  Quantum variational algorithms have garnered significant interest recently,
due to their feasibility of being implemented and tested on noisy intermediate
scale quantum (NISQ) devices. We examine the robustness of the quantum
approximate optimization algorithm (QAOA), which can be used to solve certain
quantum control problems, state preparation problems, and combinatorial
optimization problems. We demonstrate that the error of QAOA simulation can be
significantly reduced by robust control optimization techniques, specifically,
by sequential convex programming (SCP), to ensure error suppression in
situations where the source of the error is known but not necessarily its
magnitude. We show that robust optimization improves both the objective
landscape of QAOA as well as overall circuit fidelity in the presence of
coherent errors and errors in initial state preparation.
