  Quantum kernel methods leverage a kernel function computed by embedding input
information into the Hilbert space of a quantum system. However, large Hilbert
spaces can hinder generalization capability, and the scalability of quantum
kernels becomes an issue. To overcome these challenges, various strategies
under the concept of inductive bias have been proposed. Bandwidth optimization
is a promising approach that can be implemented using quantum simulation
platforms. We propose trapped-ion simulation platforms as a means to compute
quantum kernels and demonstrate their effectiveness for binary classification
tasks. We compare the performance of the proposed method with an optimized
classical kernel and evaluate the robustness of the quantum kernel against
noise. The results show that ion trap platforms are well-suited for quantum
kernel computation and can achieve high accuracy with only a few qubits.
