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.

翻译：量子机器学习近年来取得了巨大进展，其中量子支持向量机成为一种有前景的模型。本文聚焦于两种现有的量子支持向量机方法：量子核支持向量机和量子变分支持向量机。尽管这两种方法都已取得显著成果，但我们提出了一种新颖的方法，通过协同量子核支持向量机和量子变分支持向量机的优势来提高精度。我们提出的模型——量子变分核支持向量机，利用了量子核和量子变分算法。我们针对鸢尾花数据集进行了大量实验，观察到量子变分核支持向量机在精度、损失和混淆矩阵指标上均优于现有的两种模型。我们的结果表明，量子变分核支持向量机作为量子机器学习应用中的可靠且变革性工具拥有巨大潜力。因此，我们建议在未来的量子机器学习研究工作中采用该模型。