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

Transformer-based models have achieved remarkable results in natural language processing (NLP) tasks such as text classification and machine translation. However, their computational complexity and resource demands pose challenges for scalability and accessibility. This research proposes a hybrid quantum-classical transformer model that integrates a quantum-enhanced attention mechanism to address these limitations. By leveraging quantum kernel similarity and variational quantum circuits (VQC), the model captures intricate token dependencies while improving computational efficiency. Experimental results on the IMDb dataset demonstrate that the quantum-enhanced model outperforms the classical baseline across all key metrics, achieving a 1.5% improvement in accuracy (65.5% vs. 64%), precision, recall, and F1 score. Statistical significance tests validate these improvements, highlighting the robustness of the quantum approach. These findings illustrate the transformative potential of quantum-enhanced attention mechanisms in optimizing NLP architectures for real-world applications.

Quantum Convolutional Neural Network: A Hybrid Quantum-Classical Approach for Iris Dataset Classification

This paper presents a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The quantum circuit is designed to encode the features of the Iris dataset using angle embedding and entangling gates, thereby capturing complex feature relationships that are difficult for classical models alone. The model, which we term a Quantum Convolutional Neural Network (QCNN), was trained over 20 epochs, achieving a perfect 100% accuracy on the Iris dataset test set on 16 epoch. Our results demonstrate the potential of quantum-enhanced models in supervised learning tasks, particularly in efficiently encoding and processing data using quantum resources. We detail the quantum circuit design, parameterized gate selection, and the integration of the quantum layer with classical neural network components. This work contributes to the growing body of research on hybrid quantum-classical models and their applicability to real-world datasets.