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        <title>A Hybrid Quantum-Classical Fusion for Deep Semantic Paraphrase Detection</title>
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        <namePart type="given">Devanarayanan</namePart>
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        <namePart type="given">Dheeraj</namePart>
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        <namePart type="given">Reshma</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T A Hybrid Quantum-Classical Fusion for Deep Semantic Paraphrase Detection
%A K, Devanarayanan
%A Mohamad, Fayas S.
%A Mohan, Dheeraj V.
%A Sheik, Reshma
%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 k-etal-2025-hybrid
%X 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.
%U https://aclanthology.org/2025.quantumnlp-1.4/
%P 20-25

Markdown (Informal)

[A Hybrid Quantum-Classical Fusion for Deep Semantic Paraphrase Detection](https://aclanthology.org/2025.quantumnlp-1.4/) (K et al., QuantumNLP 2025)

• A Hybrid Quantum-Classical Fusion for Deep Semantic Paraphrase Detection (K et al., QuantumNLP 2025)

ACL

• Devanarayanan K, Fayas S Mohamad, Dheeraj V Mohan, and Reshma Sheik. 2025. A Hybrid Quantum-Classical Fusion for Deep Semantic Paraphrase Detection. In Proceedings of the QuantumNLP{:} Integrating Quantum Computing with Natural Language Processing, pages 20–25, Mumbai, India (Hybrid). Association for Computational Linguistics.