  Quantum computers have the opportunity to be transformative for a variety of
computational tasks. Recently, there have been proposals to use the
unsimulatably of large quantum devices to perform regression, classification,
and other machine learning tasks with quantum advantage by using kernel
methods. While unsimulatably is a necessary condition for quantum advantage in
machine learning, it is not sufficient, as not all kernels are equally
effective. Here, we study the use of quantum computers to perform the machine
learning tasks of one- and multi-dimensional regression, as well as
reinforcement learning, using Gaussian Processes. By using approximations of
performant classical kernels enhanced with extra quantum resources, we
demonstrate that quantum devices, both in simulation and on hardware, can
perform machine learning tasks at least as well as, and many times better than,
the classical inspiration. Our informed kernel design demonstrates a path
towards effectively utilizing quantum devices for machine learning tasks.
