# Daniel Hothem

Postdoctoral Appointee, Quantum Information Science

## Postdoctoral Appointee, Quantum Information Science

## Biography

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.

## Education

Ph.D. | University of Oregon | 2021 | |

M.S. | University of Oregon | 2018 | |

B.A. & B.S. (Double) | History & Mathematics | University of Virginia | 2015 |

## Publications

**Daniel Hothem,**Ojas Parekh, Kevin Thompson, “Improved approximations for extremal eigenvalues of sparse Hamiltonians,”*Proceedings of the 18*. (2023). Listen to my talk!^{th}Conference on the Theory of Quantum Computation, Communication, and Cryptography (TQC 2023)**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)