Leibniz University of Hanover

About: Leibniz University of Hanover is a education organization based out in Hanover, Niedersachsen, Germany. It is known for research contribution in the topics: Finite element method & Population. The organization has 14283 authors who have published 29845 publications receiving 682152 citations.

Training deep quantum neural networks.

Abstract: Neural networks enjoy widespread success in both research and industry and, with the advent of quantum technology, it is a crucial challenge to design quantum neural networks for fully quantum learning tasks. Here we propose a truly quantum analogue of classical neurons, which form quantum feedforward neural networks capable of universal quantum computation. We describe the efficient training of these networks using the fidelity as a cost function, providing both classical and efficient quantum implementations. Our method allows for fast optimisation with reduced memory requirements: the number of qudits required scales with only the width, allowing deep-network optimisation. We benchmark our proposal for the quantum task of learning an unknown unitary and find remarkable generalisation behaviour and a striking robustness to noisy training data.

Models for numerical device simulations of crystalline silicon solar cells--a review

Abstract: Current issues of numerical modeling of crystalline silicon solar cells are reviewed. Numerical modeling has been applied to Si solar cells since the early days of computer modeling and has recently become widely used in the photovoltaics (PV) industry. Simulations are used to analyze fabricated cells and to predict effects due to device changes. Hence, they may accelerate cell optimization and provide quantitative data e.g. of potentially possible improvements, which may form a base for the decision making on development strategies. However, to achieve sufficiently high prediction capabilities, several models had to be refined specifically to PV demands, such as the intrinsic carrier density, minority carrier mobility, recombination at passivated surfaces, and optical models. Currently, the most unresolved issue is the modeling of the emitter layer on textured surfaces, the hole minority carrier mobility, Auger recombination at low dopant densities and intermediate injection levels, and fine-tuned band parameters as a function of temperature. Also, it is recommended that the widely used software in the PV community, PC1D should be extended to Fermi-Dirac statistics.

Bose-Einstein Condensation in Microgravity

Abstract: Albert Einstein's insight that it is impossible to distinguish a local experiment in a "freely falling elevator" from one in free space led to the development of the theory of general relativity. The wave nature of matter manifests itself in a striking way in Bose-Einstein condensates, where millions of atoms lose their identity and can be described by a single macroscopic wave function. We combine these two topics and report the preparation and observation of a Bose-Einstein condensate during free fall in a 146-meter-tall evacuated drop tower. During the expansion over 1 second, the atoms form a giant coherent matter wave that is delocalized on a millimeter scale, which represents a promising source for matter-wave interferometry to test the universality of free fall with quantum matter.

The Efficiency of Motion-Compensating Prediction for Hybrid Coding of Video Sequences

Abstract: Performance bounds for generalized hybrid coding of video sequences with motion-compensating prediction are derived based on rate-distortion theory. It is shown that the spatial power spectrum of the motion-compensated prediction error can be calculated from the signal power spectrum and the displacement estimation error p.d.f.. A spatial Wiener filter can improve the efficiency of motion-compensating prediction. Memoryless encoding of the motion-compensated prediction error and intraframe encoding of the motion-compensated prediction error are compared. An evaluation of the rate-distortion functions for a typical videoconference sampling format shows that for integer pel accuracy of the displacement estimate the additional gain by motion-compensating prediction over pure intraframe coding is limited to ∼ 0.8 bits/sample in moving areas. Required accuracies of the displacement estimate for a gain of motion-compensating interframe coding over intraframe coding are given.