  Quantum optimal control involves setting up an objective function that
evaluates the quality of an operator representing the realized process w.r.t.
the target process. Here we propose a stronger objective function which
incorporates not only the target operator but also a set of its orthogonal
operators. We find significantly superior convergence of optimization routines
with the combined influences of all the operators. We refer to this method as
the $\textit{push-pull}$ optimization. In particular, we describe adopting the
push-pull optimization to a gradient based approach and a variational-principle
based approach. We carry out extensive numerical simulations of the push-pull
optimization of quantum controls on a pair of Ising coupled qubits. Finally, we
demonstrate its experimental application by preparing a long-lived
singlet-order in a two-qubit system using NMR techniques.
