Date: 29th September 2022, 10:00am

Speaker: Haixing Miao (缪海兴)

Affiliation: Tsinghua University

Tencent/VooV Meeting ID: 973-6909-6865

Online Meeting Link:

Introduction of Speaker:

Haixing, Miao. After graduation from the University of Science and Technology, Miao Haixing furthered his study in the University of Western Australia and got his doctoral degree in 2010. After graduation, he worked as a postdoctoral fellow in California Institute of Technology from 2010 to 2013 and became a Marie-Curie Fellow working in the University of Birmingham in 2014. Two years later, he was awarded Ernest Rutherford Fellowship and Birmingham Fellowship and started to give lectures at the University of Birmingham. He was the chair of the LIGO Scientific Collaboration Quantum Noise Working Group from 2020 to 2021, at which year he has returned to China and is now an associate professor at the Department of Physics, Tsinghua University. Miao Haixing’s research focuses on gravitational-wave detection and high-precision quantum measurements.


Gravitational wave has opened an entirely new window on the Universe. For example, we have tested Einstein's general theory of relativity under strong gravitational fields through the detection of gravitational waves from a binary black hole merger and embraced a new era of multi-messenger astronomy thanks to the joint gravitational and electromagnetic observation of the binary neutron star. The ground-based gravitational wave detectors LIGO, VIRGO and KAGRA are large, kilometer-scale laser interferometers, which are sufficiently sensitive to detect weak gravitational-wave signals. Despite their large size, they also exhibit a quantum effect, which is usually significant in the micro-world, and quantum noise caused by quantum fluctuations of light has become one of the main constraints for the sensitivity of detectors. During the seminar, different methods to reduce quantum noise and the corresponding quantum limit will be introduced. With an understanding of the fundamental quantum limit, we can integrate different noise reduction methods into a unified framework.