@inproceedings{k-etal-2025-hybrid,
    title = "A Hybrid Quantum-Classical Fusion for Deep Semantic Paraphrase Detection",
    author = "K, Devanarayanan  and
      Mohamad, Fayas S  and
      Mohan, Dheeraj V  and
      Sheik, Reshma",
    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-1.4/",
    pages = "20--25",
    ISBN = "979-8-89176-306-7",
    abstract = "Paraphrase Detection is a core task in natural language processing (NLP) that aims to determine whether two sentences convey equivalent meanings. This work proposes a hybrid quantum{--}classical framework that integrates Sentence-BERT embeddings, simulated quantum feature encoding, and classical machine learning models to enhance semantic similarity detection. Initially, sentence pairs are embedded using Sentence-BERT and standardized through feature scaling. These representations are then transformed via rotation-based quantum circuits to capture higher-order feature interactions and non-linear dependencies. The resulting hybrid feature space, combining classical and quantum-inspired components, is evaluated using LightGBM and deep neural network classifiers. Experimental results show that the hybrid model incorporating quantum-inspired features achieved superior classification performance, yielding a 10{\%} improvement in overall accuracy outperforming standalone deep learning baselines. These findings demonstrate that quantum{--}classical fusion enhances semantic feature extraction and significantly improves paraphrase detection performance."
}