论文标题
线性高斯量子状态平滑：了解爱丽丝估计鲍勃状态的最佳解散
Linear Gaussian Quantum State Smoothing: Understanding the optimal unravelings for Alice to estimate Bob's state
论文作者
论文摘要
量子状态平滑是一种在特定时间构建量子状态的估计的技术，其条件是在此之前和之后的测量记录下进行。该技术假设观察者，爱丽丝，监视量子系统环境的一部分，而其余的环境则是爱丽丝未观察到的，它是由次要观察者鲍勃（Bob）衡量的，后者可能会选择其监视方式。鲍勃的测量选择对爱丽丝平滑有效性的影响已经在许多最近的论文中进行了研究。在这里，我们扩展了引入线性高斯量子（LGQ）状态平滑的字母[Phys。 Rev. Lett。，122，190402（2019）]。在当前的论文中，我们提供了LGQ平滑方程式的更详细的推导，并解决了有关Bob的最佳测量策略的开放问题。具体而言，我们开发了一个简单的假设，鉴于爱丽丝的测量选择，它可以近似Bob的最佳测量选择。通过“最佳选择”，我们的意思是BOB的选择将最大程度地提高Alice平滑状态的纯度，而其过滤状态（仅基于Alice的过去测量记录）。鲍勃应该选择自己的测量值，以便他从爱丽丝的测量中观察到系统的反作用，这一假设似乎与一个人关于量子状态平滑的直觉背道而驰。尽管如此，我们证明它甚至超出了线性高斯环境。
Quantum state smoothing is a technique to construct an estimate of the quantum state at a particular time, conditioned on a measurement record from both before and after that time. The technique assumes that an observer, Alice, monitors part of the environment of a quantum system and that the remaining part of the environment, unobserved by Alice, is measured by a secondary observer, Bob, who may have a choice in how he monitors it. The effect of Bob's measurement choice on the effectiveness of Alice's smoothing has been studied in a number of recent papers. Here we expand upon the Letter which introduced linear Gaussian quantum (LGQ) state smoothing [Phys. Rev. Lett., 122, 190402 (2019)]. In the current paper we provide a more detailed derivation of the LGQ smoothing equations and address an open question about Bob's optimal measurement strategy. Specifically, we develop a simple hypothesis that allows one to approximate the optimal measurement choice for Bob given Alice's measurement choice. By 'optimal choice' we mean the choice for Bob that will maximize the purity improvement of Alice's smoothed state compared to her filtered state (an estimated state based only on Alice's past measurement record). The hypothesis, that Bob should choose his measurement so that he observes the back-action on the system from Alice's measurement, seems contrary to one's intuition about quantum state smoothing. Nevertheless we show that it works even beyond a linear Gaussian setting.
