  Characterization of quantum objects, being them states, processes, or
measurements, complemented by previous knowledge about them is a valuable
approach, especially as it leads to routine procedures for real-life
components. To this end, Machine Learning algorithms have demonstrated to
successfully operate in presence of noise, especially for estimating specific
physical parameters. Here we show that a neural network (NN) can improve the
tomographic estimate of parameters by including a convolutional stage. We
applied our technique to quantum process tomography for the characterization of
several quantum channels. We demonstrate that a stable and reliable operation
is achievable by training the network only with simulated data. The obtained
results show the viability of this approach as an effective tool based on a
completely new paradigm for the employment of NNs operating on classical data
produced by quantum systems.
