Bose-Einstein condensate experiment as a nonlinear block of a machine learning pipeline

Abstract

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 a quantum gas experiment in a machine learning pipeline, one can represent nonlinear functions while only linear operations on classical computers of the pipeline are necessary. We demonstrate successful regression and interpolation of a nonlinear function using an elongated cloud of potassium atoms and characterize the performance of our system.