  Robust control design for quantum systems is a challenging and key task for
practical technology. In this work, we apply neural networks to learn the
control problem for the semiclassical Schr\"odinger equation, where the control
variable is the potential given by an external field that may contain
uncertainties. Inspired by a relevant work [29], we incorporate the
sampling-based learning process into the training of networks, while combining
with the fast time-splitting spectral method for the Schr\"odinger equation in
the semiclassical regime. The numerical results have shown the efficiency and
accuracy of our proposed deep learning approach.
