  Optimal control theory is a powerful tool for solving control problems in
quantum mechanics, ranging from the control of chemical reactions to the
implementation of gates in a quantum computer. Gradient-based optimization
methods are able to find high fidelity controls, but require considerable
numerical effort and often yield highly complex solutions. We propose here to
employ a two-stage optimization scheme to significantly speed up convergence
and achieve simpler controls. The control is initially parametrized using only
a few free parameters, such that optimization in this pruned search space can
be performed with a simplex method. The result, considered now simply as an
arbitrary function on a time grid, is the starting point for further
optimization with a gradient-based method that can quickly converge to high
fidelities. We illustrate the success of this hybrid technique by optimizing a
holonomic phasegate for two superconducting transmon qubits coupled with a
shared transmission line resonator, showing that a combination of Nelder-Mead
simplex and Krotov's method yields considerably better results than either one
of the two methods alone.
