Degree

Bachelor of Science (B.S.)

Major(s)

Mathematics; Physics

Document Type

Immediate Open Access

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.

DOI

https://doi.org/10.54718/EOLF7980

Date Defended

4-8-2019

Thesis Director

Novotny, Mark

Second Committee Member

Koshka, Yaroslav

Third Committee Member

Oppenheimer, Seth

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