报告题目：A Separability-Entanglement Classifier via Machine Learning

报告人：曾蓓副教授，University of Guelph，麻省理工大学博士。

报告时间：2017.7.2.上午9:00

报告地点：量子楼410

报告摘要：The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing, which is known to be an NP-hard problem in general. Despite the proposed many methods – such as the positive partial transpose (PPT) criterion and the $k$-symmetric extendibility criterion – to tackle this problem in practice, none of them enables a general, effective solution to the problem even for small dimensions. Explicitly, separable states form a high-dimensional convex set, which exhibits a vastly complicated structure. In this work, we build a new separability-entanglement classifier underpinned by machine learning techniques. Our method outperforms the existing methods in generic cases in terms of both speed and accuracy, opening up the avenues to explore quantum entanglement via the machine learning approach.

报告人简介：曾蓓，女，汉族。1998-2002在清华大学基础科学班学习，取得数学和物理学学士学位。2004年取得清华大学理论物理学硕士学位。2004-2009年在麻省理工学院物理系学习，师从Isaac Chuang教授，取得博士学位。2009-2010在加拿大Waterloo大学量子计算研究所从事博士后研究。2010年加入加拿大Geulph大学数学与统计系，任助理教授。2014年起任副教授。