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.
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.
The convergence of quantum computing and artificial intelligence represents a paradigm shift in computational capability, enabling solutions to previously intractable optimization problems, accelerated machine learning training, and enhanced natural language understanding through quantum state exploitation. This paper presents a comprehensive framework encompassing five distinct quantum artificial intelligence architectures: Quantum Machine Learning utilizing quantum circuit-based training and inference, Quantum-Inspired AI implementing quantum algorithmic principles on classical hardware, Hybrid Quantum-Classical AI leveraging collaborative processing between central processing units and quantum processing units, Quantum Optimization AI for solving complex combinatorial problems, and Quantum Natural Language Processing exploiting quantum superposition for semantic reasoning. The quantum machine learning architecture implements variational quantum eigensolvers and quantum kernel methods for feature space transformation, achieving exponential speedup in specific classification tasks. The quantum-inspired approach applies tensor network decomposition and quantum annealing simulation on classical systems, demonstrating polynomial speedup for optimization problems. The hybrid quantum-classical framework orchestrates workload partitioning between conventional processors and quantum accelerators through dynamic task allocation algorithms, optimizing for quantum circuit depth and classical preprocessing overhead. Quantum optimization leverages quantum annealing and quantum approximate optimization algorithms for solving large-scale combinatorial problems in logistics, finance, and molecular simulation. Quantum natural language processing implements quantum word embeddings in Hilbert space, enabling superposition-based semantic analysis with logarithmic dimensional complexity compared to classical word vector representations. This research establishes theoretical foundations and architectural blueprints for quantum-enhanced artificial intelligence systems, positioning quantum computing as a transformative technology for next-generation machine learning, optimization, and cognitive computing applications.
Social media has become a significant resource for evaluating public attitudes regarding mental health, as individuals may express their thoughts through postings, comments, and conversations. Sentiment analysis is a promising method for studying emotional patterns expressed in mental-health-related text. Traditional machine learning for sentiment analysis faces challenges like transparency, scarce labeled data, and language limitations. This paper presents a sentiment classification (positive, negative, neutral) framework applied to mental-health-related Reddit posts. Repeated emotional patterns can offer helpful insights into sentiment trends in mental health discussions, but they shouldn’t be seen as proof of clinical disorders. In this paper, we propose a hybrid framework for sentiment classification from text, termed QBERT-LSTM, which integrates quantum-enhanced bidirectional encoder representations from transformers (QBERT) with long short-term memory (LSTM) networks. In the proposed approach, transformer models excel at capturing global context, while the LSTM enhances sequential patterns and temporal features. The quantum component uses a parameterized circuit with angle encoding and variational gates to perform nonlinear transformations on a quantum simulator (PennyLane). Subsequently, the model is tested on more intricate circuits comprising 2, 4, 6, 8, 10, and 12 qubits to assess its consistency. A novel Reddit dataset of mental health-related subreddits (anxiety, panic attacks, and depression) is constructed, with sentiment labels derived from the VADER sentiment analyzer. We thoroughly assessed the performance of our proposed model against deep learning models and cutting-edge transformer architectures, achieving up to 98% accuracy in the best-performing configuration on the generated dataset (mean accuracy 0.97±0.0059 across five random seeds).
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.
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.
This investigation presents a novel methodological framework for affective computing. Emotional processes exert substantial influence on cognitive decision-making mechanisms, brain-computer interface efficacy, and social intelligence paradigms, thereby establishing emotion recognition as a critical domain of scientific inquiry. Electroencephalographic (EEG) signals have garnered considerable scholarly attention due to their capacity to capture neurological activity with exceptional temporal resolution. However, the inherent complexity of these signals and significant inter-individual variability present substantial challenges in the development of accurate and generalizable computational models. To address these methodological limitations, this study proposes an innovative hybrid quantum-classical framework-the Quantum Deep Adaptation Network (QDAN)-for emotion recognition utilizing EEG signal analysis. This architectural approach strategically leverages the computational advantages inherent in quantum computing, particularly the fundamental quantum mechanical principles of superposition and entanglement, to enhance pattern recognition capabilities in high-dimensional EEG data representations. We employ sophisticated domain adaptation techniques to effectively mitigate variability across experimental sessions. The proposed methodological framework undergoes rigorous empirical validation utilizing the standardized SEED dataset. Experimental findings reveal significant performance heterogeneity across subjects and sessions, with the model achieving a maximum classification accuracy of 73.91 %. These results demonstrate the potential efficacy of quantum-enhanced computational approaches in addressing the complex challenges associated with EEG-based emotion recognition systems.
Understanding human decision-making in uncertain and dynamic scenarios is essential for advancing technologies in AI, predictive analytics, and behavioral simulations. Fuzzy logic provides an approach for representing gradual transitions and capturing the nuances of human behaviors, making it a foundation for tackling such challenges. This study explores a hybrid approach integrating fuzzy logic, quantum computing, and machine learning to model complex decision-making scenarios. Using a case study of the Prisoner’s Dilemma, the methodology captures nuanced human behaviors and uncertainties through fuzzy membership degrees while leveraging quantum properties like superposition and entanglement to simulate dynamic agent interactions. The resulting simulations generated a comprehensive dataset, which was subsequently applied in a quantum-based artificial intelligence model, demonstrating the capability of this integrated approach to uncover complex patterns in uncertain systems. This work offers a foundation for advancing computational models in decision support systems and predictive analytics, highlighting the promise of emerging technologies in addressing challenges in multidimensional and interdependent environments.