This research explores the use of quantum machine learning-more specifically, the Quantum Support Vector Machine (QSVM)-to predict emotional reactions triggered in virtual reality (VR) settings. The study's experimental setup involved provoking different emotional states via VR gameplay elements and capturing the physiological signals of participants. These signals were paired with the subjects' self-reported emotions, measured through the Self-Assessment Manikin. The study then employed both conventional Support Vector Machines (SVM) and QSVM to classify the data. Notably, QSVM surpassed the traditional SVM in accurately predicting the levels of arousal and valence, achieving higher precision with a reduced set of features. This enhanced efficiency is likely due to QSVM's superior handling of the intricate patterns in emotional data and the quantum models' more effective computational resource usage. These results hold considerable potential for the field of affective computing within VR environments, indicating the advantageous prospects of quantum machine learning for the domain.
This research explores the use of quantum machine learning-more specifically, the Quantum Support Vector Machine (QSVM)-to predict emotional reactions triggered in virtual reality (VR) settings. The study's experimental setup involved provoking different emotional states via VR gameplay elements and capturing the physiological signals of participants. These signals were paired with the subjects' self-reported emotions, measured through the Self-Assessment Manikin. The study then employed both conventional Support Vector Machines (SVM) and QSVM to classify the data. Notably, QSVM surpassed the traditional SVM in accurately predicting the levels of arousal and valence, achieving higher precision with a reduced set of features. This enhanced efficiency is likely due to QSVM's superior handling of the intricate patterns in emotional data and the quantum models' more effective computational resource usage. These results hold considerable potential for the field of affective computing within VR environments, indicating the advantageous prospects of quantum machine learning for the domain.
Emotion Recognition (ER) is an important capability that is increasingly being used to enhance the user experience in various fields such as, Human-Computer interaction, Cognitive Neuroscience, Neurofeedback, Robotics, Artificial Intelligence, Augmented Reality, Virtual Reality, Mental Health, etc. Emotions are generated in the limbic system, a set of brain structures that are involved in emotional processing, memory, and motivation. ER from electroencephalography (EEG) signals has gained attention from researchers in recent years as it provides a more accurate and non-intrusive way of understanding human emotions. This research paper presents a comprehensive study of the use of Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs) for ER using EEG signals. The aim of this study is to investigate the potential of quantum machine learning algorithms in improving the performance of ER tasks compared to classical Machine Learning (ML) algorithms. The model was simulated using IBM Qiskit due to the limited resources of quantum computers. Intriguingly, the experimental results contradict the speed-up claims of Quantum Computing (QC) by exhibiting longer computation times and lesser accuracy when compared to traditional methods. SVM and NN yield 68.5% and 69.2% accuracy while the quantum version gives 63% and 65.2% accuracy on the benchmark dataset SEED. This research provides evidence that Quantum Machine Learning (QML) algorithms need to be improved for ER from EEG signals and provide a reliable and non-intrusive way of understanding human emotions.
Understanding people's emotions is becoming increasingly important in diverse areas such as social media, e-commerce, and brand or trend analysis. While both deep learning networks and traditional machine learning techniques, such as Support Vector Machines (SVM) have their uses, they often fail to efficiently handle very big or complicated datasets. The study of Quantum Machine Learning (QML) methods could help solve these problems. Support Vector Machine (SVM) quantum algorithms are a scalable way to work with large amounts of data. They are more accurate at classifying datasets than traditional SVM while using less computing power. We suggest a mixed model that blends QSVM and CNN for deep learning and quantum processing that work together. The method includes extracting features, classifying mood, and preparing data from two standard sets: Amazon reviews of goods and IMDb reviews of movies. As shown in experiments on a large dataset, the suggested QMLbased hybrid design method is up to 12 % more accurate and nearly 20 % faster when working than standard machine learning methods. The new development makes quantum computing useful for faster and more flexible data processing to help with mood classification in areas like brand intelligence, watching social media, and reviewing customer reviews.
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.
This paper proposes hybrid quantum machine learning (QML) models for automatic emotion recognition from human speech signals. The proposed quantum support vector machine (QSVM) and hybrid quantum neural network (HQNN) models use Mel-Frequency Cepstral Coefficients (MFCC) as the features extracted from the speech data. We present a detailed analysis comparing the performance of QML models to conventional machine learning models for human speech-based emotion recognition, which is a multi-class classification problem. Our results illustrate that QSVM can achieve higher classification accuracy with a much reduced number of speech signal variables compared to traditional SVMs. Furthermore, the proposed HQNN classifier outperforms classical neural networks (NN) in terms of classification accuracy while utilizing a reduced number of training parameters. We have tested the performance of the HQNN model on a real IBM quantum computer. The results indicate that QML models provide a more effective and precise method for classification tasks involving the recognition of human emotions.
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.
In recent years, quantum technology has been developing in several fields, including quantum computing. Quantum Computing (QC) is a type of computing that uses quantum bits (qubits) instead of classical bits to perform calculations. Unlike classical computers, which use binary logic (0s and 1s), quantum computers exploit quantum phenomena (such as superposition and entanglement), which are expected to bring advantages over traditional Machine Learning (ML). However, QC is still an emerging field, and further research is needed to fully understand its potential in ML. In this regard, for example, it is still unclear how QC compares with traditional ML in terms of stability and generalization. Starting from this consideration, this work intends to experimentally compare quantum and traditional ML. In particular, the classical Support Vector Machine (SVM), which belongs to the traditional ML approach, and a Quantum Support Vector Machine (QSVM), as Quantum Machine Learning (QML) approach (which combines quantum computing and machine learning through quantum properties), are applied to a specific case study related to electroencephalographic (EEG) data. The comparison was conducted on similar development environments to ensure comparable results from a measurement standpoint. The results highlight that there are some critical points involving the generalization ability of QML algorithms, possibly related to an appropriate and specific hyperparameter search.