Abstract: This talk presents some recent progress in using randomness in the design of efficient quantum algorithms. First, we consider new randomized algorithms for the robust quantum phase estimation problem. Next, we discuss its applications in Heisenberg-limited Hamiltonian learning for interacting bosons and Fermi-Hubbard models.

About the speaker:  

Lexing Ying is a professor of mathematics at Stanford University. He received B.S. from Shanghai Jiaotong University in 1998 and Ph.D. from New York University in 2004. Before joining Stanford in 2012, he was a post-doc at Caltech and a professor at UT Austin. He received a Sloan Fellowship in 2007, an NSF Career Award in 2009, the Fengkang Prize in 2011, and the James H. Wilkinson Prize in 2013. He is an invited speaker of ICM 2022.