当前位置:首页>学术报告
Detecting Steerability Bounds of Generalized Werner States via a Backpropagation Neural Networ

 
Title:
Detecting Steerability Bounds of Generalized Werner States via a Backpropagation Neural Networ
Speaker:
贺衎 教授,太原理工大学数学学院
Inviter: 骆顺龙 研究员
Time & Venue:

2022.7.15 14:30 腾讯会议号:233-214-570

Abstract:

We use an error backpropagation (BP) neural network to determine whether an arbitrary two-qubit quantum state is steerable and optimize the steerability bounds of the generalized Werner state. Results show that, regardless of how we select the features for the quantum states, we can use the BP neural network to construct several models to obtain high-performance quantum steering classifiers compared with the support vector machine. Moreover, we predict the steerability bounds of the generalized Werner states using the classifiers that are newly constructed by the BP neural network; that is, the predicted steerability bounds are closer to the theoretical bounds. In particular, high-performance quantum steering classifiers with partial information about the quantum states that we need to measure in only three fixed measurement directions are obtained.

Affiliation:  

学术报告中国科学院数学与系统科学研究院应用数学研究所
地址 北京市海淀区中关村东路55号 思源楼6-7层 南楼5-6、8层 100190
@2000-2022 京ICP备05058656号-1