Arif Ullah学术报告

发布时间:2022-11-18

报告题目:Quantum dissipative dynamics of open quantum systems: From the lens of theoretical and computational approaches

主 讲 人:Arif Ullah (厦门大学)

    间:2022112215:00

腾讯会议:353-662-760

会议链接https://meeting.tencent.com/dm/q22fIpqMgOzm

主办单位:物理与光电工程学院

欢迎各位老师、同学届时前往!

报告摘要:

In the real world, considering isolated systems is an idealistic approximation with many limitations, thus incorporating the effects of the environment is important for the exact characterization of a system. Including environmental degrees of freedom in the system dynamics has great impact on the system properties because of the exchange of particles, energy and phase between the two. Because of the very large size of the environment (~ particles), it is not feasible to deal with all degrees of freedom, thus approximations are made. In the last three decades, a great progress has been made in the development of theoretical approaches for these systems. In my talk, I will introduce you to both approximate and numerically exact approaches that are developed so far, with the main focus on stochastic equation of motion approach. As an application of these methods, we will consider the relaxation of a two-level system linearly coupled to a harmonic environment and the excitation energy transfer in the natural light harvesting systems. Besides these traditional methods, I will also talk about our recently developed machine learning-based quantum dynamics approaches.

报告人简介:

Arif Ullah, born in 1989 has completed his Bachelor and Master degrees in Physics from the well-renowned Quaid-I-Azam University, Islamabad, Pakistan. In 2015, he joined Prof. YiJing Yan group at the University of Science and Technology of China and completed his PhD degree in 2019. In 2020, he went to Shanghai NewYork University for one-year Postdoc and later in 2021, he joined Prof. Pavlo O. Dral group at Xiamen University. His research interests are: The development of theoretical approaches for quantum dissipative systems, quantum computing, machine learning-based quantum dynamics approaches and the theoretical study of natural and artificial light harvesting devices.


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