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Quantum Machine Learning: Adaptive Attention Mechanisms

Jincheng Zhang

Published Sep 1, 2026• 0 citations
Publisher

Abstract

Quantum machine learning (QML) presents a paradigm shift in computational capabilities, offering the potential to solve complex problems previously intractable for classical algorithms. This paper explores the application of quantum machine learning to a novel attention mechanism, specifically designed to enhance performance in handling long sequences. We introduce a quantum-enhanced attention mechanism leveraging quantum entanglement and quantum interference to dynamically adjust the attention weights, ultimately resulting in improved model performance. The core of this approach involves the strategic manipulation of quantum states to facilitate faster convergence and better representation of complex data relationships. This work provides a foundational investigation into the potential of quantum entanglement to revolutionize attention mechanisms, demonstrating a tangible improvement in handling long sequences through a novel design.

DOI
10.5281/zenodo.22225113

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