Postdoctoral Appointee, Quantum Information Science
My research resides at the intersection of classical and quantum computing. As a member of the QPL and QuAAC, I am interested in leveraging classical tools (e.g., neural networks, statistics, classical optimization programs) to understand and solve quantum problems. Most of my work focuses on benchmarking and characterizing contemporary quantum processors, although I also spend time designing classical algorithms to probe quantum mechanical systems.
|Ph.D.||University of Oregon||2021|
|M.S.||University of Oregon||2018|
|B.A. & B.S. (Double)||History & Mathematics||University of Virginia||2015|
- Daniel Hothem, Ojas Parekh, Kevin Thompson, “Improved approximations for extremal eigenvalues of sparse Hamiltonians,” Proceedings of the 18th Conference on the Theory of Quantum Computation, Communication, and Cryptography (TQC 2023). (2023). Listen to my talk!
- Daniel Hothem, Kevin Young, Tommie Catanach, Timothy Proctor, “Learning a quantum computer’s capability.” arXiv:2304.10650 (2023)
- Daniel Hothem, Jordan Hines, Karthik Nataraj, Robin Blume-Kohout, Timothy Proctor, “Predictive models from quantum computer benchmarks,” arXiv:2305.08796 (2023)