We investigate the application of Neural Quantum Embedding and Quantum Neural Networks for sentiment analysis using the Stanford Sentiment Treebank dataset. We adapt the Neural Quantum Embedding framework, originally worked with image classification, to textual data by employing quantum embedding techniques that maximize trace distance for better data separability. Additionally, we incorporate advanced quantum feature maps and preprocessing techniques from recent quantum machine learning studies to enhance classification performance. Our experimental results demonstrate that the quantum models catch up with the classical baselines in sentiment classification accuracy, particularly in noisy intermediate-scale quantum settings. This work highlights the feasibility and potential of quantum-assisted sentiment analysis.
We investigate the application of Neural Quantum Embedding and Quantum Neural Networks for sentiment analysis using the Stanford Sentiment Treebank dataset. We adapt the Neural Quantum Embedding framework, originally worked with image classification, to textual data by employing quantum embedding techniques that maximize trace distance for better data separability. Additionally, we incorporate advanced quantum feature maps and preprocessing techniques from recent quantum machine learning studies to enhance classification performance. Our experimental results demonstrate that the quantum models catch up with the classical baselines in sentiment classification accuracy, particularly in noisy intermediate-scale quantum settings. This work highlights the feasibility and potential of quantum-assisted sentiment analysis.
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
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.
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.
This article proposes a quantum multimodal neural network (QMNN) model that can be implemented on parameterized quantum circuits (PQCs), providing a novel avenue for processing multimodal data and performing advanced multimodal sentiment analysis tasks. The comprehensive QMNN model is structured into four fundamental blocks: multimodal data preprocessing, unimodal feature extraction, multimodal feature fusion, and optimization. Through these blocks, multimodal data are initially preprocessed and encoded into quantum states. Subsequently, visual and textual features are extracted from the quantum states and are then integrated to learn the interactions between different modalities. Finally, the model parameters are fine-tuned to optimize the sentiment analysis performance. Simulation results confirm that QMNN surpasses state-of-the-art baselines, using significantly lower input dimensions and substantially fewer parameters than classical models. Furthermore, the entanglement, integrity, robustness, and scalability of the model are analyzed in depth. Internally, the strong entanglement within the multimodal fusion block enhances interactions between textual and visual features, and the integrity of the model reflects the indispensable contribution of each component to the overall performance. Externally, robustness ensures the model operates stably under noisy conditions and incomplete inputs, and scalability enables it to efficiently adapt to varying architectural depths and widths. The above simulation results and performance analyses showcase the comprehensive strength of our proposed model.
Quantum computing has emerged as a breakthrough technology for sentiment and emotion analysis, providing computational efficiency and interpretability benefits. This paper compares three quantum-inspired methods: fully complex-valued neural networks, multimodal sentiment analysis models, and quantum affective computing. This paper compares three quantum-inspired methods: fully complex-valued neural networks, multimodal sentiment analysis models, and quantum affective computing models. The research uses six benchmark sentiment datasets (CR, MPQA, MR, SST-2, SUBJ, and SST-5) to compare the performance of the methods. The approach involves representing textual and multimodal data as complex quantum states, using quantum-inspired feature representations, and using quantum measurement methods for classification. The results show that the Complex Quantum Neural Network (QNN) performs better than baseline models such as BERT and RoBERTa, with the best F1-scores on all datasets. Specifically, it has an F1-score of 86 on CR, 86 on MPQA, 90 on MR, 88 on SST-2, 97 on SUBJ, and 53 on SST-5, outperforming baseline deep learning methods. These results show the potential of quantum computing to revolutionize sentiment analysis, providing more efficient and interpretable models with better contextual understanding and scalability.
This research introduces a new paradigm for sentiment analysis on patient medication reviews through the application of quantum computing to improve machine learning models. A pre-existing dataset of medication reviews from the UCI Machine Learning repository was utilized, involving Quantum Natural Language Processing (QNLP) techniques, namely the application of quantum embeddings produced by quantum circuits to improve the performance of sentiment classification. Experimental findings indicate that improved models through quantum techniques, like the Quantum Random Forest, outperform conventional models in terms of accuracy and precision. These improvements are ascribed to quantum embeddings’ capacity to efficiently represent complex linguistic patterns in high-dimensional data. Additionally, the study uses robust validation techniques including performance benchmarking and cross-validation to guarantee reliability. Through a scalable and context-aware framework for natural language comprehension, this study highlights the revolutionary potential of QNLP in improving sentiment analysis in the healthcare industry.