I will introduce a tensor network-based method to compute the solution space properties of a broad class of combinatorial optimization problems. These properties include finding one of the optimum solutions, counting the number of solutions of a given size, and enumeration and sampling of solutions of a given size. Using the independent set problem as an example, I will demonstrate how the solution space properties can deepen our understanding, and help design better quantum algorithms.

Paper: arXiv: 2205.03718
Github: https://github.com/QuEraComputing/GenericTensorNetworks.jl
 

Jinguo Liu is a native Chinese. He completed his Ph.D. training in Qiang-Hua Wang's group at Nanjing University on condensed matter physics. After that, he has been a postdoc in Lei Wang's group for two years, a full-time consultant in QuEra computing for half a year, and now he is a postdoc in Mikhail Lukin's group at Harvard. His research direction is diverse, while all his works are about developing new and better algorithms for solving existing or new problems. According to reliable sources, every program he touched in the lab can speed up by more than two orders!

To request for meeting link, please write to phweb@ust.hk.