Emerging reinforcement learning techniques using deep neural networks have
shown great promise in control optimization. They harness non-local
regularities of noisy control trajectories and facilitate transfer learning
between tasks. To leverage these powerful capabilities for quantum control
optimization, we propose a new control framework to simultaneously optimize the
speed and fidelity of quantum computation against both leakage and stochastic
control errors. For a broad family of two-qubit unitary gates that are
important for quantum simulation of many-electron systems, we improve the
control robustness by adding control noise into training environments for
reinforcement learning agents trained with trusted-region-policy-optimization.
The agent control solutions demonstrate a two-order-of-magnitude reduction in
average-gate-error over baseline stochastic-gradient-descent solutions and up
to a one-order-of-magnitude reduction in gate time from optimal gate synthesis
counterparts.

提出一种新的量子控制框架，通过在强化学习智能体的训练环境中加入控制噪声，利用强化学习技术优化量子计算的速度和保真度以及对泄漏和随机控制误差的抗扰性，最终取得了一定的优化成果。