Practical Bayesian tomography
Practical Bayesian tomography
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DOI：
10.1088/1367-2630/18/3/033024
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发表时间：
2016-03-15
影响因子：
3.3
通讯作者：
Cory, D. G.
中科院分区：
文献类型：
--
作者：
Granade, Christopher;Combes, Joshua;Cory, D. G.
近年来，贝叶斯方法已被提出作为解决量子态和过程层析成像中的广泛问题的解决方案。国家的最先进的贝叶斯层析成像解决方案遭受三个问题：数值棘手，缺乏信息先验分布，以及无法跟踪时间依赖的过程。在这里，我们解决所有三个问题。首先，我们使用现代统计方法，由Huszar和Houlsby（2012 Phys. Rev. A 85 052120）和Ferrie（2014 New J. Phys. 16 093035）开创，使贝叶斯层析成像在数值上易于处理。我们的方法允许量子态和通道的贝叶斯点和区域估计的实际计算。其次，我们提出了第一个先验的量子态和通道，允许包括有用的实验见解。最后，我们开发了一种方法，允许跟踪的时间依赖的状态和估计的漂移和扩散过程影响的状态。我们为我们的方法提供了源代码和动画可视化示例。
In recent years, Bayesian methods have been proposed as a solution to a wide range of issues in quantum state and process tomography. State-of-the-art Bayesian tomography solutions suffer from three problems: numerical intractability, a lack of informative prior distributions, and an inability to track time-dependent processes. Here, we address all three problems. First, we use modern statistical methods, as pioneered by Huszar and Houlsby (2012 Phys. Rev. A 85 052120) and by Ferrie (2014 New J. Phys. 16 093035), to make Bayesian tomography numerically tractable. Our approach allows for practical computation of Bayesian point and region estimators for quantum states and channels. Second, we propose the first priors on quantum states and channels that allow for including useful experimental insight. Finally, we develop a method that allows tracking of time-dependent states and estimates the drift and diffusion processes affecting a state. We provide source code and animated visual examples for our methods.