Welcome to my homepage!
I am a Senior Research Fellow in the Quantum Systems Engineering group at the University of Oxford and a Senior Research Fellow at Keble College. I am a theoretical physicist working in the field of quantum physics and its applications in Quantum Technologies.
Quantum technologies aim to enhance the performance of next-generation technologies by exploiting the laws of quantum physics. Quantum physics is the branch of physics that describes the laws of nature that apply to microscopic objects which are much smaller than those of our everyday life. The typical size of these microscopic objects is a single atom, which is a basic unit of matter. Almost one hundred years after the invention of quantum physics, our increasing ability to control complex physical systems on the quantum level promises to dramatically enhance the performance of applications like sensing, metrology, communication, imaging, and computing. More details about my current research can be found here.
- (07.03.2023) Our paper on tensor network reduced order models for wall-bounded flows appears on the arXiv: M. Kiffner and D. Jaksch, Tensor network reduced order models for wall-bounded flows, arXiv:2303.03010.
- (06.06.2022) Our paper on coarse grained intermolecular interactions on quantum processors gets published: L. W. Anderson, M. Kiffner, P. Kl. Barkoutsos, I. Tavernelli, J. Crain, and D. Jaksch, Coarse grained intermolecular interactions on quantum processors, Phys. Rev. A 105, 062409 (2022).
- (06.05.2022) Our paper on quantum self-supervised learning gets published: B. Jaderberg, L. W. Anderson, W. Xie, S. Albanie, M. Kiffner, and D. Jaksch, Quantum self-supervised learning, Quantum Sci. Technol. 7 035005 (2022).
- (13.01.2022) Our paper on analysing and simulating turbulence with a quantum inspired approach gets published in Nature Computational Science: N. Gourianov, M. Lubasch, S. Dolgov, Q. Y. van den Berg, H. Babaee, P. Givi, M. Kiffner, and D. Jaksch, A Quantum Inspired Approach to Exploit Turbulence Structures, Nat Comput Sci 2, 30–37 (2022).