The growing use of Internet of Medical Things (IoMT) systems demands accurate sentiment analysis, nuanced emotion tracking, and seamless integration of multimodal data. Current models often struggle when handling heterogeneous sources and evolving emotional patterns. To address these issues, this study proposes QED-Net—a quantum-inspired deep learning architecture designed specifically for IoMT environments. It introduces four modular components. The quantum-driven sentiment amplification (QSA) enhances contextual sentiment interpretation. The temporal emotion evolution graph (TEEG) captures the shifting nature of emotional states over time. The hyperdimensional quantum tensor fusion (HD-QTF) supports synchronized integration of diverse modalities. Finally, the emotion-to-medical ontology encoder (EMOE) translates emotional cues into actionable clinical signals. These components operate both independently and in synergy, allowing for flexible deployment in real-world scenarios. Simulations conducted on benchmark datasets confirm the model’s effectiveness, with QED-Net achieving 93.2% precision in sentiment detection, 92.3% in emotion tracking, and 91.6% robustness in multimodal fusion.
The growing use of Internet of Medical Things (IoMT) systems demands accurate sentiment analysis, nuanced emotion tracking, and seamless integration of multimodal data. Current models often struggle when handling heterogeneous sources and evolving emotional patterns. To address these issues, this study proposes QED-Net—a quantum-inspired deep learning architecture designed specifically for IoMT environments. It introduces four modular components. The quantum-driven sentiment amplification (QSA) enhances contextual sentiment interpretation. The temporal emotion evolution graph (TEEG) captures the shifting nature of emotional states over time. The hyperdimensional quantum tensor fusion (HD-QTF) supports synchronized integration of diverse modalities. Finally, the emotion-to-medical ontology encoder (EMOE) translates emotional cues into actionable clinical signals. These components operate both independently and in synergy, allowing for flexible deployment in real-world scenarios. Simulations conducted on benchmark datasets confirm the model’s effectiveness, with QED-Net achieving 93.2% precision in sentiment detection, 92.3% in emotion tracking, and 91.6% robustness in multimodal fusion.
The Internet of Medical Things (IoMT) provides the capabilities of continuous real-time monitoring of patients using connected devices as well as generating a great deal of healthcare data; these are the benefits of IoMT. According to multiple sources, machine learning (ML) and deep learning (DL) have been used in the medical field for many years, and there are also issues surrounding these technologies that impact their ability to support clinical decision-making. These include issues related to scalability, interoperability, latency, explainability of the model output, and cybersecurity (WHO, 2021; Esteva et al., 2019; Topol et al., 2019). In this chapter, a hybrid classical-quantum framework is proposed for use in real-time patient monitoring, which integrates Quantum Intelligence (QI), Quantum ML (QML), Edge Computing (EC), and the IoMT. In addition, the authors provide examples of current use cases as well as practical recommendations to support the adoption of secure, scalable, and intelligent healthcare monitoring systems.
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.
Sentiment, emotion, and sarcasm analysis in multimodal dialogues is crucial for understanding the underlying intentions and attitudes expressed by individuals. Traditional methods often struggle to capture the full intensity of these polarities, leading to less accurate results. To address this limitation, we propose a quantum-inspired approach leveraging Quantum Neural Networks (QNNs) for enhanced classification and intensity analysis. A key component of our method is the Variational Quantum Eigensolver (VQE), a hybrid quantum-classical algorithm that optimizes the parameters of the QNN by minimizing the eigenvalues of a Hamiltonian system. This optimization enables the network to learn complex relationships in multimodal data more effectively. Our approach surpasses state-of-the-art methods, achieving up to 7.5 % higher accuracy and 6.8 % greater precision. Experiments on benchmark datasets such as MUStARD, Memotion, CMU-MOSEI, and MELD demonstrate its effectiveness, with an F1-score of 87.3 % on CMU-MOSEI. This method is particularly beneficial in domains like social media, customer support, and entertainment, where both verbal and non-verbal cues play a critical role in accurate sentiment analysis .
Abstract The rapid growth of Hinglish communication onsocial media has introduced complex linguistic, cultural, andmultimodal patterns that conventional affective computing sys-tems struggle to handle. While deep learning and transformer-based models perform well on monolingual and grammaticallyconsistent text, they are limited in code-mixed Hinglish settings.These models rely on deterministic, single-meaning embeddingsthat inadequately represent polysemy, fail to capture context-dependent polarity shifts across Hindi–English transitions, andtreat multimodal signals—such as text, emojis, and visuals—asweakly coupled rather than jointly dependent. Consequently,emotional nuance, irony, and ambiguity common in real-worldHinglish interactions are often misinterpreted.To overcome these limitations, this work proposes Q-OmniNet,a quantum-inspired hybrid tensor network that models affectivemeaning as contextual superposition and captures non-separable(entangled) relationships across modalities. The framework inte-grates (i) a tensor-network-based contextual encoder to simulatemultimodal entanglement, (ii) complex-valued embeddings to en-code semantic magnitude and contextual phase, and (iii) quantumkernel estimation to model higher-order affective similarity viaquantum-state overlap. Together, these components provide aricher representation of Hinglish emotion dynamics than classicalor transformer-based approaches.Experimental results on real-world multimodal Hinglishdatasets show substantial improvements. Q-OmniNet achievesaccuracies of 0.89 for emotion classification, 0.93 for sentimentanalysis, and 0.97 for sarcasm detection, outperforming tradi-tional baselines by 15–25% and transformer models by 8–14%.The quantum-hybrid variant demonstrates superior robustnessunder noisy and ambiguous conditions, achieving an averageaccuracy of 0.9275 compared to 0.7617 for conventional models.These findings highlight the effectiveness of quantum-inspiredarchitectures for context-sensitive, multilingual, and multimodalaffective understanding.
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.
As digital communication ecosystems continue to expand, brand management increasingly relies on the ability to extract, interpret, and predict consumer sentiment from large and dynamic data sources. Conventional sentiment analysis models—based on classical machine learning and deep neural architectures—often face limitations in handling ambiguity, contextual entanglement, and non-linear emotional expressions embedded in natural language. This study introduces a quantum-inspired sentiment analysis framework designed to enhance predictive brand management through improved semantic representation and probabilistic reasoning. Drawing inspiration from quantum mechanics, the proposed model employs principles such as superposition, entanglement, and Hilbert space embeddings to represent sentiment as a probabilistic distribution of cognitive states rather than as discrete classifications. By integrating these quantum-inspired techniques with classical natural language processing (NLP) and machine learning models, the system captures contextual subtleties and sentiment overlaps that traditional models often overlook. Experimental evaluation on social media and brand communication datasets demonstrates that quantum-inspired embeddings outperform conventional word vectorization techniques in recognizing sentiment polarity shifts and predicting brand reputation trends. The study highlights how quantum probabilistic reasoning can improve the interpretability and foresight of brand perception models, allowing organizations to proactively manage reputation, customer engagement, and market positioning.