论文标题
简单，可靠和降噪的连续变化量子层析成像和凸优化
Simple, reliable and noise-resilient continuous-variable quantum state tomography with convex optimization
论文作者
论文摘要
从测量数据（通常称为量子状态断层扫描的过程）中对未知量子状态的精确重建是量子信息处理技术开发的关键组成部分。多年来，已经提出了许多不同的断层扫描方法。最大似然估计是一个突出的例子，是长期以来最受欢迎的方法。最近，更先进的神经网络方法已经开始出现。在这里，我们回到基础知识，并为连续变量状态重建提供了一种方法，该方法在概念和实际上是基于凸优化的方法。凸优化已用于过程断层扫描和Qubit状态层析成像，但似乎被忽略了连续的可变量子状态断层扫描。我们证明了与热噪声和不完美检测的数据，对同性恋和异差测量损坏的数据表明了基本状态的高保真重建。与其他方法相比，一个主要优势是凸优化算法可以确保收敛到最佳解决方案。
Precise reconstruction of unknown quantum states from measurement data, a process commonly called quantum state tomography, is a crucial component in the development of quantum information processing technologies. Many different tomography methods have been proposed over the years. Maximum likelihood estimation is a prominent example, being the most popular method for a long period of time. Recently, more advanced neural network methods have started to emerge. Here, we go back to basics and present a method for continuous variable state reconstruction that is both conceptually and practically simple, based on convex optimization. Convex optimization has been used for process tomography and qubit state tomography, but seems to have been overlooked for continuous variable quantum state tomography. We demonstrate high-fidelity reconstruction of an underlying state from data corrupted by thermal noise and imperfect detection, for both homodyne and heterodyne measurements. A major advantage over other methods is that convex optimization algorithms are guaranteed to converge to the optimal solution.
