  Introducing quantum sensors as solution to real-world problem demands
reliability and controllability outside laboratory conditions. Producers and
operators ought to be assumed to have limited resources ready available for
calibration, and yet, they should be able to trust the devices. Neural networks
are almost ubiquitous for similar tasks for classical sensors: here we show the
applications of this technique to calibrating a quantum photonic sensor. This
is based on a set of training data, collected only relying on the available
probe states, hence reducing overheads. We found that covering finely the
parameter space is key to achieve uncertainties close to their ultimate level.
This technique has potential to become the standard approach to calibrate
quantum sensors.
