Quantum Machine Learning in Adaptive Attention Mechanisms

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is a rapidly developing field that explores the potential of quantum computers to enhance machine learning algorithms. A critical challenge in QML is the design of efficient and effective attention mechanisms, which are fundamental to many deep learning architectures, particularly in natural language processing and computer vision. This paper investigates the application of adaptive attention mechanisms within the context of QML, specifically focusing on the design of a novel self-adaptive attention mechanism leveraging quantum properties. We propose a framework that dynamically adjusts the attention weights based on the quantum state, allowing for a more nuanced and efficient modeling process. The core claim is that this design will result in improved model performance and enhanced learning efficiency compared to traditional attention mechanisms, utilizing the inherent advantages of quantum computing. This work presents a theoretical analysis and preliminary implementation of this novel approach.

DOI: 10.5281/zenodo.22225114

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