Quantum-Enhanced Attention Mechanism in NLP: A Hybrid Classical-Quantum Approach

TL;DR

This paper introduces a hybrid classical-quantum Transformer model for NLP that leverages quantum-enhanced attention mechanisms to improve semantic understanding and efficiency, demonstrating promising results across multiple benchmarks.

Contribution

It presents the first integration of quantum-enhanced attention into a Transformer model, combining quantum variational circuits with classical NLP architectures.

Findings

Quantum attention yields more coherent attention maps

Model achieves better performance with fewer parameters

Enhanced semantic representation in NLP tasks

Abstract

Recent advances in quantum computing have opened new pathways for enhancing deep learning architectures, particularly in domains characterized by high-dimensional and context-rich data such as natural language processing (NLP). In this work, we present a hybrid classical-quantum Transformer model that integrates a quantum-enhanced attention mechanism into the standard classical architecture. By embedding token representations into a quantum Hilbert space via parameterized variational circuits and exploiting entanglement-aware kernel similarities, the model captures complex semantic relationships beyond the reach of conventional dot-product attention. We demonstrate the effectiveness of this approach across diverse NLP benchmarks, showing improvements in both efficiency and representational capacity. The results section reveal that the quantum attention layer yields globally coherent attention maps and more separable latent features, while requiring comparatively fewer parameters than classical counterparts. These findings highlight the potential of quantum-classical hybrid models to serve as a powerful and resource-efficient alternative to existing attention mechanisms in NLP.