Learning a control field for simultaneous state transformation in CO molecules

We employ a numerical optimal control algorithm to learn optimal control fields for achieving a special simultaneous state transformation in CO molecules. The simultaneous state transformation means that for different initial states, the same control fields can accomplish the control task of driving them into relevant desired target states. The sampling-based learning control method is applied to design robust control fields for the state transformation task. A special simultaneous transformation of two states is considered and numerical results show excellent performance for the control task.