2026-09-19T17:33:15Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1514932023-08-01T04:17:13Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Oliver, William D.
Banner, William P.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-07-31T19:44:08Z
2023-07-31T19:44:08Z
2023-06
2023-07-13T14:16:00.923Z
https://hdl.handle.net/1721.1/151493
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.
S.M.
Massachusetts Institute of Technology
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Quantum-inspired and Quantum Optimization on a Superconducting Quantum Processor
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<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.</Abstract>
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