Quantum-inspired and Quantum Optimization on a Superconducting Quantum Processor
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 quan...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/151493 |
| _version_ | 1826190908088909824 |
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| author | Banner, William P. |
| author2 | Oliver, William D. |
| author_facet | Oliver, William D. Banner, William P. |
| author_sort | Banner, William P. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:47:08Z |
| format | Thesis |
| id | mit-1721.1/151493 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T08:47:08Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1514932023-08-01T04:17:13Z Quantum-inspired and Quantum Optimization on a Superconducting Quantum Processor Banner, William P. Oliver, William D. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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. 2023-07-31T19:44:08Z 2023-07-31T19:44:08Z 2023-06 2023-07-13T14:16:00.923Z Thesis https://hdl.handle.net/1721.1/151493 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Banner, William P. Quantum-inspired and Quantum Optimization on a Superconducting Quantum Processor |
| title | Quantum-inspired and Quantum Optimization on a Superconducting Quantum Processor |
| title_full | Quantum-inspired and Quantum Optimization on a Superconducting Quantum Processor |
| title_fullStr | Quantum-inspired and Quantum Optimization on a Superconducting Quantum Processor |
| title_full_unstemmed | Quantum-inspired and Quantum Optimization on a Superconducting Quantum Processor |
| title_short | Quantum-inspired and Quantum Optimization on a Superconducting Quantum Processor |
| title_sort | quantum inspired and quantum optimization on a superconducting quantum processor |
| url | https://hdl.handle.net/1721.1/151493 |
| work_keys_str_mv | AT bannerwilliamp quantuminspiredandquantumoptimizationonasuperconductingquantumprocessor |