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Controlling quantum systems with machine learning

PhD defence, 1 November 2021, Mogens Dalgaard

2021.11.01 | Karen Konradi

Mogens Dalgaard

During his PhD studies, Mogens Dalgaard researched control of quantum systems. Many quantum architectures are applicable in technologies such as computers with potential improvements in processing powers. However, constructing these quantum technologies requires improvements in our ability to control the underlying quantum systems. In his research, Mogens Dalgaard has investigated the use of machine learning to overcome some of the challenges we currently phase in this regard.

Specifically, Mogens Dalgaard found that a machine learning-based optimization algorithm could outperform conventional quantum optimization algorithms with potential application in quantum computing.

The PhD degree was completed at the Department of Physics and Astronomy, Aarhus University.


This summary was prepared by the PhD student. 

Time: Monday, 1 November 2021 at 13.00

Place: Building 1525, Room 626

Title of dissertation: Mastering quantum systems – with simulations, control, and deep learning.

Contact information: Mogens Dalgaard, dalgaard@phys.au.dk

Members of the assessment committee:

Dr. Professor Alexander Pechen, Russian Academy of Sciences, Russia.

Dr. Professor Renato Renner, Institute for Theoretical Physics Zürich, Switzerland.

Dr. Associate Professor Steen Hannestad (chair), Department of Physics and Astronomy, Aarhus University, Denmark.

Main supervisor: Dr. Professor MSO Jacob Sherson, Department of Physics and Astronomy, Aarhus University, Denmark.


Language: The PhD dissertation will be defended in English 

The defense is public.
Due to the coronavirus situation, the PhD thesis will not be available for reading at the offices of the PhD administration. Instead, interested parties may send an e-mail to gradschool.nat@au.dk to receive a digital copy of the PhD thesis. Please note, it may in certain cases be necessary to make an individual arrangement to read the PhD thesis.

PhD defence
16882 / i43