  In quantum control, the robustness with respect to uncertainties in the
system's parameters or driving field characteristics is of paramount importance
and has been studied theoretically, numerically and experimentally. We test in
this paper stochastic search procedures (Stochastic gradient descent and the
Adam algorithm) that sample, at each iteration, from the distribution of the
parameter uncertainty, as opposed to previous approaches that use a fixed grid.
We show that both algorithms behave well with respect to benchmarks and discuss
their relative merits. In addition the methodology allows to address high
dimensional parameter uncertainty; we implement numerically, with good results,
a 3D and a 6D case.
