Algorithm for kernel-based machine learning

We present an algorithm that is able to determine the hyperparameters of a kernel-based representation of a machine-learning representation of input-output data. The algorithm is best applied on fully error-corrected quantum machines but can be applied on near-term computers. We present the scaling relationships and potential applications for quantum chemistry and other areas of physics.