Quantum machine learning (QML) has witnessed immense progress recently, with
quantum support vector machines (QSVMs) emerging as a promising model. This
paper focuses on the two existing QSVM methods: quantum kernel SVM (QK-SVM) and
quantum variational SVM (QV-SVM). While both have yielded impressive results,
we present a novel approach that synergizes the strengths of QK-SVM and QV-SVM
to enhance accuracy. Our proposed model, quantum variational kernel SVM
(QVK-SVM), leverages the quantum kernel and quantum variational algorithm. We
conducted extensive experiments on the Iris dataset and observed that QVK-SVM
outperforms both existing models in terms of accuracy, loss, and confusion
matrix indicators. Our results demonstrate that QVK-SVM holds tremendous
potential as a reliable and transformative tool for QML applications. Hence, we
recommend its adoption in future QML research endeavors.

本文针对 QSVM 模型的两种方法：量子核 SVM（QK-SVM）和量子变分 SVM（QV-SVM）进行研究，并提出了一种将二者优势相结合的新型模型：量子变分核 SVM（QVK-SVM）。实验结果表明，QVK-SVM 在准确性、损失和混淆矩阵指标方面均优于现有模型，具有很大的应用潜力，值得被推广应用于未来的量子机器学习研究。