Quantum computing has the potential to provide speedups over classical state-of-the-art for some combinatorial optimization problems. Recent advances in both hardware and algorithm development have made it possible to solve small problems on modern quantum computers. Combinatorial optimization problems (especially NP-hard problems) are of particular interest, since for many of these problems best classical algorithms cannot provide solutions of sufficient quality in reasonable time. In this talk, I will provide an overview of our efforts on improving the performance of Quantum Approximate Optimization Algorithm (QAOA) and on applying QAOA to problems of practical size using problem-decomposition schemes. I will discuss the potential for quantum advantage with QAOA on graph problems, as well as the limitations of the state-of-the-art approaches.
Ruslan Shaydulin is a PhD candidate in Computer Science at Clemson University working under the supervision of Dr. Ilya Safro, with additional advising from Dr. Yuri Alexeev (Argonne National Laboratory). He received his B.S. in Applied Mathematics and Physics from Moscow Institute of Physics and Technology in 2016. His research interests center around hybrid quantum-classical schemes that incorporate noisy intermediate-scale quantum (NISQ) co-processors to solve problems of practical size. He has been working in close collaboration with Argonne and Los Alamos National Laboratories since 2018. He is an organizer of multiple quantum computing tutorials, including a number of quantum computing training sessions at Argonne and a mini-tutorial at SIAM PP20.
Host: Pavel Lougovski, firstname.lastname@example.org
Last Updated: May 28, 2020 - 4:06 pm