  Deep reinforcement learning has been recognized as an efficient technique to
design optimal strategies for different complex systems without prior knowledge
of the control landscape. To achieve a fast and precise control for quantum
systems, we propose a novel deep reinforcement learning approach by
constructing a curriculum consisting of a set of intermediate tasks defined by
a fidelity threshold. Tasks among a curriculum can be statically determined
using empirical knowledge or adaptively generated with the learning process. By
transferring knowledge between two successive tasks and sequencing tasks
according to their difficulties, the proposed curriculum-based deep
reinforcement learning (CDRL) method enables the agent to focus on easy tasks
in the early stage, then move onto difficult tasks, and eventually approaches
the final task. Numerical simulations on closed quantum systems and open
quantum systems demonstrate that the proposed method exhibits improved control
performance for quantum systems and also provides an efficient way to identify
optimal strategies with fewer control pulses.
