  We study the functional relationship between quantum control pulses in the
idealized case and the pulses in the presence of an unwanted drift. We show
that a class of artificial neural networks called LSTM is able to model this
functional relationship with high efficiency, and hence the correction scheme
required to counterbalance the effect of the drift. Our solution allows
studying the mapping from quantum control pulses to system dynamics and then
analysing the robustness of the latter against local variations in the control
profile.
