论文标题
量子信息处理的因素图
Factor Graphs for Quantum Information Processing
论文作者
论文摘要
[...]在本文中，我们有兴趣概括因子图和描述量子系统的相关方法。研究了经典图形模型的两个概括，即双边因子图（DEFG）和量子因子图（QFGS）。通常，因子图中的因子代表非负实值的局部函数。在经典因子图中概括因素的两种不同的方法分别产生DEFG和QFG。我们提出了/重新提供和分析了DEFG/QFGS的信念传播算法的广义版本。作为DEFG的特定应用，我们研究了与量子通道相对于内存的量子通道的信息速率及其上/下限。在这项研究中，我们还提出了一种数据驱动的方法，以优化信息速率上限/下限。
[...] In this thesis, we are interested in generalizing factor graphs and the relevant methods toward describing quantum systems. Two generalizations of classical graphical models are investigated, namely double-edge factor graphs (DeFGs) and quantum factor graphs (QFGs). Conventionally, a factor in a factor graph represents a nonnegative real-valued local functions. Two different approaches to generalize factors in classical factor graphs yield DeFGs and QFGs, respectively. We proposed/re-proposed and analyzed generalized versions of belief-propagation algorithms for DeFGs/QFGs. As a particular application of the DeFGs, we investigate the information rate and their upper/lower bounds of classical communications over quantum channels with memory. In this study, we also propose a data-driven method for optimizing the upper/lower bounds on information rate.
