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IEEE International Conference on Quantum Computing and Engineering (QCE)

Support Vector Classification and Regression Using an Adaptive Quantum-Classical Dual Kernel

Dhana Phassadawongse, Stephen John Turner

Published Aug 30, 2025• 1 citation
Publisher

Abstract

This paper introduces a generalized quantum-classical dual kernel learning method for Support Vector Machines (SVMs), extending its application to both classification and regression tasks. Our method combines quantum and classical kernels via an adaptively weighted linear combination, forming a dual kernel learning technique that outperforms models relying solely on either kernel type. In this paper, we demonstrate the effectiveness of our approach on several real-world datasets in terms of classification accuracy and for regression, high$R^{2}$-score and low Root Mean Square Error (RMSE). Furthermore, we evaluate the dual kernel model on both ideal and noisy quantum backends using the IBM Qiskit toolkit. Under ideal conditions, the dual kernel method consistently maintains superior performance compared to purely classical or quantum kernels. While the presence of noise may affect performance, the dual kernel still achieves results that are competitive with the best individual kernels, underscoring its robustness and practical viability in real-world applications.

DOI
10.1109/qce65121.2025.00194

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This paper introduces a generalized quantum-classical dual kernel learning method for Support Vector Machines (SVMs), extending its application to both classification and regression tasks. Our method combines quantum and classical kernels via an adaptively weighted linear combination, forming a dual kernel learning technique that outperforms models relying solely on either kernel type. In this paper, we demonstrate the effectiveness of our approach on several real-world datasets in terms of classification accuracy and for regression, highR2R^{2}R2-score and low Root Mean Square Error (RMSE). Furthermore, we evaluate the dual kernel model on both ideal and noisy quantum backends using the IBM Qiskit toolkit. Under ideal conditions, the dual kernel method consistently maintains superior performance compared to purely classical or quantum kernels. While the presence of noise may affect performance, the dual kernel still achieves results that are competitive with the best individual kernels, underscoring its robustness and practical viability in real-world applications.
Quantum machine learning has emerged as a promising frontier, leveraging the principles of quantum computing to enhance classical algorithms. This paper proposes an optimized Quantum Support Vector Classifier (QSVC) for the classification of the MNIST dataset (a benchmark in handwritten digit recognition). We employ a Chebyshev Parameterized Quantum Circuit (PQC) with an increased number of qubits and layers, and utilize an existing projected quantum kernel method to effectively capture the complex patterns in the data. To enhance model robustness, data augmentation techniques are applied, and dimensionality reduction is performed using Principal Component Analysis (PCA). The proposed QSVC demonstrates competitive accuracy on both the training and testing datasets, highlighting the potential of quantum kernel methods in practical machine learning applications. A comparative analysis with classical Support Vector Machines (SVM) demonstrates the feasibility of quantum methods for high-dimensional classification tasks. Additionally, we investigate the impact of varying the number of qubits in the quantum circuit, providing in-depth insights into the scalability and performance trade-offs of quantum kernel methods.
Abstract Quantum machine learning is a rapidly growing field at the intersection of quantum computing and machine learning. In this work, we examine our quantum machine learning models, which are based on quantum support vector classification (QSVC) and quantum support vector regression (QSVR). We investigate these models using a quantum-circuit simulator, both with and without noise, as well as the IonQ Harmony quantum processor. For the QSVC tasks, we use a dataset containing fraudulent credit card transactions and image datasets (the MNIST and the Fashion-MNIST datasets); for the QSVR tasks, we use a financial dataset and a materials dataset. For the classification tasks, the performance of our QSVC models using 4 qubits of the trapped-ion quantum computer was comparable to that obtained from noiseless quantum-circuit simulations. The result is consistent with the analysis of our device-noise simulations with varying qubit-gate error rates. For the regression tasks, applying a low-rank approximation to the noisy quantum kernel, in combination with hyperparameter tuning in ε-SVR, improved the performance of the QSVR models on the near-term quantum device. Our results suggest that the quantum kernel, as described by our shallow quantum circuit, can be effectively used for both QSVC and QSVR tasks, indicating its resistance to noise and its adaptability to various datasets.
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Support vector machines are powerful and frequently used machine learning methods for classification and regression tasks, which rely on the construction of kernel matrices. While crucial for the performance of this machine learning approach, choosing the most suitable kernel is highly problem-dependent. The emergence of quantum computers and quantum machine learning techniques provides new possibilities for generating powerful quantum kernels. Within this work, we solve a real-world price forecasting problem using fidelity and projected quantum kernels, which are promising candidates for the utility of near-term quantum computing. In our analysis, we examine and validate the most auspicious quantum kernels from literature and compare their performance with an optimized classical kernel. Unlike previous work on quantum support vector machines, our dataset includes categorical features that need to be encoded as numerical features, which we realize by using the one-hot-encoding scheme. One-hot-encoding, however, increases the dimensionality of the dataset significantly, which collides with the current limitations of noisy intermediate scale quantum computers. To overcome these limitations, we use autoencoders to learn a low-dimensional representation of the feature space that still maintains the most important information of the original data. To examine the impact of autoencoding, we compare the results of the encoded date with the results of the original, unencoded dataset. We could demonstrate that quantum kernels are comparable to or even better than the classical support vector machine kernels regarding the mean absolute percentage error scores for both encoded and unencoded datasets.
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.
Quantum Machine Learning (QML) integrates principles of quantum computation with statistical learning to address classification tasks. This paper conducts a comparative study of three paradigms—classical Support Vector Classification (SVC), Quantum Support Vector Classification (QSVC) using quantum kernels, and QSVC enhanced via the Pegasos stochastic algorithm. We benchmark classification accuracy on a SARS-CoV-2 dataset, analyzing model robustness and resource requirements. Our results indicate that while classical SVC maintains superior scalability and stability for large datasets, QSVC variants show competitive accuracy across simulator backends (Central Processing Units or CPUs and Graphical Processor Units or GPUs) and may achieve quantum advantage as hardware matures. Implications for hybrid workflows and future research directions are discussed.