  Inferring a process matrix characterizing a quantum channel from experimental
measurements is a key issue of quantum information. Sometimes the noise
affecting the measured counts brings to matrices very different from the
expected ones and the mainly used estimation procedure, i.e. the maximum
likelihood estimation (MLE), is also characterized by several drawbacks. To
lower the noise could be necessary to increase the experimental resources, e.g.
time for each measurement. In this paper, an alternative procedure, based on
suitable Neural Networks, has been implemented and optimized to obtain a
denoised process matrix and this approach has been tested with a specific
quantum channel, i.e. a Control Phase. This promising method relies on the
analogy that can be established between the elements of a process matrix and
the pixels of an im
