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學術報告“Tensor network based machine learning of non-Markovian quantum processes”
2020-12-16

來源:物理系  供稿:      點擊次數:  字號:【  

時間:20201216日下午1500-1600

地點:校本部實驗樓715會議室

主講人:郭楚博士 (信息工程大學)

主講人簡介:郭楚博士于20179月獲得新加坡科技設計大學物理學博士學位,現為信息工程大學助理教授。主要研究方向為量子多體物理、量子計算以及量子機器學習。郭楚博士在張量網絡算法方面有多年工作經驗,獨立開發了整套基于張量網絡算法的數值計算工具包,用于求解量子多體物理,量子計算等領域的問題,已在美國物理評論(Phys. Rev.)系列期刊發表論文20余篇, 其中以第一作者或通訊作者發表15篇,包括PRL一篇。

主講內容簡介: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.