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 are evaluated using LightGBM and deep neural network classifiers.Experimental results shows that the hybrid model incorporating quantuminspired 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.
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 are evaluated using LightGBM and deep neural network classifiers.Experimental results shows that the hybrid model incorporating quantuminspired 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.
Sentence classification is a core task in Natural Language Processing, and sentiment analysis in particular remains difficult because of contextual ambiguity, semantic complexity, and the overlap between closely related sentiment classes. This paper presents a hybrid quantum-classical architecture for sentence classification that combines classical deep learning with quantum feature extraction. The classical branch employs a Feedforward Neural Network (FNN) and a Long Short-Term Memory (LSTM) network to learn complementary semantic and contextual representations, while the quantum branch reduces TF-IDF features through Truncated Singular Value Decomposition (SVD) and encodes them into a parameterized quantum circuit built with Qiskit. The classical and quantum representations are combined through feature-level fusion and classified using a MultiLayer Perceptron (MLP). The framework is evaluated on the SST-2 dataset for binary sentiment classification and on a merged SST-2 + SST-5 dataset for multi-class classification. The proposed hybrid model achieves an accuracy of 90.5%, precision of 89.9%, recall of 90.1%, and F1-score of 90.1% on SST-2, with a ROC-AUC close to 0.97, exceeding the individual FNN and LSTM baselines. On the merged multi-class dataset, the hybrid model attains 66.65% accuracy against 65.15% for FNN and 62.65% for LSTM. These results indicate that feature-level integration of classical and quantum representations can improve sentence classification performance within currently available quantum simulation environments.
Large language and sentence-embedding models provide rich semantic representations, but their high dimensionality poses a challenge for near-term quantum machine learning (QML), where quantum circuits can process only a limited number of input features. We investigate a hybrid quantum-classical pipeline that transforms high-dimensional sentence embeddings into compact representations for variational quantum classification. The workflow combines a pretrained sentence-embedding model, dimensionality reduction, angle encoding, a variational quantum circuit (VQC), and a classical decision layer. We systematically compare principal component analysis (PCA), neighborhood components analysis (NCA), and linear discriminant analysis (LDA), covering both unsupervised and supervised dimensionality reduction. Using the TREC question-classification dataset, we study the relationship between representation dimensionality, information retention, qubit count, and classification performance. Preliminary PCA experiments reveal a strong information bottleneck: reducing 768-dimensional embeddings to 3, 4, 5, and 8 dimensions retains about 8.2%, 10.2%, 11.9%, and 16.4% of the variance, with corresponding classification accuracies of 50.3%, 51.2%, 57.9%, and 63.4%. In contrast, supervised reduction is substantially more efficient. LDA reaches 85.3% accuracy and NCA reaches 83.1% using only 5 dimensions, under a leakage-free cross-validation protocol, compared with 85.1% for a full 384-dimensional classical baseline. These results indicate that supervised dimensionality reduction can preserve task-relevant information far more effectively than variance-based compression, making compact representations a promising route toward practical hybrid quantum-classical NLP models.
Rigorous empirical validation of quantum machine learning on natural language tasks remains scarce. We evaluate a 10-qubit hybrid quantum-classical variational circuit (2,148 parameters) for paraphrase detection across three benchmarks: MRPC, Quora Question Pairs (QQP), and adversarial PAWS. On QQP (n = 10 seeds), the circuit achieves 75.53% +/- 0.75%. accuracy, statistically outperforming parameter-matched classical baselines (DeepMLP: p = 0.015, Cohen's d = 1.20; F1: p < 0.001, d = 2.32) and surpassing DistilBERT-4bit with 31,191 fewer parameters. On MRPC the optimal 2-layer variant reaches 92% of BERT-base accuracy at 54,000 lower parameter cost. Circuit depth analysis reveals a dataset-depth scaling effect; entanglement analysis via the Meyer-Wallach measure identifies multi-qubit entanglement as the primary performance driver (r = 0.85 across four variants). Adversarial evaluation on PAWS reveals emergent robustness: 98.2% recall versus 81.6% classical (+16.6 pp, d = 1.24, p < 0.001), without adversarial training. These results constitute the first systematic parameter-matched multi-benchmark empirical validation of hybrid variational circuits for NLP. All results are from classical simulation; hardware validation is future work.
Abstract Hybrid quantum–classical models for text classification are constrained by the mismatch between high-dimensional sentence embeddings and the limited number of qubits available in Noisy Intermediate-Scale Quantum (NISQ) hardware. Existing approaches rely on fixed dimensionality-reduction techniques such as PCA, which compress embeddings independently of the learning task and often remove semantic information essential for downstream classification. This work addresses this limitation by applying Neural Quantum Embedding (NQE) to sentence-level sentiment analysis. NQE jointly performs nonlinear neural compression and quantum-oriented encoding, producing compact embeddings optimized for variational circuits.We integrate NQE with a Quantum Neural Network based on a three-layer SU(4) ansatz. Experiments on three sentence-level sentiment datasets show that NQE substantially improves hybrid quantum performance, increasing accuracy by up to 38%, with corresponding improvements in precision, recall, and F1-score. Additional experiments with a classical neural network demonstrate that NQE also enhances classical performance, yielding accuracy gains of up to 6.3%. Although classical models achieve higher absolute accuracy due to greater representational capacity, the relative improvement introduced by NQE is significantly larger in the quantum setting, where embedding structure must align with strict hardware constraints.Overall, this study introduces a task-adaptive, learnable embedding mechanism that bridges the representational gap between classical embeddings and quantum circuits. The results show that NQE improves optimization stability, enhances quantum-state separability, and enables more effective hybrid sentiment classification within NISQ limitations
Quantum Natural Language Processing (QNLP) represents a novel interdisciplinaryparadigm that integrates Quantum Computing with Natural Language Processing toenhance semantic modeling and computational efficiency. Despite the remarkable successof transformer-based architectures such as BERT and GPT, classical NLP systems requirelarge-scale computational resources and extensive parameter tuning. This paper proposesa hybrid quantum–classical architecture employing parameterized quantum circuits (PQCs)integrated with classical embedding layers for text classification tasks. The frameworkutilizes classical preprocessing, quantum state encoding, variational optimization, andclassical post-measurement classification. Experimental results on benchmark sentimentanalysis datasets demonstrate competitive accuracy with significantly reduced parametercomplexity. The findings indicate that hybrid QNLP models provide enhanced semanticcompositionality and efficient representation in Hilbert space while remaining feasiblewithin Noisy Intermediate-Scale Quantum (NISQ) constraints
The paper proposes a quantum-classical hybrid method of Word Sense Disambiguation (WSD) to combine quantum inspired computation on WSD with classical natural language processing methods. The quantum state encoding is proposed to make ambiguous words coded in the framework as quantum superposition of all the possible senses, and semantic interpretations can be explored by encoding quantum states. A simulation of quantum circuits based on simulated quantum interactions was used to create a hybrid model involving variational quantum circuits and classical neural layer using the PyTorch platform. The system was trained and tested on a manually annotated collection of 5,000 polysemous sentences spanning various domains and tested on WordNet synsets. The experimental findings reveal that the hybrid quantum-classical model of WSD has 94.7 per cent accuracy and weighted F1-score of 94.0 per cent, which is better than CNN-LSTM and Transformer (BERT-base) baselines. The confusion matrix affirms enhanced sense differentiation and less semantic duplicity. The suggested architecture illustrates the possibility of quantum-enhanced natural language processing to be used in order to reach a higher contextual accuracy without compromising the computational efficiency that would be suitable with near-term quantum hardware.