The award was given by the IEEE Chicago Section Award committee in recognition and appreciation of Shaydulin’s “valued services and work in Quantum Algorithm Development.”
Shaydulin received his Ph.D. in computer science from Clemson University in 2020 and B.S. in applied mathematics and physics from Moscow Institute of Physics and Technology in 2016. His research centers on applying quantum computers to solve problems in optimization and machine learning. He was a research aide at Argonne in 2018 and 2019 and a research intern at IBM Research and NASA Ames Research Center in 2020. He joined Argonne in the fall of 2020 as an Argonne scholar, where he has focused on unlocking the potential of near-term quantum computers.
Among his achievements, Shaydulin led the development of decomposition-based methods that increase the size – by multiple orders of magnitude – of the problems that can be solved by using quantum computers. He also led the development of a novel connection between the symmetry properties of the optimization problem and the quantum dynamics of the leading quantum approximate optimization algorithm (QAOA). This connection can be used to predict QAOA performance, simplifying the task of selecting problem instances with the highest potential for quantum advantage.
Shaydulin’s service to the scientific community is also exemplary. During the past year, for example, he was a session chair at IEEE Quantum Week, co-organized a minisymposium at the SIAM Conference on Optimization, and co-organized a workshop at the Chicago Quantum Exchange.
“I am honored to receive this IEEE Young Engineer Award for my work in quantum computing,” said Shaydulin. “The potential of quantum computers is exciting, and I continue to be fascinated by the challenges and opportunities it presents in advancing scientific discovery.”
The award will be formally announced by IEEE on Nov. 9 during the IEEE Chicago Section’s monthly ExCom meeting
For further information about Shaydulin’s work see his website. For a look at his papers, including the QAOAKit repository he and his colleagues have developed for tackling open problems in quantum optimization, see the listing under Google Scholar.