"Quantum cryptography aims to exploit the laws of quantum mechanics to securely transmit data. Among different possible encryption methods, we focus on quantum key distribution (QKD). In this context, the no-cloning theorem limits the access of an eavesdropper to information communicated via a quantum channel between two parties. In this thesis, we implement a specific prepare-and-measure continuous-variable QKD protocol proposed by Cerf et al. [1], which encodes classical information in displacement amplitudes of squeezed coherent states of light. In our experimental implementation, we use propagating squeezed microwaves at the carrier frequency of f0 = 5.5231 GHz. The detection of these microwave signals relies on cryogenic amplification chains. Here, state-of-the-art cryogenic high-electron mobility transistor amplifiers add 10 − 20 noise photons, corresponding to a quantum efficiency of η < 5%. These phase-insensitive amplifiers are ill-suited for QKD, as they typically reduce the signal-to-noise ratio (SNR) below a threshold required for the secure communication and are also bound by the standard quantum limit (SQL), η = 50%. Therefore, we make use of superconducting phase-sensitive amplifiers which can even exceed the SQL to implement the aforementioned CV-QKD protocol with quantum microwaves in the single-shot regime. As our main experimental result, we achieve a positive secret key for our microwave CV-QKD protocol implementation and analyze its robustness against an eavesdropping attack. To this end, we use a Josephson parametric amplifier (JPA) in the phase-sensitive regime at the beginning of the cryogenic amplification chain. With this modification, we demonstrate a significant improvement in the experimental quantum efficiency, η = 38%. This step allows us to increase the SNR from 14% to 177% during the CV-QKD protocol sequence which results in the positive secret key. The current SNR is mainly limited by the dynamic range of our JPAs. In the future, the SNR can be further improved by exploiting traveling wave parametric amplifiers. Our results highlight the experimental feasibility of microwave CV-QKD protocols."

[1] N. J. Cerf, M. Lévy, and G. V. Assche, Quantum distribution of Gaussian keys using squeezed states, Physical Review A 63, 052311 (2001).

(Published at www.wmi.badw.de/fileadmin/WMI/Publications/Krueger_Philipp_Masterarbeit_2022.pdf)

"This essay is focused around the idea of unpaired Majorana Fermions in quantum wires as proposed by Kitaev in 2001 [1], and experimental signatures of Majorana Fermions in Hybrid Superconductor-Semiconductor Nanowire Devices discovered by Mourik et al. in 2012 [2]. After a brief motivation, the theoretical background of Kitaev’s toy model is presented, afterwards the findings of Mourik et al. will be discussed. Finally, an outlook is given on the future of Majorana Fermions."

[1] Kitaev and A. Yu, Unpaired majorana fermions in quantum wires, Physics-Uspekhi 44, 131–136 (2001).
[2] V. Mourik, K. Zuo, S. M. Frolov, S. R. Plissard, E. P. A. M. Bakkers, and L. P. Kouwenhoven, Signatures of majorana fermions in hybrid superconductorsemiconductor nanowire devices, Science 336, 1003 (2012).

"ChargeQ offers the only platform that uses quantum computing to provide solutions for managing the load on power grids from high-performance electric car charging systems. ChargeQ solves the energy supply problem with its communication, control and information platform that provides intelligent charging management for wallboxes and charging stations for EVs. The algorithm calculates the most favorable sequence of charging processes, taking into account customer preferences, and generates individual suggestions based on charging patterns. Scheduling problems are a subset of complex optimization problems where normal algorithms reach their limits. That’s why we solve this problem with quantum computing! [...] Our solution has three central aims: Prevention of electricity peaks, cost-savings for DSOs and combating climate change through accelerating EV adoption."

"The goal of this study was to find the quantum phase transition at intermediate local disorder strengths on a Heisenberg chain. Exact diagonalization was used to find the reduced density matrices for a different number of consecutive spins for the lowest energy eigenstate of the Heisenberg model with an additional random field in z-direction at low and high disorder strengths. The resulting dataset representing extended and localized phases was used to train a neural network. Afterwards, the trained network was applied on intermediate disorder strengths to deduct the critical disorder strength for a phase transition. This phase transition was predicted for all system sizes to be around Wc = 2.5J for the system sizes L ∈ {8, 9, 10, 11, 12} and block sizes n ∈ [1, 6]. Low block and system sizes suffered from low accuracy and high losses in the machine learning model, whereas for n > 3 block sizes the Wc value showed smaller deviations from a previously published theoretical value Wc ≈ 3.6 calculated with entanglement entropy on systems up to L = 22. This deviation can be attributed to the effect of the smaller system sizes and the effect of open boundary conditions."