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        <title>Quantum-Enhanced Gated Recurrent Units for Part-of-Speech Tagging</title>
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    <name type="personal">
        <namePart type="given">Ashutosh</namePart>
        <namePart type="family">Rai</namePart>
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    <name type="personal">
        <namePart type="given">Shyambabu</namePart>
        <namePart type="family">Pandey</namePart>
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    <name type="personal">
        <namePart type="given">Partha</namePart>
        <namePart type="family">Pakray</namePart>
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            <title>Proceedings of the QuantumNLP{:} Integrating Quantum Computing with Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Santanu</namePart>
            <namePart type="family">Pal</namePart>
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        <name type="personal">
            <namePart type="given">Partha</namePart>
            <namePart type="family">Pakray</namePart>
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        <name type="personal">
            <namePart type="given">Priyanka</namePart>
            <namePart type="family">Jain</namePart>
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            <namePart type="given">Asif</namePart>
            <namePart type="family">Ekbal</namePart>
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        <name type="personal">
            <namePart type="given">Sivaji</namePart>
            <namePart type="family">Bandyopadhyay</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <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.</abstract>
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        <url>https://aclanthology.org/2025.quantumnlp-1.5/</url>
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            <start>26</start>
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%0 Conference Proceedings
%T Quantum-Enhanced Gated Recurrent Units for Part-of-Speech Tagging
%A Rai, Ashutosh
%A Pandey, Shyambabu
%A Pakray, Partha
%Y Pal, Santanu
%Y Pakray, Partha
%Y Jain, Priyanka
%Y Ekbal, Asif
%Y Bandyopadhyay, Sivaji
%S Proceedings of the QuantumNLP{:} Integrating Quantum Computing with Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Mumbai, India (Hybrid)
%@ 979-8-89176-306-7
%F rai-etal-2025-quantum
%X 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.
%U https://aclanthology.org/2025.quantumnlp-1.5/
%P 26-32

Markdown (Informal)

[Quantum-Enhanced Gated Recurrent Units for Part-of-Speech Tagging](https://aclanthology.org/2025.quantumnlp-1.5/) (Rai et al., QuantumNLP 2025)

• Quantum-Enhanced Gated Recurrent Units for Part-of-Speech Tagging (Rai et al., QuantumNLP 2025)

ACL

• Ashutosh Rai, Shyambabu Pandey, and Partha Pakray. 2025. Quantum-Enhanced Gated Recurrent Units for Part-of-Speech Tagging. In Proceedings of the QuantumNLP{:} Integrating Quantum Computing with Natural Language Processing, pages 26–32, Mumbai, India (Hybrid). Association for Computational Linguistics.