  Physical systems can be used as an information processing substrate and with
that extend traditional computing architectures. For such an application the
experimental platform must guarantee pristine control of the initial state, the
temporal evolution and readout. All these ingredients are provided by modern
experimental realizations of atomic Bose Einstein condensates. By embedding the
nonlinear evolution of a quantum gas in a Machine Learning pipeline, one can
represent nonlinear functions while only linear operations on classical
computing of the pipeline are necessary. We demonstrate successful regression
and interpolation of a nonlinear function using a quasi one-dimensional cloud
of potassium atoms and characterize the performance of our system.
