  Robust control design for quantum systems has been recognized as a key task
in the development of practical quantum technology. In this paper, we present a
systematic numerical methodology of sampling-based learning control (SLC) for
control design of quantum systems with Hamiltonian uncertainties. The SLC
method includes two steps of "training" and "testing and evaluation". In the
training step, an augmented system is constructed by sampling uncertainties
according to possible distributions of uncertainty parameters. A gradient flow
based learning and optimization algorithm is adopted to find the control for
the augmented system. In the process of testing and evaluation, a number of
samples obtained through sampling the uncertainties are tested to evaluate the
control performance. Numerical results demonstrate the success of the SLC
approach. The SLC method has potential applications for robust control design
of quantum systems.
