Enhancing Quantum Support Vector Machines through Variational Kernel Training
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866910314351558656 |
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| author | Innan, Nouhaila Khan, Muhammad Al-Zafar Panda, Biswaranjan Bennai, Mohamed |
| author_facet | Innan, Nouhaila Khan, Muhammad Al-Zafar Panda, Biswaranjan Bennai, Mohamed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_06063 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Enhancing Quantum Support Vector Machines through Variational Kernel Training Innan, Nouhaila Khan, Muhammad Al-Zafar Panda, Biswaranjan Bennai, Mohamed Quantum Physics Machine Learning 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. |
| title | Enhancing Quantum Support Vector Machines through Variational Kernel Training |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2305.06063 |