  Quantum state tomography is an essential component of modern quantum
technology. In application to continuous-variable harmonic-oscilator systems,
such as the electromagnetic field, existing tomography methods typically
reconstruct the state in discrete bases, and are hence limited to states with
relatively low amplitudes and energies. Here we overcome this limitation by
utilizing a feed-forward neural network to obtain the density matrix directly
in the continuous position basis. An important benefit of our approach is the
ability to choose specific regions in the phase space for detailed
reconstruction. This results in relatively slow scaling of the amount of
resources required for the reconstruction with the state amplitude, and hence
allows us to dramatically increase the range of amplitudes accessible with our
method.
