  Superconducting quantum systems are promising candidates for quantum
information processing due to their scalability and design flexibility.
However, the existence of defects, fluctuations, and inaccuracies is
unavoidable for practical superconducting quantum circuits. In this paper, a
sampling-based learning control (SLC) method is used to guide the design of
control fields for manipulating superconducting quantum systems. 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.
