Quantum Natural Language Processing for Next-Generation Intent Recognition: Foundations, Techniques, And Future Directions

Abstract

AI-based systems in virtual assistants, conversation agents, and automated platforms rely heavily on intent recognition in Natural Language Processing (NLP). Using TF-IDF with classic classifiers results in reliable baselines, although using major transformer models, such as BERT, offers a deeper analysis and requires many resources. New developments in Quantum Machine Learning (QML) suggest new architectures that might help solve language problems. This study examined different types of intent classification models. A model can be set up using (i) classical TF-IDF + Logistic Regression, (ii) transformer-based BERT, and (iii) a quantum-classical variational quantum circuit (VQC) model from PennyLane. The models were evaluated using a custom JSON dataset with many intent examples. The results indicate that the classical model achieved the best performance among all, generating an accuracy of 68.97% and an F1-score of 70.69%, compared to the performance of BERT (accuracy=65.52% and F1-score = 63.91%) and the VQC model (accuracy=31.03% and F1-score = 25.65%). In addition to explaining the differences in the performance of the models, the visualizations pointed out their sensitive reactions to the structure and level of data in the categories. This paper discusses the advantages and disadvantages of different models, the challenges of using current quantum simulation tools, and the usefulness of QNLP in real-time and privacy-sensitive applications. At this current step in quantum model building, the findings here provide important guidance for future efforts on explainable, scalable, and hardware-accelerated QNLP.