Quantum-inspired and Quantum Optimization on a Superconducting Quantum Processor
Name
banner-wbanner-sm-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
3.05 MB
Format
Adobe PDF
Checksum (MD5)
290aa75a3ffcd48f29b3be1241ce5445
Author(s)
Banner, William P.
Advisor(s)
Oliver, William D.
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
Abstract
Quantum and Quantum-inspired optimization represent rapidly growing fields that combine classical optimization techniques with either quantum-inspired ideas or quantum hardware to address complex optimization problems. This thesis provides an overview of quantum-inspired optimization as well as quantum optimization, including the theoretical underpinnings of both processes on hardware and in software. In particular, this thesis considers a specific, practically relevant problem, a BMW production planning problem, and evaluates the performance of quantum-inspired optimizers. This evaluation is implemented by comparing the performance of a family of quantum-inspired optimizers with that of several common black-box combinatorial methods. We find that the use of important operations research techniques including the incorporation of domain-specific information as well as state-space pruning improves the performance of all solvers. In addition, we find that in a majority of tested cases, quantum-inspired methods tie or improve upon the results of their conventional counterparts, albeit by small margins, particularly in regimes of moderate state-space size. This thesis demonstrates that quantum-inspired optimization can outperform many conventional optimization methods in some cases, motivating future use and study of quantum-inspired protocals as well as implementation of fully-quantum optimization techniques.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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