Honors Theses

College

College of Arts and Sciences

Department

Department of Physics and Astronomy

Degree

Bachelor of Science (B.S.)

Major

Mathematics, Physics

Document Type

Honors Thesis

Abstract

Quantum annealing (QA) is a global search-heuristic designed to solve NP-hard optimization problems. Currently, D-Wave Systems is the only commercial QA company, boasting a line of chips that have over 2000 working qubits. Unfortunately, their API lacks many useful features for exploring the actual physics and efficacy of QA as an optimization tool. Further, the typical QA routine is prone to errors for a variety of reasons: noise, problem mis-specification, minor-embedding errors, and using incorrect annealing parameters. This work attempts to alleviate both of these issues with a single Python package: dwaveutils. With it, we explore two case studies: simulations of transversefield Ising Hamiltonians and forward-reverse error-mitigation annealing. The first shows the utility that dwaveutils brings when it comes to submitting and post-processing problems on D-Wave. The second shows the power of having a numeric annealing solver that allows for exploration of modified annealing routines. With it, we’ve uncovered several potentially fruitful investigations that could reduce errors on modern devices with no cost in ancilla qubits and allow for a new feature: graph introspection. Overall, our package allows for easy exploration and improvements of modern quantum annealing.

Publication Date

4-8-2019

First Advisor

Novotny, Mark

Second Advisor

Koshka, Yaroslav

Third Advisor

Oppenheimer, Seth

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