Enhancing Quantum Support Vector Machines through Variational Kernel Training
This is an incremental improvement for quantum machine learning researchers, potentially enhancing model reliability in specific applications.
The paper tackled the problem of improving quantum support vector machines (QSVMs) by synergizing two existing methods, resulting in a new model called QVK-SVM that outperformed both on the Iris dataset in accuracy, loss, and confusion matrix indicators.
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.