Learning control of population transfer between subspaces of quantum systems using an adaptive target scheme

An adaptive target scheme is implemented for learning control of population transfer between subspaces of quantum systems. In this control scheme, the target state is updated according to the renormalized yield in the desired subspace throughout the learning iterations, to obtain the desired laser control field. In the numerical experiments, we perform learning control simulations based on a V-type three-subspace quantum system. The field obtained by learning control can transfer the population to the target subspace with high probability. In comparison with a fixed target state, this adaptive target scheme proves to be more efficient for the quantum control problem under consideration.