Traditional quantum system control methods often face different constraints,
and are easy to cause both leakage and stochastic control errors under the
condition of limited resources. Reinforcement learning has been proved as an
efficient way to complete the quantum system control task. To learn a
satisfactory control strategy under the condition of limited resources, a
quantum system control method based on enhanced reinforcement learning
(QSC-ERL) is proposed. The states and actions in reinforcement learning are
mapped to quantum states and control operations in quantum systems. By using
new enhanced neural networks, reinforcement learning can quickly achieve the
maximization of long-term cumulative rewards, and a quantum state can be
evolved accurately from an initial state to a target state. According to the
number of candidate unitary operations, the three-switch control is used for
simulation experiments. Compared with other methods, the QSC-ERL achieves close
to 1 fidelity learning control of quantum systems, and takes fewer episodes to
quantum state evolution under the condition of limited resources.

提出了一种基于增强强化学习（QSC-ERL）的量子系统控制方法，使用增强神经网络将强化学习中的状态和动作映射到量子系统中的量子状态和控制操作，在有限资源的条件下能够实现接近 1 的保真度的量子系统学习控制，并且在量子状态演化中所需的周期较少。