  Kernel methods are of current interest in quantum machine learning due to
similarities with quantum computing in how they process information in
high-dimensional feature (Hilbert) spaces. Kernels are believed to offer
particular advantages when they cannot be computed classically, so a kernel
matrix with indisputably nonclassical elements is desirable provided it can be
generated efficiently in a particular physical machine. Kerr nonlinearities,
known to be a route to universal continuous variable (CV) quantum computation,
may be able to play this role for quantum machine learning. We propose a
quantum hardware kernel implementation scheme based on superconducting quantum
circuits. The scheme does not use qubits or quantum circuits but rather
exploits the analogue features of Kerr coupled modes. Our approach is more akin
to the growing number of analog machine learning schemes based on sampling
quantum probabilities directly in an engineered device by stochastic quantum
control.
