Emotion recognition seeks to identify a person’s state by examining signals such as facial expressions, speech, physiological signals and conversational content. Yet accurate emotion recognition presents obstacles because these signals can be complex, noisy, nonlinear and heavily affected by surrounding context. This paper investigates quantum-based representations combined with machine learning techniques to enhance emotion classification. The research examines emotion sources, including facial expressions, speech, EEG signals and conversational data. It explores how quantum-inspired techniques can merge with established deep learning methods to produce representations. The design presented fuses a neural network with a four qubit ZZFeatureMap component enabling the system to learn data from both conventional and quantum derived feature spaces. Experiments examined EEG segments for each participant and the system achieved average recognition rates of seventy nine point six percent for valence and eighty one point five percent for arousal across ten repeated trials. These findings show that quantum derived features can add information when combined with conventional deep learning methods. Nevertheless because experiments used simulated circuits the results do not prove a performance advantage over conventional techniques on actual quantum computers. The study also highlights difficulties such as quantum noise, computational scalability, experimental assessment, data privacy and real world deployment.
Emotion recognition seeks to identify a person’s state by examining signals such as facial expressions, speech, physiological signals and conversational content. Yet accurate emotion recognition presents obstacles because these signals can be complex, noisy, nonlinear and heavily affected by surrounding context. This paper investigates quantum-based representations combined with machine learning techniques to enhance emotion classification. The research examines emotion sources, including facial expressions, speech, EEG signals and conversational data. It explores how quantum-inspired techniques can merge with established deep learning methods to produce representations. The design presented fuses a neural network with a four qubit ZZFeatureMap component enabling the system to learn data from both conventional and quantum derived feature spaces. Experiments examined EEG segments for each participant and the system achieved average recognition rates of seventy nine point six percent for valence and eighty one point five percent for arousal across ten repeated trials. These findings show that quantum derived features can add information when combined with conventional deep learning methods. Nevertheless because experiments used simulated circuits the results do not prove a performance advantage over conventional techniques on actual quantum computers. The study also highlights difficulties such as quantum noise, computational scalability, experimental assessment, data privacy and real world deployment.
Applications in behavioural research, human-computer interaction, and mental health depend on the ability to recognize emotions. In order to improve the accuracy of emotion recognition using electroencephalography (EEG) data, this work presents a hybrid quantum deep learning technique. Conventional EEG-based emotion recognition techniques are limited by noise and high-dimensional data complexity, which make feature extraction difficult. To tackle these issues, our method combines traditional deep learning classification with quantum-enhanced feature extraction. To identify important brain wave patterns, Bandpass filtering and Welch method are used as preprocessing techniques on EEG data. Intricate inter-band interactions that are essential for determining emotional states are captured by mapping frequency band power attributes (delta, theta, alpha, and beta) to quantum representations. Entanglement and rotation gates are used in a hybrid quantum circuit to maximize the model's sensitivity to EEG patterns associated with different emotions. Promising results from evaluation on a test dataset indicate the model's potential for accurate emotion recognition. The model will be extended for real-time applications and multi-class categorization in future study, which could improve EEG-based mental health screening instruments. This method offers a promising tool for applications in adaptive human-computer systems and mental health monitoring by showcasing the possibilities of fusing traditional deep learning with quantum processing for reliable, scalable emotion recognition.
Abstract This study explores Quantum AI’s potential in enhancing Speech Emotion Recognition (SER) systems. Our primary objective is to evaluate the performance of quantum-inspired algorithms compared to classical machine-learning approaches in accurately identifying and classifying emotions from speech signals. We hypothesise that quantum computing techniques can improve the efficiency and accuracy of emotion recognition, particularly in handling complex, highdimensional acoustic data. We developed a hybrid quantum-classical model that combines quantum-inspired neural networks with traditional feature extraction techniques. Our approach utilises quantum circuits for data encoding and quantum variational algorithms for classification. We implemented quantum versions of support vector machines (QSVM) and quantum approximate optimisation algorithms (QAOA) for emotion classification tasks. These quantum methods were compared against a hybrid Convolutional Neural Network and Long Short Term (LSTM). The hybrid network is called a Convolutional Long Short Term network(CLSTM). The study employed a custom dataset for speech emotion recognition. We prepossessed the audio data to extract relevant acoustic features, including mel-frequency cepstral coefficients (MFCCs), pitch, and energy. In contrast, our implemented quantum-inspired model for this corpus showed lower performance, with the highest training accuracy of 30%, struggling with most emotions but performing best with ’Trust’. These varied results across different implementations suggest that while Quantum AI techniques show promise in advancing speech emotion recognition, their effectiveness may depend on the specific dataset, language, and model architecture used. Finally, the speed of a simulated quantum network is shown to outperform current methods in terms of matrix multiplication by leaps and bounds.
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.
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.
Multimodal emotion recognition can benefit from combining complementary information from facial images, speech, and text.However, integrating heterogeneous modalities obtained from independently collected datasets introduces challenges in data alignment and label consistency.This study presents a hybrid quantum-classical framework that combines modality-specific classical encoders with a quantum re-uploading variational classifier for six-class emotion recognition.Facial images, speech samples, and textual samples were obtained from separate publicly available datasets and aligned at the emotion-label level to construct a unified multimodal experimental setting.Image, speech, and text encoders generated modality-specific representations that were fused through a classical neural network and projected into a six-dimensional quantum input space.A six-qubit quantum circuit with three re-uploading layers repeatedly encoded the fused features before six-class classification.On a test set of 6,347 samples, the classical Image-Speech-Text fusion model achieved 73.94% accuracy and a 70.64% macro F1-score, whereas the proposed quantum reuploading model achieved 72.57% accuracy and a 68.76% macro F1-score.The quantum model used 8,706 trainable parameters compared with 15,440,006 for the classical multimodal model, representing a 99.94% reduction in trainable parameters.The results indicate that quantum re-uploading can provide competitive performance for multimodal emotion recognition while highlighting the limitations of label-level alignment across independently collected datasets.
Speech emotion recognition (SER) remains fragile in real-world conditions because emotional cues are subtle, speaker-dependent, and easily confounded by recording variability, while high-performing deep models typically rely on large and carefully curated training sets. Quantum machine learning offers an alternative way to introduce nonlinear correlation modeling with compact modules, yet existing quantum SER studies remain limited, and the impact of circuit structure is not well understood. This paper presents HQTN-SER, a hybrid quantum-classical framework that investigates how quantum tensor network connectivity can support SER under small-qubit settings. HQTN-SER introduces (i) an MPS-inspired quantum tensor network module that enforces structured interactions to model correlations in speech representations with a small number of trainable parameters, and (ii) a fusion strategy that combines quantum measurement features with a learned classical latent embedding for end-to-end emotion classification. We evaluate HQTN-SER on three public benchmarks (RAVDESS, SAVEE, and MDER) under a unified preprocessing and training protocol. The proposed model achieves test accuracies of 80.09\% on RAVDESS, 78.12\% on SAVEE, and 73.51\% on MDER, with corresponding macro-F1 scores of 0.8012, 0.7826, and 0.7351, respectively. The model also exhibits stable convergence while using only three or four qubits, suggesting that MPS-inspired circuit structure can be an effective and hardware-aware design choice for quantum-assisted SER. These results provide a compact experimental baseline for studying structured quantum modules in affective computing.