报告题目：Einstein-Podolsky-Rosen steering based on semisupervised machine learning
报告人：集美大学陈芝花教授
报告时间：2021年12月24日（周五）10：00 - 11：00
报告地点：腾讯会议814391297
报告摘要：Einstein-Podolsky-Rosen(EPR)steering is a kind of powerful nonlocal quantum resource in quantum information processing such as quantum cryptography and quantum communication. Many criteria have been proposed in the past few years to detect the steerability both analytically and numerically. Supervised machine learning such as support vector machines and neural networks have also been trained to detect the EPR steerability. To implement supervised machine learning, one needs a lot of labeled quantum states by using the semidefinite programming, which is very time consuming. We present a semi-supervised support vector machine method which only uses a small portion of labeled quantum states in detecting quantum steering. We show that our approach can significantly improve the accuracies by detailed examples.
报告人简介：陈芝花，女，2009年3月毕业于浙江大学计算数学专业，同年进入浙江工业大学工作，2013年4月至2014年4月到新加坡国立大学量子技术中心访学一年，于2019年6月进入集美大学理学院工作，并聘为教授。主要从事量子信息主要是量子关联等方面的工作。以第一作者或主要作者在《Physical Reivew Letters》、《Physical Reivew A》等物理学权威期刊发表论文多篇，主持国家自然科学基金年项目3项。
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