Quantum control has been of increasing interest in recent years, e.g. for tasks like state initialization and stabilization. Feedback-based strategies are particularly powerful, but also hard to find, due to the exponentially increased search space. Deep reinforcement learning holds great promise in this regard. It may provide new answers to difficult questions, such as whether nonlinear measurements can compensate for linear, constrained control. Here we show that reinforcement learning can successfully discover such feedback strategies, without prior knowledge. We illustrate this for state preparation in a cavity subject to quantum-non-demolition detection of photon number, with a simple linear drive as control. Fock states can be produced and stabilized at very high fidelity. It is even possible to reach superposition states, provided the measurement rates for different Fock states can be controlled as well.

中文翻译：

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具有弱非线性测量的量子态准备的深度强化学习

近年来，量子控制受到越来越多的关注，例如用于状态初始化和稳定等任务。基于反馈的策略特别强大，但也很难找到，因为搜索空间呈指数增长。深度强化学习在这方面有很大的前景。它可能会为难题提供新的答案，例如非线性测量是否可以补偿线性约束控制。在这里，我们表明强化学习可以成功地发现这种反馈策略，而无需先验知识。我们说明了这一点，用于在腔中进行状态准备，该腔受到光子数的量子非破坏检测，使用简单的线性驱动作为控制。可以以非常高的保真度产生和稳定 Fock 状态。甚至可以达到叠加态，