Print Email Facebook Twitter Learning based hardware-centric quantum circuit generation Title Learning based hardware-centric quantum circuit generation Author Schalkers, Merel (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Möller, M. (mentor) de Laat, D. (mentor) Aardal, K.I. (graduation committee) Degree granting institution Delft University of Technology Programme Applied Mathematics Date 2021-05-19 Abstract Large faulttolerant universal gate quantum computers will provide a major speedup to a variety of common computational problems. While such computers are years away, we currently have noisy intermediatescale quantum (NISQ) computers at our disposal. In this project we present two quantum machine learning approaches that can be used to find quantum circuits suitable for specific NISQ devices. We present one gradientbased and one nongradient based machine learning approach to optimize the created quantum circuits, to best mimic the behaviour of a given function up to measurement. We make sure that the created quantum circuits obey the restrictions of the chosen hardware, therefore the approaches can be used to find circuits perfectly suited for specific NISQ devices. This enables the user to make the best use of quantum technology in the near future. In doing this we created our own quantum simulator which can be used to simulate small quantum circuits that obey hardware restrictions. We also present the method used to implement this simulator. Finally we present the results of applying both machine learning approaches to different problem types and compare their performance. Subject OptimizationQuantum ComputingMachine Learning To reference this document use: http://resolver.tudelft.nl/uuid:a799cbe9-abcd-4b31-9e42-02f3414f5c2c Part of collection Student theses Document type master thesis Rights © 2021 Merel Schalkers Files PDF MSc_Thesis_Schalkers_4932110.pdf 729.99 KB Close viewer /islandora/object/uuid:a799cbe9-abcd-4b31-9e42-02f3414f5c2c/datastream/OBJ/view