Learning control for maximizing the purity at a fixed time in an open quantum system

In quantum information, the purity is an important performance index that is closely related to the success of quantum computation and communication tasks. For an open quantum system, the purity of the system may decrease as the system evolves. An important task is to maximize the purity of an open quantum system at a given fixed time. In this paper, we employ a learning control algorithm to search a control field to maximize the purity of open quantum systems at a predetermined time. Using a coupled system involving carbon monoxide and copper as an example, we perform learning control simulations by solving the quantum Liouville-von Neumann equation based on a three-level model. In the control scheme, the target state is updated after each iteration to obtain the desired control field, using the flexibility to avoid setting any specific targets in advance.