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                学术报告“Tensor network based machine learning of non-Markovian quantum processes”

                来源:物理系  供稿:      点击次数:  字号:【  



                主讲人:郭楚博士 (信息工程大学)

                主讲人简介:郭楚博士于20179月获得新加坡科技设计大学物理学博士学位,现为信息工●程大学助理教授。主要研究方向为量子多体物理、量子计算以及量子机器学习。郭楚博士在张量网络算法方面有多年工作经验,独立开发了整套基于张量网络算法的数值计算工具包,用于求解量子多体物理,量子计算等领域的问题,已在美国物理评论(Phys. Rev.)系列期刊发表论文20余篇, 其中以第一作者或通讯作者发表15篇,包括PRL一篇。

                主讲内容简介ζ :We show how to learn structures of generic, non-Markovian, quantum stochastic processes using a tensor network based machine learning algorithm. We do this by representing the process as a matrix product operator (MPO) and train it with a database of local input states at different times and the corresponding time-nonlocal output state. In particular, we analyze a qubit coupled to an environment and predict output state of the system at different time, as well as reconstruct the full system process. We show how the bond dimension of the MPO, a measure of non-Markovianity, depends on the properties of the system, of the environment and of their interaction. Hence, this study opens the way to a possible experimental investigation into the process tensor and its properties.