At its core, quantum mechanics is a theory developed to describe fundamental observations in the spectroscopy of solids and gases. Despite these practical roots, however, quantum theory is infamous for being highly counterintuitive, largely due to its intrinsically probabilistic nature. Neural networks have recently emerged as a powerful tool that can extract nontrivial correlations in vast datasets. These networks routinely outperform state-of-the-art techniques in language translation, medical diagnosis, and image recognition. It remains to be seen if neural networks can be trained to predict stochastic quantum evolution without a priori specifying the rules of quantum theory. Here, we demonstrate that a recurrent neural network can be trained in real time to infer the individual quantum trajectories associated with the evolution of a superconducting qubit under unitary evolution, decoherence, and continuous measurement from physical observations only. The network extracts the system Hamiltonian, measurement operators, and physical parameters. It is also able to perform tomography of an unknown initial state without any prior calibration. This method has the potential to greatly simplify and enhance tasks in quantum systems such as noise characterization, parameter estimation, feedback, and optimization of quantum control.

中文翻译：

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使用递归神经网络从物理观察重建超导量子位的量子动力学

量子力学的核心是发展用来描述固体和气体光谱学中基本观察的理论。尽管有这些实践根源，但是量子理论以高度反直觉而臭名昭著，这在很大程度上是由于其固有的概率性质。神经网络最近已成为一种强大的工具，可以提取大量数据集中的非平凡相关性。这些网络通常在语言翻译，医学诊断和图像识别方面优于最新技术。是否可以在没有先验先验的情况下训练神经网络来预测随机量子演化还有待观察指定量子理论的规则。在这里，我们证明了可以实时训练循环神经网络，以仅根据物理观测来推断与超导量子位的演化相关的单个量子轨迹，这些量子轨迹是在整体演化，解干和连续测量下进行的。网络提取系统哈密顿量，测量算子和物理参数。它也可以执行未知初始状态的层析成像，而无需任何事先校准。这种方法有可能极大简化和增强量子系统中的任务，例如噪声表征，参数估计，反馈和量子控制优化。