Sentiment analysis has been instrumental in developing artificial intelligence when applied to various domains. However, most sentiments and emotions are temporal and often exist in a complex manner. Several emotions can be experienced at the same time. Instead of recognizing only categorical information about emotions, there is a need to understand and quantify the intensity of emotions. The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime. The inspiration comes from manifesting human cognition and decision-making capabilities, which may adopt a brief explanation through quantum theory. Quantum state fidelity was used to characterize states and estimate emotion intensities rendered by subjects from the Amsterdam Dynamic Facial Expression Set (ADFES) dataset. The Quantum variational classifier technique was used to perform this experiment on the IBM Quantum Experience platform. The proposed method successfully quantifies the intensities of joy, sadness, contempt, anger, surprise, and fear emotions of labelled subjects from the ADFES dataset.
Sentiment analysis has been instrumental in developing artificial intelligence when applied to various domains. However, most sentiments and emotions are temporal and often exist in a complex manner. Several emotions can be experienced at the same time. Instead of recognizing only categorical information about emotions, there is a need to understand and quantify the intensity of emotions. The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime. The inspiration comes from manifesting human cognition and decision-making capabilities, which may adopt a brief explanation through quantum theory. Quantum state fidelity was used to characterize states and estimate emotion intensities rendered by subjects from the Amsterdam Dynamic Facial Expression Set (ADFES) dataset. The Quantum variational classifier technique was used to perform this experiment on the IBM Quantum Experience platform. The proposed method successfully quantifies the intensities of joy, sadness, contempt, anger, surprise, and fear emotions of labelled subjects from the ADFES dataset.
Quantum computations are extensively used to solve challenging problems and emotion detection is one such field that has lot of potential to be explored. Classical studies can identify emotions but fail to observe their intensities at such minute level. The proposed research primarily intends to quantify the intensities related with happy and sad emotions on a quantum computer examining subjects within Amsterdam Dynamic Facial Expression Set (ADFES) dataset. Leveraging quantum ability to compute quantum state fidelity established closeness of the encoded states. We used quantum variational classifier technique for this experiment performed on the quantum simulator available on the IBM Quantum Experience platform.
Quantum computing is typically associated with domains such as cybersecurity, drug discovery, finance, and large-scale algorithmic optimization. While these applications drive technological progress, they remain largely disconnected from the emotional and psychological realities that shape everyday human experience. To enhance personal relevance, quantum systems can instead be applied to support daily emotional well-being, guiding individuals through complex affective states and fostering more harmonious, enriched lives. This study proposes an AI framework that leverages quantum optimization to interpret and classify emotional expressions from text, expanding computational capacity to understand aspects of human consciousness and contextual affect in synchrony with lived experience. By embedding quantum-enhanced affective computation into daily interactions, the model moves beyond predictive analytics to form a dynamic interface with emotional cognition, with potential applications in counselling, self-awareness, and therapeutic support across clinical and organizational settings. Using the HappyDB dataset, containing over 100,000 self-reported happy moments annotated with emotional categories, we demonstrate that conventional AI approaches struggle to capture subtle context and interdependent emotional cues. Our method addresses this gap by integrating classical neural architectures with a quantum variational layer, enabling synchronous evaluation of complex semantic and affective relationships in text. The hybrid model transforms text into semantic embeddings, processes them through sequential LSTM layers, and optimizes latent emotional representations via quantum-inspired techniques. This allows the system to emulate human-like context awareness and achieve enhanced emotion classification beyond the capabilities of classical methods. By applying quantum computing to daily-life emotional cognition at scale, this work reframes quantum algorithms as tools for enriching human experience. Beyond mental health assessment and digital therapeutics, it establishes a new frontier for human-centered quantum intelligence, demonstrating that complex psychological and semantic interactions can be optimized to advance AI-assisted well-being.
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 .
Emotional cognition is nonlinear, probabilistic, and interdependent, making it hard to recreate human emotional intelligence in AI systems. The study offers the Quantum-Emotion Neural Network (QENN). This unique Quantum Physics-Inspired AI approach uses aspects of quantum mechanics such as superposition, entanglement, and quantum probability amplitudes in neural network design. Where regular models handle emotions as non-interacting or unchanging parts, QENN represents them using amplitude encoding and quantum-inspired neurons to allow all emotional states to be managed together. Training and evaluation of the model took place on the IndiComics Emotion (ICE) dataset, which has 17,340 multimodal instances over eight emotion classes-happy, Sad, Angry, Fearful, Disgust, Surprised, Neutral, and Mixed emotions. The tests showed QENN achieved better results than CNN and LSTM, with an accuracy of 93.8%, 93.5% precision, 92.9% recall, an F1-score of 93.2%, and an ROC-AUC of 95.8% overall. In contrast, traditional CNN and LSTM models had 84.7% and 85.3% accuracies, and their ROC-AUC was 87.2% and 88.5%, respectively. Evaluation with the confusion matrix showed that precision remained above 90% in all emotion types, even for complex ones like ‘Mixed.’ Also, the training-validation accuracy curves revealed excellent generalisation and limited overfitting, with a learning rate of 0.001 giving the most minor validation loss, equal to 0.21. These outcomes show that quantum-inspired solutions are robust and easy to understand for emotion modelling. This work proves that QENN is a critical step forward in making machines that understand emotions as people do.
Abstract Facial expressions bring human interactions to life with nonverbal cues that convey hues of emotions, feelings, and cultural intent. Analyzing these expressions is essential in the age of digital transformation. Traditional approaches to real-time facial expression analysis have limitations in capturing the complexity of these expressions and consume enormous computational power. The promising computing capacity of quantum computers is poised to solve current problems and set new standards by meeting growing demands. We have created a hybrid model that leverages the capabilities of the classical and quantum domains. The proposed model uses a quantum distance-based classifier along with classical artificial neural networks to perform real-time facial expression analysis and classification. Based on a thorough review of the literature, this novel and comprehensive work is, to the best of our knowledge, one of the few that addresses eight different emotions in the quantum domain. The implementation of a novel quantum error correction method has improved the accuracy of this hybrid model. Our model was trained on the CK+ facial expression database. To ensure fairness of the study, we tested our model on the largest facial expression database, AffectNet-8, and compared the performance with state-of-the-art models. Our model accuracy is 10.83% higher than that of the state-of-the-art models. From a holistic perspective, our proposed novel hybrid model appears to have universal value for all kinds of real-time image analysis and classification problems. As we move forward, we planned to focus on quantum neural networks for image processing and facial expression analysis.
This Facial emotion recognition plays a pivotal role in human-computer interaction, with far-reaching implications spanning psychology, healthcare, and human computer interaction. Conventional approaches, primarily utilizing classical machine learning methods, frequently face challenges in effectively interpreting intricate facial expressions under diverse environmental circumstances. However, the burgeoning paradigm of quantum machine learning (QML) presents a transformative approach, leveraging the principles of quantum computing to potentially revolutionize the landscape of emotion detection from facial expressions. This abstract explores the convergence of quantum computing and facial emotion recognition, proposing a novel framework wherein facial features undergo encoding into quantum states, undergo processing through quantum circuits, and ultimately translate into predictions of emotional states. Within this context, quantum algorithms like variational algorithms and quantum neural networks emerge as promising avenues for both training and inference tasks, presenting unparalleled opportunities to improve the accuracy and efficiency of emotion recognition systems. Despite the immense potential offered by QML, practical implementation encounters formidable challenges, including noise, decoherence, and scalability constraints inherent in existing quantum devices. However, ongoing research endeavors persist in addressing these obstacles, driven by the promise of transforming emotion recognition systems into more nuanced and robust entities. As the integration of quantum computing principles advances, it holds the promise of facilitating deeper insights into human emotions, thereby fostering more seamless and intuitive human-computer interaction experiences. By bridging the gap between quantum computing and facial emotion recognition, this abstract underscores the transformative potential of QML in reshaping the future of emotion recognition systems, paving the way for more empathetic and responsive technological interfaces.