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.
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.
Word Sense Disambiguation (WSD) is one of the unresolved issues in natural language processing because polysemous words are inherently ambiguous and require semantic interpretation based on the context. Transformerbased models have made considerable progress in contextual modelling still, they cannot address fine-grained sense distinctions, long-range dependencies, and sentences containing multiple ambiguous words interacting with each other. At the same time quantum-inspired methods also take advantage of superposition and interference to model ambiguity, although generally only at the word level, and do not do contextual propagation. This paper presents Q-EntangleFormer, a new hybrid quantum-classical Transformer based on a unified quantum multi-sense superposition encoding, contextual entanglement of words, and quantum-enhanced attention of deep contextual WSD. The unclear word is represented as a quantum superposition of the possible meanings, the dependence between the context and sense is represented by entanglement networks and the hybrid attention model can combine classical and quantum signals with a fusion gate. Empirical testing of standard WSD metrics indicates that QEntangleFormer has significantly better accuracy than both classical Transformer models and current quantum WSD approaches. The framework also shows that it is parameterefficiently scalable and that it is better understood through amplitude-based semantic reasoning. It therefore connects quantum representation theory and contextual semantic modelling and points to a new direction of quantum-augmented natural language understanding.
Visual word sense disambiguation focuses on polysemous words, where candidate images can be easily confused. Traditional methods use classical probability to calculate the likelihood of an image matching each gloss of the target word, summing these to form a posterior probability. However, due to the challenge of semantic uncertainty, glosses from different sources inevitably carry semantic biases, which can lead to biased disambiguation results. Inspired by quantum superposition in modeling uncertainty, this paper proposes a Quantum Inference Model for Unsupervised Visual Word Sense Disambiguation (Q-VWSD). It encodes multiple glosses of the target word into a superposition state to mitigate semantic biases. Then, the quantum circuit is executed, and the results are observed. By formalizing our method, we find that Q-VWSD is a quantum generalization of the method based on classical probability. Building on this, we further designed a heuristic version of Q-VWSD that can run more efficiently on classical computing. The experiments demonstrate that our method outperforms state-of-the-art classical methods, particularly by effectively leveraging non-specialized glosses from large language models, which further enhances performance. Our approach showcases the potential of quantum machine learning in practical applications and provides a case for leveraging quantum modeling advantages on classical computers while quantum hardware remains immature.
This paper presents a novel algorithm for Word Sense Disambiguation (WSD) based on Quantum Probability Theory.The Quantum WSD algorithm requires concepts representations as vectors in the complex domain and thus we have developed a technique for computing complex word and sentence embeddings based on the Paragraph Vectors algorithm.Despite the proposed method is quite simple and that it does not require long training phases, when it is evaluated on a standardized benchmark for this task it exhibits state-of-the-art (SOTA) performances.
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
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.