A Versatile Variational Quantum Kernel Framework for Non-Trivial Classification
The study developed a variational quantum kernel framework using resource-efficient ansätze and a parameter scaling technique to improve convergence for complex classification tasks. Benchmarking across eight real-world datasets showed that the proposed quantum kernels achieved competitive accuracy against classical RBF kernels in simulation, demonstrating their potential as versatile tools for high-dimensional machine learning.