报告人：曾蓓教授，香港科技大学

邀请人：王健教授

时 间： 6月6日 上午10:30至11:30，

地 点：致原楼1007室

摘要：

Quantum computers offer unprecedented capabilities for solving complex problems, positioning them as invaluable tools across diverse domains. A notable extension of classical neural networks, quantum neural networks (QNNs), leverages the unique features of quantum computers to push the boundaries of existing knowledge. This talk delves into recent progress in employing QNNs for addressing challenging problems within quantum information theory, such as detecting quantum entanglement, devising quantum query algorithms, identifying quantum error-correcting codes, and executing quantum state tomography. Furthermore, we will explore the potential of QNNs in enhancing the applicability of generative models, such as the Generative Pre-trained Transformer (GPT), for the analysis of quantum correlations. This presentation highlights the potential of QNNs as a promising direction for future research in quantum information theory.

报告人简介：

曾蓓教授，本科和硕士毕业于清华大学物理系，2009年博士毕业于麻省理工学院。2010年起在加拿大圭尔夫大学任教，2019年加入香港科技大学，担任香港科技大学量子科技研究中心主任，2021年入选美国物理学会会士（APS Fellow）。研究领域包括量子信息、量子计算和量子纠错。

欢迎各位老师和同学参加!