Sampling-based learning control of quantum systems

Quantum information technology (QIT) has many important potential applications. Robustness has been recognised as a key task in developing practical QIT. In this research, we presented a sampling-based learning control (SLC) method for robust control design. In particular, the SLC method is employed for optimal control of the state-to-state transition probability for inhomogeneous quantum ensembles of spins as well as three-level atomic systems. Numerical results are presented, showing excellent performance for the challenging task of simultaneously steering the members of inhomogeneous quantum ensembles to the same desired state. In addition, the SLC method is also used to guide the design of control fields for manipulating superconducting quantum systems when possible defects, fluctuations, and inaccuracies exist in superconducting circuits. Numerical results for one-qubit systems and coupled two-qubit systems show that the ‘‘smart’’ fields learned using the SLC method can achieve robust manipulation of superconducting qubits, even in the presence of large fluctuations and inaccuracies. These results have been published in [Chen C, Dong D, Long R, Petersen I R and Rabitz H. Sampling-based learning control of inhomogeneous quantum ensembles. Physical Review A, 2014, Vol. 89, p. 023402].