Quantum Machine Learning: A Novel Activation Function Design Based on Quantum Adaptive Adjustment

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers the potential to revolutionize machine learning by leveraging the unique properties of quantum mechanics. This paper investigates the application of quantum adaptive adjustment (QAA) as a novel activation function for quantum machine learning models. We propose a QAA activation function that dynamically adjusts the activation threshold based on the input data, enabling the model to learn more complex patterns and enhance performance, particularly when dealing with high-dimensional datasets and non-linear relationships. The core mechanism involves integrating a quantum-inspired adjustment algorithm that iteratively refines the activation function to better represent the underlying data distribution. We demonstrate the effectiveness of this QAA activation function through theoretical analysis and experimental results on benchmark datasets. The paper concludes that incorporating QAA into the activation function significantly improves model accuracy and generalization ability.

DOI: 10.5281/zenodo.22203414

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