@inproceedings{rai-etal-2025-quantum,
    title = "Quantum-Enhanced Gated Recurrent Units for Part-of-Speech Tagging",
    author = "Rai, Ashutosh  and
      Pandey, Shyambabu  and
      Pakray, Partha",
    editor = "Pal, Santanu  and
      Pakray, Partha  and
      Jain, Priyanka  and
      Ekbal, Asif  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the QuantumNLP{\{}:{\}} Integrating Quantum Computing with Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Mumbai, India (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.quantumnlp-1.5/",
    pages = "26--32",
    ISBN = "979-8-89176-306-7",
    abstract = "Deep learning models for Natural Language Processing (NLP) tasks, such as Part-of-Speech (POS) tagging, usually have significant parameter counts that make them costly to train and deploy. Quantum Machine Learning (QML) offers a potential approach for building more parameter-efficient models. This paper proposes a hybrid quantum-classical gated recurrent unit model for POS tagging in code-mixed social media text. By integrating a quantum layer into the recurrent framework, our model achieved an accuracy comparable to the baseline classical model, while needing fewer parameters. Although the cut-off point in the parameters is modest in our setup, the approach is promising when scaled to deeper architectures. These results suggest that hybrid models can offer a resource-efficient alternative for NLP tasks."
}