论文标题
用量子储层网络重建量子状态
Reconstructing quantum states with quantum reservoir networks
论文作者
论文摘要
重建量子状态是各种新兴量子技术的重要任务。重建量子状态的密度矩阵的过程称为量子状态断层扫描。通常,随着任意量子状态的层析成像是具有挑战性的,因为有效协议的范式仍然在对不同类型的量子状态的特定技术中应用。在这里,我们根据储层计算的框架介绍了一个量子状态断层扫描平台。它形成量子神经网络,并作为重建任意量子状态(有限维数或连续变量)的综合设备。这仅在单个物理设置中仅测量平均职业数量,而无需任何最佳测量基础或相关测量值。
Reconstructing quantum states is an important task for various emerging quantum technologies. The process of reconstructing the density matrix of a quantum state is known as quantum state tomography. Conventionally, tomography of arbitrary quantum states is challenging as the paradigm of efficient protocols has remained in applying specific techniques for different types of quantum states. Here we introduce a quantum state tomography platform based on the framework of reservoir computing. It forms a quantum neural network, and operates as a comprehensive device for reconstructing an arbitrary quantum state (finite dimensional or continuous variable). This is achieved with only measuring the average occupation numbers in a single physical setup, without the need of any knowledge of optimum measurement basis or correlation measurements.