Date of Award
Doctor of Philosophy
Energy Science and Engineering
Travis S. Humble
Cristian D. Batista, Raphael C. Pooser, James Ostrowski
The modeling and prediction of quantum mechanical phenomena is key to the continued development of chemical, material, and information sciences. However, classical computers are fundamentally limited in their ability to model most quantum effects. An alternative route is through quantum simulation, where a programmable quantum device is used to emulate the phenomena of an otherwise distinct physical system. Unfortunately, there are a number of challenges preventing the widespread application of quantum simulation arising from the imperfect construction and operation of quantum simulators. Mitigating or eliminating deleterious effects is critical for using quantum simulation for scientific discovery. This dissertation develops strategies for implementing quantum simulation and simultaneously mitigating error through the use of device control and calibration. First, an example of the benefits of calibration and control on simulator performance is provided through a case study on simulating the classical Shastry-Sutherland Ising model using quantum annealing. Motivated by the increased precision and accuracy provided by such strategies, a paradigm for parameterized Hamiltonian simulation using quantum optimal control is proposed and validated through numerical simulation. Finally, we apply the methods developed to demonstrate the feasibility of using optimal control for simulation of exotic, dynamical quantum phenomena. Specifically, we demonstrate that quantum optimal control can realize the quantum simulation of string order melting in superconducting quantum devices. These results affirm the utility of quantum optimal control methods for quantum simulation tasks and establish new opportunities for applications of quantum computing to the study of phenomena in quantum physics.
Kairys, Paul M., "Control and calibration strategies for quantum simulation. " PhD diss., University of Tennessee, 2022.