Quantum analogues of classical optimization algorithms.
| dc.contributor.advisor | Konrad, Thomas. | |
| dc.contributor.advisor | Tame, Mark Simon. | |
| dc.contributor.author | Mpofu, Kelvin Tafadzwa. | |
| dc.date.accessioned | 2018-10-23T08:12:21Z | |
| dc.date.available | 2018-10-23T08:12:21Z | |
| dc.date.created | 2017 | |
| dc.date.issued | 2017 | |
| dc.description | Master of Science in Physics. University of KwaZulu-Natal, Durban 2017. | en_US |
| dc.description.abstract | This thesis explores the quantum analogues of algorithms used in mathematical optimization. The thesis focuses primarily on the iterative gradient search algorithm (algorithm for finding the minimum or maximum of a function) and the Newton-Raphson algorithm. The thesis introduces a new quantum gradient algorithm suggested by Professor Thomas Konrad and colleagues and a quantum analogue of the Newton-Raphson Method, a method for finding approximations to the roots or zeroes of a real-valued function. The quantum gradient algorithm and the quantum Newton-Raphson are shown to give a polynomial speed up over their classical analogues. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10413/15712 | |
| dc.language.iso | en_ZA | en_US |
| dc.subject.other | Quantum optimization. | en_US |
| dc.subject.other | Quantum algorithms. | en_US |
| dc.title | Quantum analogues of classical optimization algorithms. | en_US |
| dc.type | Thesis | en_US |