Integrated Calendar

Quantum technologies allow for fully novel schemes of hybrid computing. We
employ modern segmented ion traps. I will sketch architectures, the required trap
technologies and fabrication methods, control electronics for quantum register
reconfigurations, and recent improvements of qubit coherence and gate
performance. Currently gate fidelities of 99.995% (single bit) and 99.8% (two bit) are
reached. We are implementing a reconfigurable qubit register and have realized
multi-qubit entanglement [1] and fault-tolerant syndrome readout [2] in view for
topological quantum error correction [3] and realize user access to quantum
computing [4]. The setup allows for mid-circuit measurements and real-time control
of the algorithm. We are currently investigating various used cases, including
variational quantum eigensolver approaches for chemistry or high energy relevant
models, and measurement-based quantum computing. The fully equipped in house
clean room facilities for selective laser etching of glass enables us to design and
fabricate complex ion trap devices, in order to scale up the number of fully
connected qubits. Also, we aim for improving on the speed of entanglement
generation. The unique and exotic properties of ions in Rydberg states [5] are
explored experimentally, staring with spectroscopy [6] of nS and nD states where
states with principal quantum number n=65 are observed. The high polarizability [7]
of such Rydberg ions should enable sub-μs gate times [8].

[1] Kaufmann er al, Phys. Rev. Lett. 119, 150503 (2017)
[2] Hilder, et al., Phys. Rev. X.12.011032 (2022)
[3] Bermudez, et al, Phys. Rev. X 7, 041061 (2017)
[4] https://iquan.physik.uni-mainz.de/
[5] A. Mokhberi, M. Hennrich, F. Schmidt-Kaler, Trapped Rydberg
ions: a new platform for quantum information processing,
Advances In Atomic, Molecular, and Optical Physics, Academic
Press, Ch. 4, 69 (2020), arXiv:2003.08891
[6] Andrijauskas et al, Phys. Rev. Lett. 127, 203001 (2021)
[7] Niederlander et al, NJP 25 033020 (2023)
[8] Vogel et al, Phys. Rev. Lett. 123, 153603 (2019)

Over the past decade, deep learning has made significant strides in the fields of computer vision and machine learning. However, there is still a lack of understanding regarding how these machines store and utilize training samples to generalize to unseen data. In my thesis (guided by Prof. Irani), I investigated how neural networks encode training samples in their parameters and how such samples can sometimes be reconstructed. Additionally, I examined the capabilities of generative models in learning and generalizing from a single video. Specifically, I explored the effectiveness of patch-based methods and diffusion models in generating diverse output samples, and how such models can utilize the motion and dynamics of a single input video to learn and generalize.

Artificial metalloenzymes (ArMs) have attracted increasing attention in the past two
decades as attractive alternatives to either homogeneous catalysts or enzymes.
Artificial metalloenzymes result from anchoring a catalytically competent abiotic metal
cofactor within a host protein, Figure. The resulting ArMs combine attractive features
of both homogeneous- and bio-catalysts. Importantly, they enable access to new-tonature
reactions in a cellular environment.
Relying on a supramolecular anchoring of an organometallic cofactor in various
protein scaffolds, we have optimized the performance of ArMs for sixteen different
reactions, Figure.
Following a general introduction to the underlying principles of ArMs, this talk will
highlight our recent progress in engineering and evolving such hybrid catalysts for
olefin metathesis, C–H activation, hydroamination, and allylic substitution. A
particular emphasis will be set on performing catalysis in a cellular environment.

It is a long-standing open question in quantum complexity theory whether the definition of non-deterministic quantum computation requires quantum witnesses (

It is a long-standing open question in quantum complexity theory whether the definition of non-deterministic quantum computation requires quantum witnesses (