  Complete characterization of states and processes that occur within quantum
devices is crucial for understanding and testing their potential to outperform
classical technologies for communications and computing. However, solving this
task with current state-of-the-art techniques becomes unwieldy for large and
complex quantum systems. Here we realize and experimentally demonstrate a
method for complete characterization of a quantum harmonic oscillator based on
an artificial neural network known as the restricted Boltzmann machine. We
apply the method to optical homodyne tomography and show it to allow full
estimation of quantum states based on a smaller amount of experimental data
compared to state-of-the-art methods. We link this advantage to reduced
overfitting. Although our experiment is in the optical domain, our method
provides a way of exploring quantum resources in a broad class of large-scale
physical systems, such as superconducting circuits, atomic and molecular
ensembles, and optomechanical systems.
