Benchmarking Quantum Kernels Across Diverse and Complex Data

Quantum kernel methods are emerging as a significant area in quantum machine learning, yet their effectiveness on diverse, high-dimensional real-world data has not been thoroughly validated. This research introduces a variational quantum kernel framework that employs resource-efficient ansätze for complex classification tasks. A benchmark was conducted on eight challenging datasets, revealing that the proposed quantum kernel outperformed standard classical kernels, such as the radial basis function kernel. Further research is necessary to fully evaluate the practical advantages of quantum kernels.