Quantum Neural Networks: Adaptive Activation for Enhanced Efficiency

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum Neural Networks (QNNs) represent a paradigm shift in machine learning, leveraging the unique properties of quantum systems to potentially surpass classical neural networks. However, the inherent complexity of quantum systems presents significant challenges in designing efficient and adaptable architectures. This paper explores the integration of adaptive activation functions into QNNs to address this challenge. We propose a novel mechanism for dynamically adjusting activation function parameters, fostering increased efficiency and improved model performance. The core of our work lies in a system that employs a feedback loop, iteratively refining the activation function based on the network's internal state and the task at hand. This adaptive process significantly reduces computational costs compared to static activation functions. We demonstrate the effectiveness of this approach through simulations and preliminary experiments, showcasing enhanced performance in a targeted task. The resulting QNN exhibits superior results in terms of accuracy and speed, highlighting the potential of this novel activation mechanism.

DOI: 10.5281/zenodo.22243973

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