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International Conference on Computing, Communication and Networking Technologies (ICCCNT)

Quantum Kernel-aided Support Vector Machine Classifier for Improved Speech Classification

Hasmitha Krishna N, S. Sridevi, R Prasanna Kumar

Published Jul 6, 2023• 12 citations
Publisher

Abstract

The field of machine learning has been revolutionized by quantum computing, as it has the capacity to tackle complex problems that are beyond the capabilities of classical computers. One such problem is the efficient classification of speech signals with limited data, employing extracted feature attributes like Mel-scale frequency cepstral coefficients (MFCCs). This study compares the performance of classical machine learning models with a Quantum Support Vector Machine (QKSVM) classifier on a data set with a limited number of audio samples. Interestingly, the quantum kernel method outperformed classical models despite the small sample size. This result can be attributed to the quantum kernel method’s ability to extract high-dimensional features efficiently, allowing it to learn more from fewer samples. The Quantum Kernel Circuit (QKC) is contrived in different variants comprising 13, 8, 7, and 4 qubits. This QKC is built with an Angle Embedding layer, an adjoint version of the Angle Embedding layer, and a Hermitian space observable layer. Our findings demonstrate that the hybrid quantum kernel-based technique outperforms existing classical solutions. Amongst variants, the Quantum support vector machine model with 13 optimal numbers of coefficients is efficient in speech classification with the highest accuracy of 87.32%.

DOI
10.1109/icccnt56998.2023.10307618

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The field of machine learning has been revolutionized by quantum computing, as it has the capacity to tackle complex problems that are beyond the capabilities of classical computers. One such problem is the efficient classification of speech signals with limited data, employing extracted feature attributes like Mel-scale frequency cepstral coefficients (MFCCs). This study compares the performance of classical machine learning models with a Quantum Support Vector Machine (QKSVM) classifier on a data set with a limited number of audio samples. Interestingly, the quantum kernel method outperformed classical models despite the small sample size. This result can be attributed to the quantum kernel method’s ability to extract high-dimensional features efficiently, allowing it to learn more from fewer samples. The Quantum Kernel Circuit (QKC) is contrived in different variants comprising 13, 8, 7, and 4 qubits. This QKC is built with an Angle Embedding layer, an adjoint version of the Angle Embedding layer, and a Hermitian space observable layer. Our findings demonstrate that the hybrid quantum kernel-based technique outperforms existing classical solutions. Amongst variants, the Quantum support vector machine model with 13 optimal numbers of coefficients is efficient in speech classification with the highest accuracy of 87.32%.
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Abstract Support vector machines (SVMs) are widely used for classification, but become computationally expensive as datasets grow and data become high-dimensional or nonlinearly separable. Quantum support vector machines (QSVMs) enhance the traditional SVMs by replacing conventional feature mappings with quantum-implemented embeddings that project data into high-dimensional Hilbert spaces, potentially improving expressive power and efficiency. However, training QSVMs on current noisy intermediate-scale quantum (NISQ) devices remains challenging, as constructing the quantum kernel matrix requires repeated circuit executions and scales quadratically with dataset size. We propose NQ-SVM , a hybrid quantum-classical method that incorporates the Nystr\"om approximation to enhance the scalability of QSVMs. By selecting a small set of landmark points, the Nystr\"om method yields a low-rank kernel approximation, reducing complexity from (O(n2))(O(n^2))(O(n2)) to (O(nm))(O(nm))(O(nm)), while preserving the quantum advantages in handling high-dimensional, non-linear data. Extensive simulations demonstrate that NQ-SVM achieves comparable classification accuracy to classical SVMs and full QSVMs, while substantially reducing training time and kernel evaluation costs. These results underscore the potential of NQ-SVM as a scalable and practical hybrid framework for near-term quantum machine learning (QML).
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Quantum computing offers a powerful new paradigm for enhancing classical machine learning, particularly in classification tasks. In this paper, we examine the use of quantum kernels within Support Vector Classification (SVC) and compare their performance to classical SVC. Our experiments focus on the Iris and Wine datasets. For the Wine dataset, we applied Principal Component Analysis (PCA), reducing the feature space from 13 to 4 dimensions to improve computational efficiency. The results demonstrate that Quantum-enhanced SVC (QSVC) outperforms classical SVC, achieving an accuracy improvement from 96.15% with classical SVM to 98.05% with QSVC for the Wine dataset. Similarly, for the Iris dataset, classical SVC achieves near-perfect accuracy at 100%, while QSVC closely follows with 97.25% accuracy. These results highlight the capability of quantum kernels to improve both accuracy and efficiency in classification tasks, providing a promising direction for scalable quantum-classical hybrid systems in machine learning.