Quantum-Enhanced Attention Mechanism in NLP: A Hybrid Classical-Quantum Approach
This work addresses the problem of capturing complex semantic relationships in NLP for researchers and practitioners, offering a resource-efficient alternative to existing attention mechanisms, though it is incremental as it builds on hybrid quantum-classical approaches.
The paper tackles the challenge of enhancing deep learning in NLP by integrating a quantum-enhanced attention mechanism into a classical Transformer model, resulting in improved efficiency and representational capacity with fewer parameters.
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.