  Deep neural networks are a powerful tool for the characterization of quantum
states.
  Existing networks are typically trained with experimental data gathered from
the specific quantum state that needs to be characterized.
  But is it possible to train a neural network offline and to make predictions
about quantum states other than the ones used for the training?
  Here we introduce a model of network that can be trained with classically
simulated data from a fiducial set of states and measurements, and can later be
used to characterize quantum states that share structural similarities with the
states in the fiducial set. With little guidance of quantum physics, the
network builds its own data-driven representation of quantum states, and then
uses it to predict the outcome statistics of quantum measurements that have not
been performed yet.
  The state representation produced by the network can also be used for tasks
beyond the prediction of outcome statistics, including clustering of quantum
states and identification of different phases of matter.
  Our network model provides a flexible approach that can be applied to online
learning scenarios, where predictions must be generated as soon as experimental
data become available, and to blind learning scenarios where the learner has
only access to an encrypted description of the quantum hardware.
