论文标题
基于半决赛编程的自洽量子测量断层扫描
Self-consistent quantum measurement tomography based on semidefinite programming
论文作者
论文摘要
我们提出了一种基于半芬矿编程（SDP）的量子测量层析成像（QMT）的估计方法，并讨论如何使用它来检测实验性缺陷，例如射击噪声和/或在近期量子计算机上输入状态的准备状态有缺陷。此外，如果我们旨在表征的积极操作员值措施（POVM）在信息上是完整的，我们提出了一种自洽断层扫描的方法，即恢复一组输入状态和POVM效应，与实验结果一致，并且不假定对层析造影的输入状态的任何先验知识。与文献中已经讨论过的许多方法相反，我们的方法不依赖于其他假设，例如低噪声或可靠的输入状态子集的存在。
We propose an estimation method for quantum measurement tomography (QMT) based on semidefinite programming (SDP), and discuss how it may be employed to detect experimental imperfections, such as shot noise and/or faulty preparation of the input states on near-term quantum computers. Moreover, if the positive operator-valued measure (POVM) we aim to characterize is informationally complete, we put forward a method for self-consistent tomography, i.e., for recovering a set of input states and POVM effects that is consistent with the experimental outcomes and does not assume any a priori knowledge about the input states of the tomography. Contrary to many methods that have been discussed in the literature, our approach does not rely on additional assumptions such as low noise or the existence of a reliable subset of input states.
