  This paper investigates the impact of control field noise on the optimal
manipulation of quantum dynamics. Simulations are performed on several
multilevel quantum systems with the goal of population transfer in the presence
of significant control noise. The noise enters as run-to-run variations in the
control amplitude and phase with the observation being an ensemble average over
many runs as is commonly done in the laboratory. A genetic algorithm with an
improved elitism operator is used to find the optimal field that either fights
against or cooperates with control field noise. When seeking a high control
yield it is possible to find fields that successfully fight with the noise
while attaining good quality stable results. When seeking modest control
yields, fields can be found which are optimally shaped to cooperate with the
noise and thereby drive the dynamics more efficiently. In general, noise
reduces the coherence of the dynamics, but the results indicate that population
transfer objectives can be met by appropriately either fighting or cooperating
with noise, even when it is intense.
