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dc.contributor.authorPeters, Evan
dc.date.accessioned2020-05-29 20:59:42 (GMT)
dc.date.available2021-05-30 04:50:08 (GMT)
dc.date.issued2020-05-29
dc.date.submitted2020-05-28
dc.identifier.urihttp://hdl.handle.net/10012/15962
dc.description.abstractThe widespread benefits of classical machine learning along with promised speedups by quantum algorithms over their best performing classical counterparts have motivated development of quantum machine learning algorithms that combine these two approaches. Quantum Kernel Methods (QKMs) [22, 49] describe one such combination, which seeks to leverage the high dimensional Hilbert space over quantum states to perform classification on encoded classical data. In this work I present an analysis of QKM algorithms used to encode and classify real data using a quantum processor, aided by a suite of custom noise models and hardware optimizations. I introduce and validate techniques for error mitigation and readout error correction designed specifically for this algorithm/hardware combination. Though I do not achieve high accuracy with one type of QKM-based classifier, I provide evidence for possible fundamental limitations to the QKM as well as hardware limitations that are unaccounted for by a reasonable Markovian noise model.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectquantum machine learningen
dc.subjectquantum computingen
dc.subjectmachine learningen
dc.titleApplications of the Quantum Kernel Method on a Superconducting Quantum Processoren
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentPhysics and Astronomyen
uws-etd.degree.disciplinePhysics (Quantum Information)en
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Scienceen
uws-etd.embargo.terms1 yearen
uws.contributor.advisorKempf, Achim
uws.contributor.affiliation1Faculty of Scienceen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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