Enhancing Quantum Support Vector Machines through Variational Kernel Training

• URL: http://arxiv.org/abs/2305.06063v2
• Date: Thu, 11 May 2023 05:37:22 GMT
• Title: Enhancing Quantum Support Vector Machines through Variational Kernel Training
• Authors: Nouhaila Innan, Muhammad Al-Zafar Khan, Biswaranjan Panda, and Mohamed Bennai
• Abstract summary: This paper focuses on the two existing quantum kernel SVM and quantum variational SVM methods. We present a novel approach that synergizes the strengths of QK-SVM and QV-SVM to enhance accuracy. Our results demonstrate that QVK-SVM holds tremendous potential as a reliable and transformative tool for QML applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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