Enhancing Quantum Support Vector Machines through Variational Kernel Training

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.

15 pages, 13 figures, 1 table

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Cited by in corpus (8)

• QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection
• Simulation of a Variational Quantum Perceptron using Grover's Algorithm
• A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection
• LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networks
• Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection
• MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption
• Quantum Bayesian Networks for Machine Learning in Oil-Spill Detection
• Equivalence between exponential concentration in quantum machine learning kernels and barren plateaus in variational algorithms