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Quantum analogues of classical optimization algorithms.

dc.contributor.advisorKonrad, Thomas.
dc.contributor.advisorTame, Mark Simon.
dc.contributor.authorMpofu, Kelvin Tafadzwa.
dc.date.accessioned2018-10-23T08:12:21Z
dc.date.available2018-10-23T08:12:21Z
dc.date.created2017
dc.date.issued2017
dc.descriptionMaster of Science in Physics. University of KwaZulu-Natal, Durban 2017.en_US
dc.description.abstractThis 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.urihttp://hdl.handle.net/10413/15712
dc.language.isoen_ZAen_US
dc.subject.otherQuantum optimization.en_US
dc.subject.otherQuantum algorithms.en_US
dc.titleQuantum analogues of classical optimization algorithms.en_US
dc.typeThesisen_US

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