论文标题
线性量子系统:教程
Linear quantum systems: a tutorial
论文作者
论文摘要
本教程的目的是简要介绍线性量子控制系统。首先提出了线性量子控制系统的数学模型,然后给出了一些基本控制理论概念,例如稳定性,可控性和可观察性,它们与量子信息科学中的几个重要概念密切相关,例如无折叠的子系统,量子非量子性非分离变量变量和反作用逃避测量。之后,尤其是引入量子高斯状态,提出了信息理论的不确定性关系,该关系通常比众所周知的海森堡不确定性关系给混合高斯状态提供更好的束缚。量子卡尔曼滤波器用于量子线性系统,这是用于经典(即非量化机械)线性系统的卡尔曼滤波器的量子类比。记录了量子线性系统的量子卡尔曼典型分解,并通过最近的实验说明了其应用。由于单光子状态和多光子状态在量子信息技术中是有用的资源,因此提出了量子线性系统对这些类型的输入的响应。最后,简要介绍了量子线性系统的相干反馈控制,并使用最新的实验来证明量子线性系统和网络理论的有效性。简要介绍了量子线性系统的DBECK控制,并且最新的实验用于证明量子线性系统和网络理论的有效性。
The purpose of this tutorial is to give a brief introduction to linear quantum control systems. The mathematical model of linear quantum control systems is presented first, then some fundamental control-theoretic notions such as stability, controllability and observability are given, which are closely related to several important concepts in quantum information science such as decoherence-free subsystems, quantum non-demolition variables, and back-action evasion measurements. After that, quantum Gaussian states are introduced, in particular, an information-theoretic uncertainty relation is presented which often gives a better bound for mixed Gaussian states than the well-known Heisenberg uncertainty relation. The quantum Kalman filter is presented for quantum linear systems, which is the quantum analogy of the Kalman filter for classical (namely, non-quantum-mechanical) linear systems. The quantum Kalman canonical decomposition for quantum linear systems is recorded, and its application is illustrated by means of a recent experiment. As single- and multi-photon states are useful resources in quantum information technology, the response of quantum linear systems to these types of input is presented. Finally, coherent feedback control of quantum linear systems is briefly introduced, and a recent experiment is used to demonstrate the effectiveness of quantum linear systems and networks theory.dback control of quantum linear systems is briefly introduced, and a recent experiment is used to demonstrate the effectiveness of quantum linear systems and networks theory.