Master Thesis MSTR-2019-54

BibliographyRadic, Marco: Quantum-enhanced machine learning in the NISQ era.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 54 (2019).
61 pages, english.

Quantum computation technologies have reached a new level of sophistication with the release of the first commercial offerings. Likewise, Machine Learning is popular for use-cases in both industry and research. With Quantum Machine Learning, one hopes to combine both areas in a symbiotic relationship to achieve an advantage in artificial intelligence with the use of quantum technologies. Recently presented approaches make use of quantum technologies in combination with classical hardware resources in order to mitigate the problems imposed by shortcomings of quantum computers of the current generation. Some of these approaches use quantum circuits with free parameters, which are optimized to solve problems and objectives in Machine Learning. This work presents a concept for automated modelling of these quantum circuits, with the goal of constructing suitable circuits for the task of classification. The concept is implemented in a prototype and validated in experiments.

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Department(s)University of Stuttgart, Institute of Architecture of Application Systems
Superviser(s)Leymann, Prof. Frank; Vietz, Daniel
Entry dateDecember 9, 2019
   Publ. Institute   Publ. Computer Science