{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/151493"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/151493","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Quantum-inspired and Quantum Optimization on a Superconducting Quantum Processor","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_html":"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_has_math":false,"creators":["Banner, William P."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Quantum-inspired and Quantum Optimization on a Superconducting Quantum Processor"]}]}],"canonical_facts":{"dc:contributor.advisor":["Oliver, William D."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Banner, William P."],"dc:date.accessioned":["2023-07-31T19:44:08Z"],"dc:date.available":["2023-07-31T19:44:08Z"],"dc:date.issued":["2023-06"],"dc:description.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. 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