Quantum Computing Techniques for Machine Learning

dc.contributor.advisor	Theis, Dirk Oliver, juhendaja
dc.contributor.author	Lei, Andrew
dc.contributor.other	Tartu Ülikool. Loodus- ja täppisteaduste valdkond	et
dc.contributor.other	Tartu Ülikool. Arvutiteaduse instituut	et
dc.date.accessioned	2023-11-08T13:04:10Z
dc.date.available	2023-11-08T13:04:10Z
dc.date.issued	2020
dc.description.abstract	Quantum computing has been shown to provide a considerable speed boost for a number of problems. One area that some have looked into is using quantum computing for machine learning. I implement several methods for encoding data on the Iris dataset and compare their performance. Variational input encoding as suggested by Theis and Vidal seem to provide an advantage for Havlicek encoding. There were no similar improvements for amplitude encoding, but this could be because of the implementation.	et
dc.identifier.uri	https://hdl.handle.net/10062/94108
dc.language.iso	eng	et
dc.publisher	Tartu Ülikool	et
dc.rights	openAccess	et
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Quantum computing	et
dc.subject	machine learning	et
dc.subject	quantum machine learning	et
dc.subject.other	magistritööd	et
dc.subject.other	informaatika	et
dc.subject.other	infotehnoloogia	et
dc.subject.other	informatics	et
dc.subject.other	infotechnology	et
dc.title	Quantum Computing Techniques for Machine Learning	et
dc.type	Thesis	et