Quantum machine learning has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learning tasks. In this work, we investigate the applicability of hybrid quantum-classical neural networks to sentiment analysis, a central problem in natural language processing. We focus on a dataset of tweets related to COVID-19, where the textual content is vectorized using TF-IDF and fed into both classical feedforward networks and hybrid architectures incorporating parameterized quantum circuits. Our results show that hybrid models can achieve accuracy comparable to the classical baseline, while exhibiting distinct learning dynamics, especially in terms of validation loss and accuracy, that suggest a richer representational capacity. Moreover, when applying transfer learning to an SMS spam classification task, the hybrid models consistently outperform the classical counterpart, achieving an accuracy increase of 15 percentage points (from 66% to 81%) on the spam class, demonstrating enhanced generalization. These findings highlight the feasibility of employing QML for natural language processing and point toward the potential advantages of hybrid models as quantum hardware continues to advance.
Quantum machine learning has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learning tasks. In this work, we investigate the applicability of hybrid quantum-classical neural networks to sentiment analysis, a central problem in natural language processing. We focus on a dataset of tweets related to COVID-19, where the textual content is vectorized using TF-IDF and fed into both classical feedforward networks and hybrid architectures incorporating parameterized quantum circuits. Our results show that hybrid models can achieve accuracy comparable to the classical baseline, while exhibiting distinct learning dynamics, especially in terms of validation loss and accuracy, that suggest a richer representational capacity. Moreover, when applying transfer learning to an SMS spam classification task, the hybrid models consistently outperform the classical counterpart, achieving an accuracy increase of 15 percentage points (from 66% to 81%) on the spam class, demonstrating enhanced generalization. These findings highlight the feasibility of employing QML for natural language processing and point toward the potential advantages of hybrid models as quantum hardware continues to advance.
Sentiment analysis (SA) is an essential component of natural language processing (NLP) and is used to analyze sentiments, attitudes, and emotional tones in various contexts. It provides valuable insights into public opinion, customer feedback, and user experiences. Researchers have developed various classical machine learning (ML) and neuro-fuzzy approaches to address the exponential growth of data and the complexity of language structures in SA. However, these approaches often fail to determine the optimal number of clusters, interpret results accurately, handle noise or outliers efficiently, and scale effectively to high-dimensional data. In addition, they are frequently insensitive to input variations. In this article, we propose a novel hybrid approach for SA called the quantum fuzzy neural network (QFNN), which leverages quantum properties and incorporates a fuzzy layer to overcome the limitations of classical SA algorithms. In this study, we test the proposed approach on two Twitter datasets: the Coronavirus Tweets Dataset (CVTD) and the General Sentimental Tweets Dataset (GSTD), and compare it with classical and hybrid algorithms. The results show that QFNN outperforms all classical, quantum, and hybrid algorithms, achieving 100% and 90% accuracy in the case of CVTD and GSTD, respectively. Furthermore, QFNN demonstrates its robustness against six different noise models, providing the potential to tackle the computational complexity associated with SA on a large scale in a noisy environment. The proposed approach expedites sentiment data processing and precisely analyzes different forms of textual data, thus improving sentiment classification and insights associated with SA.
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.
Abstract Quantum Machine Learning (QML) is a promising field that combines the power of quantum computing with machine learning. Variational quantum circuits, where parameters of circuits are learned classically, have been widely used in many recent applications of QML. This is an instance of a hybrid quantum-classical framework, where both classical and quantum components are present. However, applying these techniques to applications involving massive data is a challenging task. One way to overcome this, is using the concept of classical-quantum transfer learning with the help of dressed quantum circuit, introduced recently, where the underlying neural architecture is pre-trained classically, but at the final steps (decision layer), a quantum circuit is used, followed by quantum measurements and post-processing to classify images with high precision. In this paper, we applied hybrid classical-quantum transfer learning to another task of massive data processing, i.e. Natural Language Processing (NLP). We show how to (binary) classify short texts (e.g., SMS) with classical-quantum transfer learning, which was originally applied to image processing only. Our quantum network was pre-trained by Bidirectional Encoder Representations from the Transformers (BERT) model, and its variational quantum circuit is fine-tuned for text processing. We evaluated the performance of our hybrid neural architecture using the Receiver Operating Characteristic (ROC) curve, which is typically used in the evaluation of classification problems. The results indicate high precision as well as lower loss function. To our knowledge, our work is the first application of quantum transfer learning to the area of NLP. Finally a comparison with a tool that uses learning but in a different way than transfer learning is presented
Combining variational quantum circuits with classical neural network models is a potential direction for quantum natural language processing (QNLP) in the NISQ (Noisy Intermediate-Scale Quantum) era. We propose a hybrid classical-quantum neural network that enhances a transformer architecture in two key ways: first, by incorporating optimized variational quantum circuits into the self-attention and multilayer perceptron components, and second, by integrating a hybrid convolutional module for effective dimensionality reduction which can reduce the quantum resources required for subsequent layers and enhances the model’s trainability by mitigating issues like the barren plateau phenomenon, thus making it feasible to construct a deeper and more powerful network architecture. Our experiments on three widely-used sentiment analysis datasets demonstrate that the proposed model achieves comparable or even superior performance in terms of accuracy and F1 score compared to classical transformer-based sentiment classification models with similar structures, while reducing the number of parameters through the utilization of quantum advantages.
Exploiting quantum properties to improve performance of different tasks in Natural Language Processing (NLP) and other domains has increasingly becoming a successful trend to deal complex language phenomena or to fill task or domain-specific gaps with an approach that needs less data and minor computational resources. The field that has, to date, yielded more quantum-based attention is the retrieval and classification of textual data. This work aims to replicate the excellent results of hybrid quantum approaches for syntactic tasks on semantic classification tasks. In detail, a quantum machine learning algorithm, namely, the Variational Quantum Classifier (VQC), is used to perform sentiment analysis classification tasks. This algorithm can deduce the relationships between input features and their corresponding class affiliations using a parametrized quantum circuit and an encoding layer that translates classical data into quantum states. The approach has been tested on a well-known benchmark annotated dataset used for the Italian language, and the results have been compared to existing baselines, pointing out state-of-the-art scores.
This paper presents SentiQNN (Sentiment Quantum Neural Network), a novel quantum-inspired neural network architecture specifically designed for sentiment analysis tasks in natural language processing. SentiQNN leverages quantum computing principles including superposition, entanglement, and quantum measurement to enhance traditional neural network capabilities for text classification. The model incorporates complex-valued embeddings, quantum mixture operations, and multi-task learning with sentiment lexicon integration to achieve superior performance in sentiment classification tasks. Through extensive experimentation across multiple benchmark datasets, SentiQNN demonstrates state-of-the-art performance with 88.11% accuracy, outperforming traditional classical approaches by 3-7%. The architecture introduces innovative quantum state preparation layers, quantum circuit operations, and quantum measurement mechanisms specifically optimized for natural language understanding. Our comprehensive evaluation reveals that quantum-inspired approaches can significantly enhance sentiment analysis performance while maintaining computational efficiency. The findings contribute to the emerging field of quantum machine learning and provide a foundation for future research in quantum-enhanced natural language processing systems.