Quantum-Enhanced Deep Learning Model for Text Classification

The proposed Quantum-Enhanced Deep Learning Model for Text Classification successfully demonstrates the effectiveness of integrating quantum computing concepts with classical deep learning techniques. By combining the power of BERT-based feature extraction, classical neural networks, and quantum circuits, the system achieves improved performance in sentiment classification tasks. The implementation of the Complex Quantum Kernel Self-Attention Network (CQKSAN) enhances the model’s ability to capture complex patterns and semantic relationships within text data. The use of quantum principles such as superposition and entanglement contributes to richer feature representations, leading to better classification accuracy compared to traditional models. The system also provides a user-friendly interface for uploading datasets and viewing results, making it practical for real-world applications. Experimental results show that the model performs efficiently on large and complex text inputs, delivering accurate predictions such as positive and negative sentiment classification.

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