This article proposes classical and quantum vectorization methods for text data classification, along with corresponding neural network architectures for classification based on these representations. The study applies classical and quantum variants of one-hot encoding, bag-of-words, TF-IDF, and word2vec vectorization techniques, which are respectively integrated with CNN, LSTM, MLP, and BiLSTM neural network models for text classification. All models are trained on the same dataset under identical experimental conditions, with classification accuracy and computational efficiency adopted as the primary evaluation metrics. Classical vectorization methods represent semantic and contextual properties of text in a limited manner, whereas quantum vectorization overcomes these limitations. When neural networks are trained using quantum-based representations, higher classification accuracy and significantly reduced computation time are achieved compared to classical approaches. The combined use of quantum word2vec and the BiLSTM architecture yields the highest performance among all evaluated models. The obtained results demonstrate that this approach is the most effective for text data classification, as it accurately captures semantic relationships and leverages deep neural network architectures. This study may serve as a scientific foundation for future research aimed at developing more advanced neural architectures based on quantum computing paradigms.
This article proposes classical and quantum vectorization methods for text data classification, along with corresponding neural network architectures for classification based on these representations. The study applies classical and quantum variants of one-hot encoding, bag-of-words, TF-IDF, and word2vec vectorization techniques, which are respectively integrated with CNN, LSTM, MLP, and BiLSTM neural network models for text classification. All models are trained on the same dataset under identical experimental conditions, with classification accuracy and computational efficiency adopted as the primary evaluation metrics. Classical vectorization methods represent semantic and contextual properties of text in a limited manner, whereas quantum vectorization overcomes these limitations. When neural networks are trained using quantum-based representations, higher classification accuracy and significantly reduced computation time are achieved compared to classical approaches. The combined use of quantum word2vec and the BiLSTM architecture yields the highest performance among all evaluated models. The obtained results demonstrate that this approach is the most effective for text data classification, as it accurately captures semantic relationships and leverages deep neural network architectures. This study may serve as a scientific foundation for future research aimed at developing more advanced neural architectures based on quantum computing paradigms.
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.
Quantum neural networks (QNNs) have recently attracted remarkable attention as a potential alternative to classical and deep neural network (NN) models particularly under constraints imposed by noisy intermediate-scale quantum (NISQ) devices, which support only shallow circuits with few quantum bits (qubits) and limited variational depth. However, applying QNNs to natural language processing (NLP) remains challenging because text representations are typically high-dimensional, while NISQ classifiers can only encode a small number of features at a time, requiring classical compression to enable stable training and flexible circuit configuration. In addition, many existing studies integrate QNNs into rich-text representation pipelines, where the quantum circuit is often implemented as a variational quantum circuit (VQC). Here, the VQC acts as an additional component within a larger model, and it remains unclear how the quantum component improves the classification task. Therefore, in this work, we propose a fully reproducible and compact QNN pipeline that provides a controlled setting for studying the contribution of the VQC to text classification. The model is trained end-to-end with a lightweight classical compression layer, and the overall study is supported by systematic ablation analyses of the compression layer, the classifier heads under a shared frozen low-dimensional representation, and quantum circuit structure. Our model uses the term frequency-inverse document frequency (TF-IDF) features with linear compression followed by a shallow four-qubit, two-layer VQC, implemented in PyTorch and PennyLane for end-to-end training. It is evaluated on five benchmark text classification datasets against classical baselines, including logistic regression and multilayer perceptron (MLP) models. Experimental results show that the proposed QNN achieves a competitive performance across all evaluated datasets, yielding the best results on three of the five benchmark datasets.
This article proposes an LSTM-based approach for sarcasm detection by forming quantum features from textual data. In the proposed method, words are first encoded in quantum space, and their semantic similarity is calculated using interference. Based on the encoded data, quantum TF-IDF features are generated and fed into a neural network for classification. The obtained results were compared with those of the classical approach. According to experimental results, the quantum approach achieved 83% accuracy, while the classical approach achieved 78%. Additionally, the quantum approach reduced computation time by 49% compared to the classical one. Due to its superposition and parallelism properties, the quantum approach provides higher performance. Its limitations include testing on a simulator, interdependence between data, and the inability to use real quantum technologies. Applying quantum computing through TF-IDF methods for natural language processing and sarcasm detection can be a promising direction.
Text classification remains among the most significant tasks in natural language processing and the field has been used in healthcare, law, social media analysis, and economic literature. Classical attention networks existing machine learning approaches and deep learning approaches, including LSTM, BiLSTM, Transformers, and classical attention networks, are susceptible to such problems as long-range dependencies and semantic ambiguity, as well as sparse or underrepresented text. To deal with these deficiencies, this research presents a proposed a Quantum-Enhanced Semantic Attention Network (QESAN), a quantum computing concept integrated into semantic attention-based deep learning paradigm. The proposed QESAN coded textual features using amplitude encoding, quantum superposition, and entanglement-like transformations, such that the model is able to learn not only complex relations between interdependencies, but also fairly subtle semantic relations which are generally hard to learn on classical models. The result of these quantum-enhanced representations is then sent through an attention mechanism which acts on the most semantically significant aspects to produce results that are context-sensitive and can be interpreted. The Kaggle Quantum Programming experimental results indicate that QESAN is effective with accuracy of 98.7 which is far superior than the LSTM, BiLSTM, Transformer, and classical attention networks. The results indicate the ability of QESAN to solve the conventional issues of the text classification process particularly in the assignment of dealing with the long-range dependencies, semantic ambiguity, and sparse data. Overall, the integration of the concept of quantum computing with the attention-based deep learning provides the scalable and resilient solution to the issue of the modern text classification problems of the high-performance.
Text sentiment analysis is an important task in natural language processing and has always been a hot research topic. However, in low-resource regions such as South Asia, where languages like Bengali are widely used, the research interest is relatively low compared to high-resource regions due to limited computational resources, flexible word order, and high inflectional nature of the language. With the development of quantum technology, quantum machine learning models leverage the superposition property of qubits to enhance model expressiveness and achieve faster computation compared to classical systems. To promote the development of quantum machine learning in low-resource language domains, we propose a quantum-classical hybrid architecture. This architecture utilizes a pre-trained multilingual BERT model to obtain vector representations of words and combines the proposed Batched Upload Quantum Recurrent Neural Network (BUQRNN) and Parameter Non-shared Batched Upload Quantum Recurrent Neural Network (PN-BUQRNN) as feature extraction models for sentiment analysis in Bengali. Our numerical results demonstrate that the proposed BUQRNN structure achieves a maximum accuracy improvement of 0.993% in Bengali text classification tasks while reducing average model complexity by 12%. The PN-BUQRNN structure surpasses the BUQRNN structure once again and outperforms classical architectures in certain tasks.
Image classification is a fundamental task in deep learning, and recent advances in quantum computing have generated significant interest in quantum neural networks. Traditionally, Convolutional Neural Networks (CNNs) are employed to extract image features, while Multilayer Perceptrons (MLPs) handle decision making. However, parameterized quantum circuits offer the potential to capture complex image features and define sophisticated decision boundaries. In this paper, we present a novel Hybrid Quantum–Classical Neural Network (H-QNN) for image classification, and demonstrate its effectiveness using the MNIST dataset. Our model combines quantum computing with classical supervised learning to enhance classification accuracy and computational efficiency. In this study, we detail the architecture of the H-QNN, emphasizing its capability in feature learning and image classification. Experimental results demonstrate that the proposed H-QNN model outperforms conventional deep learning methods in various training scenarios, showcasing its effectiveness in high-dimensional image classification tasks. Additionally, we explore the broader applicability of hybrid quantum–classical approaches in other domains. Our findings contribute to the growing body of work in quantum machine learning, and underscore the potential of quantum-enhanced models for image recognition and classification.