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：量子机器学习近期取得了巨大进展，其中量子支持向量机成为一种极具前景的模型。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。尽管这两种方法均展现出显著成效，我们提出了一种融合两者优势的新方法以提高准确率。我们提出的模型——量子变分核支持向量机，同时利用了量子核与量子变分算法。基于鸢尾花数据集的广泛实验表明，量子变分核支持向量机在准确率、损失函数及混淆矩阵指标上均优于现有模型。实验结果证实，量子变分核支持向量机作为量子机器学习中可靠且变革性的工具具有巨大潜力，因此我们推荐在未来的量子机器学习研究中采用该模型。