Skip to content Skip to main navigation Report an accessibility issue

Quantum Algorithm Theory and Discrete Optimization Group (QAT & DOG)


Quantum computers have the potential to revolutionize how complex problems are solved. The Quantum Algorithm Theory and Discrete Optimization Group (QAT & DOG) at the University of Tennessee studies fundamental quantum algorithm design and applications of these algorithms to discrete optimization problems.

News news

  • Kazeem Kosebinu has been accepted to the Gene Golub SIAM Summer School at Lehigh University and to the USQIS Summer School at Fermi National Ac- celerator Laboratory.
  • Anthony Wilkie has been accepted to the Los Alamos National Laboratory Quantum Summer School.
  • Dr. Rebekah Herrman has been selected as a Visiting Research Fellow at Phase- craft, Inc. for the summer of 2023.
  • Dr. Rebekah Herrman has been selected as a 2022 DARPA FORWARD Riser.

Affiliated faculty

  • Dr. Rebekah Herrman
  • Dr. Jim Ostrowski
  • Dr. Bing Yao

Current students and postdoctoral researchers

  • Marzieh Bakhshi
  • Dr. Igor Gaidai
  • Kazeem Kosebinu
  • Moises Ponce
  • Shamim Riten (incoming Fall 2023)
  • Anthony Wilkie

Funded Projects

  • AI TENNessee Initiative – Seed Funds for AI Research and Teambuilding: Developing ML/AI-based Tools to Analyze the Multi-dimensional Spectroscopic Data of Scanning Tunneling Microscopy (May 2023 – present)
  • NSF REU Site- Quantum algorithms and optimization (February 2023-present)
  • NSF CISE SMALL- Optimizing Quantum Circuit Design (October 2022-present)
  • DARPA ONISQ- Problem Structure and the Quantum Advantage: Theory and Algorithms (November 2019-present)

Sample publications

  • Herrman, R., Lotshaw, P. C., Ostrowski, J., Humble, T. S., & Siopsis, G. (2022). Multi-angle quantum approximate optimization algorithm. Scientific Reports, 12(1), 6781.
  • Herrman, R., Treffert, L., Ostrowski, J., Lotshaw, P. C., Humble, T. S., & Siopsis, G. (2021). Globally optimizing QAOA circuit depth for constrained optimization problems. Algorithms, 14(10), 294.
  • Xie, J., & Yao, B. (2023). Physics-constrained deep learning for robust inverse ecg modeling. IEEE Transactions on Automation Science and Engineering, 20(1), 151.
  • Li, H., Yao, B., Tu, T., & Guo, G. (2012). Quantum computation on gate- defined semiconductor quantum dots. Chinese Science Bulletin, 57, 1919.