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master's thesis
Cjevovod za kvantno strojno učenje
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:407220
Cvitanović, Marijo
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Cvitanović, M. (2022). Cjevovod za kvantno strojno učenje (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:407220

Cvitanović, Marijo. "Cjevovod za kvantno strojno učenje." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:407220

Cvitanović, Marijo. "Cjevovod za kvantno strojno učenje." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:407220

Cvitanović, M. (2022). 'Cjevovod za kvantno strojno učenje', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 22 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:407220

Cvitanović M. Cjevovod za kvantno strojno učenje [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 July 22] Available at: https://urn.nsk.hr/urn:nbn:hr:168:407220

M. Cvitanović, "Cjevovod za kvantno strojno učenje", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:407220

Metadata
TitleCjevovod za kvantno strojno učenje
Title (english)Pipeline for quantum machine learning
AuthorMarijo Cvitanović
MentorMario Brčić (mentor)
Committee memberMario Brčić (predsjednik povjerenstva)
Committee memberKrešimir Fertalj (član povjerenstva)
Committee memberNikica Hlupić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-04, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Podatkovni sustav koji omogućava pouzdano i učinkovito provođenje operacija nad velikim skupovima podataka se naziva cjevovod. Podržavaju se i izvode operacije od dobave podataka, njihove pred-obrade te za stvaranje i korištenje modela. Radi pouzdanosti sustava i modela, koristi se prikupljanje dijagnostičkih podataka o funkcioniranju pojedinih faza u cjevovodu te se statistike prikazuju u prikladnom sučelju da bi podatkovni inženjer ili podatkovni znanstvenik mogli osiguravati željeni poslovni efekt. U ovom radu je izrađen cjevovod koji automatizira sve procese vezane za kvantni model strojnog učenja. Od verzioniranja modela do njegovog promatranja. Konstruirana je i objašnjena arhitektura i svaki njen detalj. Naposljetku, modelu, koji je postavljen na Kubernetes grozd, je moguće pristupiti preko REST servisa i dobiti odgovor na postavljeni upit.
Abstract (english)
A pipeline is a data system that enables reliable and efficient operations on large datasets. Operations from data supply, their pre-processing are supported and performed creating and using models. For system and model reliability, diagnostic collection is used on data of individual phases in the pipeline and statistics are displayed in a suitable interface. That interface could provide the desired business effect to a data engineer or data scientist. In this paper, pipeline was created that can automate the training and use of a separate model of quantum machine learning. The system architecture is constructed and explained which allows execution on a quantum computer as well as on a simulator. Trained and powered can be accessed through the REST service.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:407220
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2023-01-14 20:13:00