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.
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.
In this work, we studied the use of Quantum Machine Learning (QML) algorithms for binary classification and compared their performance with classical Machine Learning (ML) methods. QML merges principles of Quantum Computing (QC) and ML, offering improved efficiency and potential quantum advantage in data-driven tasks and when solving complex problems. In binary classification, where the goal is to assign data to one of two categories, QML uses quantum algorithms to process large datasets efficiently. Quantum algorithms like Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) exploit quantum parallelism and entanglement to enhance performance over classical methods. This study focuses on two common QML algorithms, Quantum Support Vector Classifier (QSVC) and QNN. We used the Qiskit software and conducted the experiments with three different datasets. Data preprocessing included dimensionality reduction using Principal Component Analysis (PCA) and standardization using scalers. The results showed that quantum algorithms demonstrated competitive performance against their classical counterparts in terms of accuracy, while QSVC performed better than QNN. These findings suggest that QML holds potential for improving computational efficiency in binary classification tasks. This opens the way for more efficient and scalable solutions in complex classification challenges and shows the complementary role of quantum computing.
Quantum Machine Learning (QML) has emerged as a paradigm that combines the computational advantages of quantum computing with the predictive capabilities of machine learning to address complex data classification problems. As data dimensionality increases rapidly and classical learning algorithms face scalability constraints, QML leverages quantum parallelism, entanglement, and high-dimensional Hilbert space representations to enhance learning performance. This paper reviews and analyses advanced QML algorithms, including Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and Quantum Kernel Estimation, for optimizing both binary and multi-class classification tasks under high-complexity conditions. The proposed QML framework is evaluated on benchmark datasets like MNIST, the Breast Cancer Wisconsin (BCW) dataset, and synthetic nonlinear datasets, and is compared against classical machine learning baselines, including Support Vector Machines (SVM), Random Forests (RF), and Deep Neural Networks (DNN). The results demonstrate notable improvements in classification accuracy (up to 96.8%), decision margins, and computational efficiency in quantum-suitable data regimes, while also highlighting current limitations related to noise, circuit depth, and hardware constraints. Overall, the study presents a unified QML framework, theoretical formulations, and experimental evaluations that illustrate the potential of quantum algorithms for next-generation classification tasks.
Machine Learning (ML) has changed the way computers work by letting them learn from data and make smart choices. However, traditional ML techniques encounter scalability and efficiency constraints as datasets get larger and computational problems become more complicated[1]. These restrictions could be circumvented by the new paradigms that quantum computing offers by using entanglement and superposition [2], [3]. The combination of the Quantum computing with the Machine Learning is called the Quantum Machine Learning(QML), offers efficiency improvements, new algorithmic opportunities, and speedups in certain linear algebraic operations. So, this review dives deeply into quantum-classical algorithms, with a particular emphasis on the approaches like quantum neural network and quantum support vector machines. We examine computational complexity, architectural design, benchmarking findings, and real-world applications (such as optimization, cryptography, and classification). We also discuss important issues including scalability, data encoding, and noise, and we suggest future paths for error prevention and scalable structures.
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.
Quantum Machine Learning is an active research field where the power of quantum mechanics is integrated into machine learning algorithms to solve problems that are intractable for classical computers. Quantum support vector machine (QSVM) was one of the first quantum algorithms that got implemented on real quantum hardware. It demonstrated promising outcomes as compared to classical Support vector machine (SVM) through many implementations. However, it encounters challenges in scaling with large datasets and in resource effectiveness when compare to the usage of innovative classical algorithms in the Noisy Intermediate Scale Quantum era. Here is a review of some latest developments of QSVM and discussion on prospects of QSVM application in the future.