Quantum devices need precise control to achieve their full capability. In this work, we address the problem of controlling closed quantum systems, tackling two main issues. First, in practice the control signals are usually subject to unknown classical distortions that could arise from the device fabrication, material properties and/or instruments generating those signals. Second, in most cases modeling the system is very difficult or not even viable due to uncertainties in the relations between some variables and inaccessibility to some measurements inside the system. In this paper, we introduce a general model-free control approach based on deep reinforcement learning (DRL), that can work for any controllable closed quantum system. We train a deep neural network (NN), using the REINFORCE policy gradient algorithm to control the state probability distribution of a controllable closed quantum system as it evolves, and drive it to different target distributions. We present a novel controller architecture that comprises multiple NNs. This enables accommodating as many different target state distributions as desired, without increasing the complexity of the NN or its training process. The used DRL algorithm works whether the control problem can be modeled as a Markov decision process (MDP) or a partially observed MDP. Our method is valid whether the control signals are discrete- or continuous-valued. We verified our method through numerical simulations based on a photonic waveguide array chip. We trained a controller to generate sequences of different target output distributions of the chip with fidelity higher than 99%, where the controller showed superior performance in canceling the classical signal distortions.

中文翻译：

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量子器件的无模型失真消除和控制


Quantum 设备需要精确控制才能实现其全部功能。在这项工作中，我们解决了控制封闭量子系统的问题，解决了两个主要问题。首先，在实践中，控制信号通常会受到未知的经典失真的影响，这些失真可能由器件制造、材料特性和/或产生这些信号的仪器引起。其次，在大多数情况下，由于某些变量之间关系的不确定性以及系统内部某些测量的可访问性，系统建模非常困难，甚至不可行。在本文中，我们介绍了一种基于深度强化学习 （DRL） 的通用无模型控制方法，该方法适用于任何可控的封闭量子系统。我们训练一个深度神经网络 （NN），使用 REINFORCE 策略梯度算法来控制可控封闭量子系统在演化过程中的状态概率分布，并将其驱动到不同的目标分布。我们提出了一种包含多个 NN 的新型控制器架构。这样就可以根据需要容纳尽可能多的不同目标状态分布，而不会增加 NN 或其训练过程的复杂性。无论控制问题是否可以建模为马尔可夫决策过程 （MDP） 还是部分观察到的 MDP，所使用的 DRL 算法都有效。无论控制信号是离散值还是连续值，我们的方法都是有效的。我们通过基于光子波导阵列芯片的数值模拟验证了我们的方法。我们训练了一个控制器来生成芯片不同目标输出分布的序列，保真度高于 99%，其中控制器在消除经典信号失真方面表现出卓越的性能。