科学研究
学术报告
当前位置: 学院主页 > 科学研究 > 学术报告 > 正文

Quantum Uncertainty Principles for Measurements with Interventions

发布时间:2023-06-09 作者: 浏览次数:
Speaker: 肖运龙 DateTime: 6月12日(周一) 11:00-12:00
Brief Introduction to Speaker:

Yunlong Xiao(肖运龙) holds a Ph.D. in Mathematical Physics from the Max Planck Institute for Mathematics in the Sciences (MiS) in Leipzig, Germany, where he conducted his research under the guidance of Prof. Jürgen Jost and Prof. Naihuan Jing, successfully completing his degree in February 2017. He also obtained a second Ph.D. in Pure Mathematics from South China University of Technology, working under the supervision of Prof. Naihuan Jing, and graduated in June 2017. Following his academic pursuits, he served as a Postdoctoral Fellow at the Institute for Quantum Science and Technology at the University of Calgary, Canada, working alongside Prof. Barry C. Sanders and Prof. Gilad Gour until August 2019. From 2020 to 2022, he worked as a Research Fellow at the Quantum Hub in the School of Physical and Mathematical Sciences at Nanyang Technological University, Singapore, under the mentorship of Prof. Mile Gu. Currently, Yunlong holds the position of Scientist II at the Institute of High Performance Computing (IHPC), The Agency for Science, Technology, and Research (A*STAR), Singapore. His research interests lie at the intersection of quantum foundation, quantum causal inference, quantum resource theory, and quantum communication.

Place: 6209
Abstract:Heisenberg's uncertainty principle traditionally applies to passive measurements, where a system evolves freely before being observed. However, our understanding of the world is shaped through interactive experiences from an early age. Even toddlers instinctively engage in actions, observe the resulting reactions, and adapt their future actions based on those observations. This raises intriguing questions about the intersection of quantum theory and interactive measurements: How do quantum phenomena manifest in interactive scenarios, and what is their relationship with causal inference? We are excited to share that our recent work delves into these questions and provides insightful answers. We invite you to explore our findings and expand your understanding of this fascinating field.