine learning, with a specific focus on the design of a novel non-linear activation function. We propose a "Quantum Probability Activation" mechanism, leveraging quantum computing's inherent probabilistic nature to learn more complex patterns and improve the performance of machine learning models, particularly in scenarios involving high-dimensional data and non-linear relationships. The core mechanism involves introducing a probabilistic activation function that dynamically adjusts the output based on quantum probabilities. This approach aims to overcome limitations of traditional activation functions in certain applications. The research explores the potential benefits of this design in various machine learning tasks, including classification and regression. We detail the theoretical underpinnings of the Quantum Probability Activation, present preliminary experimental results demonstrating its effectiveness, and conclude with a discussion of future research directions.

DOI: 10.5281/zenodo.22203192

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