Abstract:

As quantum computing technology develops, there is a growing interest in finding useful applications of near-term quantum machines. In this talk, I will describe our recent works on using near-term quantum devices to tackle classical combinatorial optimization problems. The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical variational algorithm designed to solve such problems. An essential but missing ingredient for understanding and deploying QAOA is a constructive approach to carry out the outer-loop classical optimization. In the first part of the talk, I will report our in-depth study of the performance of QAOA on MaxCut problems by developing an efficient parameter-optimization procedure and revealing its ability to exploit non-adiabatic operations. In the second part, I will describe and analyze an architecture for quantum optimization to solve maximum independent set (MIS) problems using neutral atom arrays trapped in optical tweezers. We show that the solutions of MIS problems can be efficiently encoded in the ground state of interacting atoms in 2D arrays by utilizing the Rydberg blockade mechanism. We benchmark the performance of leading classical algorithms and quantum algorithms, including quantum annealing and QAOA, for solving the MIS problem. 

Short Bio:

Dr. Shengtao Wang finished his PhD with Prof. Luming Duan at the university of Michigan-Ann Arbor in 2017. During his PhD, he worked on realization of topological phases in cold atomic systems, trapped ion quantum computation, and quantum supremacy. After that, he joined Mikhail Lukin’s group at Harvard as a postdoctoral scholar. He has been working on applications of near-term quantum devices, especially on exploring potential quantum advantages in solving optimization problems. His current interests include applications of near-term quantum computers, quantum many-body dynamics, and quantum machine learning.