GroverAttention: Quantum-Enhanced Attention Mechanism for Effcient Transformers

Transformer-based models have revolutionized natural language processing (NLP), but their computational complexity remains a bottleneck. This paper proposes a novel approach to integrating Quantum Attention into Transformers, leveraging Grover’s algorithm to optimize query-key matching. By replacing classical attention mechanisms with quantum-enhanced token selection, we achieve reduced computational cost and improved inference speed. Experimental results on real-world datasets demonstrate the effectiveness of our hybrid Quantum-Classical Transformer, showing an improvement in accuracy with reduced training overhead. Our findings suggest that quantum-assisted attention has the potential to significantly optimize deep learning architectures for NLP applications.