Department of Physics & Astronomy
University of New Mexico

Thesis and Dissertation Defenses

Learning, Optimizing, and Simulating Fermions with Quantum Computers

Presented by Andrew Zhao

Fermions are fundamental particles which obey seemingly bizarre quantum-mechanical principles, yet constitute all the ordinary matter that we inhabit. As such, their study is heavily motivated from both fundamental and practical incentives. In this talk, I will explore how the tools of quantum information can assist us on both of these fronts. I will primarily do so through the task of partial quantum state learning, a critical bottleneck in quantum simulation algorithms, particularly for currently available, imperfect quantum machines. As a consequence of developing fast learning protocols, I also articulate how the nature of fermions requires special consideration (in contrast to other types of particles). Finally, I will show how fermions appear in unexpected computational tasks by drawing surprising connections to a hard classical optimization problem. This insight leads to new avenues for tackling the classical problem through the established toolbox for fermionic simulation.

3:30 pm, Thursday, November 30, 2023

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