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undergraduate thesis
Primjena metoda strojnog učenja za kvantna računala
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:050504
Ljubas, Jan
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Ljubas, J. (2023). Primjena metoda strojnog učenja za kvantna računala (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:050504

Ljubas, Jan. "Primjena metoda strojnog učenja za kvantna računala." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:050504

Ljubas, Jan. "Primjena metoda strojnog učenja za kvantna računala." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:050504

Ljubas, J. (2023). 'Primjena metoda strojnog učenja za kvantna računala', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 21 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:050504

Ljubas J. Primjena metoda strojnog učenja za kvantna računala [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 June 21] Available at: https://urn.nsk.hr/urn:nbn:hr:168:050504

J. Ljubas, "Primjena metoda strojnog učenja za kvantna računala", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:050504

Metadata
TitlePrimjena metoda strojnog učenja za kvantna računala
Title (english)Application of Machine Learning Methods for Quantum Computers
AuthorJan Ljubas
MentorGoran Delač (mentor)
Committee memberGoran Delač (predsjednik povjerenstva)
Committee memberMarin Kačan (član povjerenstva)
Committee memberHelena Čeović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-05, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Glavna tema ovog završnog rada su metode strojnog učenja primjenjive za kvantna računala. Cilj rada je proučiti koje su metode trenutno ostvarive za izvršavanje na postojećim kvantnim računalima i kako hibridni klasično-kvantni algoritmi pospješuju dosadašnje klasične algoritme. Za prikaz programskog kôda i simulacije izabran je IBM-ov open-source SDK za programiranje kvantnih računala, Qiskit. [3] Glavninu rada čini pregled metoda strojnog učenja za koje postoje kvantni pandani [6]. Poseban naglasak stavlja se na programsku potporu za kvantne neuronske mreže i na kvantni SVM (na klasifikaciju i regresiju pomoću ansatza). Kroz kôd pisan u Qiskitu detaljno se opisuju postupci i teorijska podloga koja omogućava iskorištavanje prednosti kvantnih računala nad klasičnim računalima. Prikaz (teorijskih, simuliranih) rezultata napravljen je dijagramima i tablicama.
Abstract (english)
The main topic of this bachelor's thesis are machine learning methods applicable for quantum computers. The objective is to study the methods which are applicable on the existing or the near-term quantum computers and explore how the hybrid quantum-classical algorithms (in theory) enhance traditional, classical algorithms. Implementing the quantum algorithms and running simulations was done using Qiskit, IBM's open-source SDK. [3] The bulk of the paper consists of an overview of the well-known machine learning methods and their quantum counterparts [6]. In Chapters 3 and 4, special emphasis is placed on quantum neural networks implementation and quantum support vector machines(SVM) (or solving the classification and regression problems using variational quantum circuits). Along with the Qiskit code, the description of theoretical foundations behind it is provided. The simulated results are presented through diagrams and tables.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:050504
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-10-11 09:35:43