This paper presents a quantum convolutional neural network (QCNN) that classifies the happy and sad emotions from the fabled facial emotion recognition dataset (FER), FER-2013. The model uses the data re-uploading concept on the qubit(s) of the parameterized quantum circuits (PQCs) in the quantum convolutional layer of the hybrid QCNN model, which achieved a 70.25 % validation accuracy on the test set. Later, a model averaging ensemble approach was implemented which yielded results motivating further investigation.
This paper presents a quantum convolutional neural network (QCNN) that classifies the happy and sad emotions from the fabled facial emotion recognition dataset (FER), FER-2013. The model uses the data re-uploading concept on the qubit(s) of the parameterized quantum circuits (PQCs) in the quantum convolutional layer of the hybrid QCNN model, which achieved a 70.25 % validation accuracy on the test set. Later, a model averaging ensemble approach was implemented which yielded results motivating further investigation.
Facial expression recognition (FER) underpins applications in affective computing but remains challenged by computational cost and the ambiguity of compound emotions. We introduce a Hybrid-Quantum Convolutional Neural Network (HQ-CNN) that integrates quantum principles (superposition, entanglement) into a classical CNN pipeline to enhance representational power and efficiency. Evaluated on the Real-World Affective Faces Database (RAF-DB), the HQ-CNN improves accuracy by 4.60% on basic emotions and 4.47% on compound emotions, while reducing computation time by up to 22.11% and 6.20%, respectively, relative to a VGG16 baseline. Confusion-matrix analysis shows fewer misclassifications on challenging compound categories, indicating better separation of overlapping affective cues. These results support the use of quantum-enhanced architectures as a viable path toward robust, real-time FER systems.
Emotion recognition in speech is essential for human-computer interaction. This study introduces a refined hybrid Quantum Convolutional Neural Network (QCNN) framework designed to substantially enhance Speech Emotion Recognition (SER). It addresses major challenges such as speaker variability, noise robustness, and cross-domain applicability that often limit classical deep learning models. By Utilizing quantum principles like superposition and entanglement within a 6-qubit, 8-layer parameterized circuit, the model derives highdimensional, noise-resistant features from speech signals. The method includes sophisticated preprocessing steps, such as bandpass filtering and energy-based Voice Activity Detection (VAD), followed by Mel-Frequency Cepstral Coefficients (MFCC) extraction. Evaluated on a diverse dataset of 4,515 samples across eight emotions (Angry, Calm, Disgust, Fear, Happy, Neutral, Sad, Surprised), the QCNN achieved an accuracy of 86 %, setting a new benchmark. It surpasses classical approaches such as a CNN with attention(77%), a traditional QCNN(77.87%), and a hybrid quantum-classical network(76%). The model demonstrated balanced performance, with precision scores ranging from 0.79 to 1.0 (including a perfect 1.0 for Fear) and recall scores from 0.82 to 0.94. These results underscore the promise of quantum-enhanced neural networks in capturing complex, nonlinear speech emotion patterns and establishing a new standard for robust, generalizable SER systems.
The incorporation of modern technologies into education has transformed how learning is seen and assessed, with a rising emphasis on understanding student behavior using facial expression recognition (FER). Recognizing students' emotional and cognitive states can provide vital insights into their engagement and learning styles, allowing teachers to adjust their teaching tactics in real time. Traditional machine learning algorithms have been used to solve this problem, but they frequently struggle with the complexity of facial expressions and big datasets, resulting in limitations in accuracy and processing efficiency. This study explores the potential of quantum computing in addressing these challenges through the development of a Deep Quantum Convolutional Neural Network (QCNN) architecture. By combining the advanced image processing capabilities of convolutional neural networks (CNNs) with the computational power of quantum features such as superposition and entanglement, we aim to enhance the detection of facial expressions that reflect students' engagement, concentration, frustration, and satisfaction. Finally, the result demonstrates a classification accuracy of 91.58
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 re-uploading 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.
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.
Quantum machine learning is emerging as a promising frontier at the intersection of quantum computing and Artificial Intelligence, offering potential gains in speed, efficiency, and robustness. In this paper, a hybrid Quantum Convolutional Neural Network (QCNN) architecture is presented for visual pattern recognition tasks, leveraging quantum superposition and entanglement for enhanced feature extraction. Unlike previous studies that focus on small-scale or simplified datasets, this study systematically evaluates QCNNs across multiple benchmark datasets and provides a direct comparison with classical CNNs, highlighting practical quantum advantages such as parameter efficiency and noise-resilience. The proposed approach encodes classical image data from benchmark datasets, including70K28×28grayscale images from MNIST and FashionMNIST, and60Kdownsampled grayscale CIFAR-10 images, into quantum states using amplitude encoding, followed by hierarchical quantum convolution and pooling layers implemented via parametrised quantum circuits (PQCs). The model culminates in a classical dense layer for final classification. Experimental results demonstrate that the QCNN achieves competitive performance on low-dimensional grayscale datasets with 93.2 % accuracy on MNIST and 88.5% on Fashion MNIST, though it underperforms on more complex datasets like CIFAR-10 with 67.8 % accuracy due to current quantum hardware limitations. While not yet surpassing classical CNNs in raw accuracy, the proposed QCNN exhibits notable parameter efficiency, noise resilience, and theoretical potential for energy-efficient computation, highlighting its promise for future applications as quantum technologies mature.