abstract:The recent breakthroughs in quantum computers have shown quantum advantage (aka quantum supremacy), i.e., quantum computers outperform classic computers for solving a specific problem. These problems are highly physics-oriented.  A more relevant fact is that there are already general-purpose programmable quantum computing devices available to the public.  A natural question for statisticians is whether these computers will benefit statisticians in solving some statistics or data science problems. If the answer is yes, what kind of statistics problems should statisticians resort to quantum computers? Unfortunately, the general answer to this question remains elusive.

In this talk, I will present challenges and opportunities for developing quantum algorithms. I will introduce a novel quantum algorithm for a statistical problem and demonstrate that the intersection of statistical computing and quantum computing is an exciting and promising research area. The development of quantum algorithms for statistical problems will not only advance the field of quantum computing but also provide new tools and insights for solving challenging statistical problems.



Bio:Professor Ping Ma is a Distinguished Research Professor at the University of Georgia and co-directs the big data analytics lab.  He was a Beckman Fellow at the Center for Advanced Study at the University of Illinois at Urbana-Champaign, a Faculty Fellow at the US National Center for Supercomputing Applications, and a recipient of the National Science Foundation CAREER Award. His paper won the best paper award from the Canadian Journal of Statistics in 2011. He delivered the 2021 National Science Foundation Distinguished Lecture. Professor Ma serves on multiple editorial boards.  He is a Fellow of the American Association for the Advancement of Science and the American Statistical Association.