Tuned quantum kernels match classical RBF on four datasets

Quantum kernel methods are the most concrete near-term proposal for quantum machine learning on classical data, yet benchmarks disagree on whether they outperform tuned classical kernels. The study imposes controls to compare quantum and classical kernels, tuning the bandwidth on the same grid budget and recomputing the kernel inside every fold, and finds tuned quantum kernels are statistically indistinguishable from a tuned RBF on all four real datasets.