Co-sputtered MoRe thin films for carbon nanotube growth-compatible superconducting coplanar resonators

Molybdenum rhenium alloy thin films can exhibit superconductivity up to critical temperatures of $T_c=15\mathrm{K}$. At the same time, the films are highly stable in the high-temperature methane / hydrogen atmosphere typically required to grow single wall carbon nanotubes. We characterize molybdenum rhenium alloy films deposited via simultaneous sputtering from two sources, with respect to their composition as function of sputter parameters and their electronic dc as well as GHz properties at low temperature. Specific emphasis is placed on the effect of the carbon nanotube growth conditions on the film. Superconducting coplanar waveguide resonators are defined lithographically; we demonstrate that the resonators remain functional when undergoing nanotube growth conditions, and characterize their properties as function of temperature. This paves the way for ultra-clean nanotube devices grown in situ onto superconducting coplanar waveguide circuit elements.Comment: 8 pages, 6 figure

Optomechanical coupling and damping of a carbon nanotube quantum dot

Carbon nanotubes are excellent nano-electromechanical systems, combining high resonance frequency, low mass, and large zero-point motion. At cryogenic temperatures they display high mechanical quality factors. Equally they are outstanding single electron devices with well-known quantum levels and have been proposed for the implementation of charge or spin qubits. The integration of these devices into microwave optomechanical circuits is however hindered by a mismatch of scales, between typical microwave wavelengths, nanotube segment lengths, and nanotube deflections. As experimentally demonstrated recently in [Blien et al., Nat. Comm. 11, 1363 (2020)], coupling enhancement via the quantum capacitance allows to circumvent this restriction. Here we extend the discussion of this experiment. We present the subsystems of the device and their interactions in detail. An alternative approach to the optomechanical coupling is presented, allowing to estimate the mechanical zero point motion scale. Further, the mechanical damping is discussed, hinting at hitherto unknown interaction mechanisms.Comment: 17 pages, 13 figures, 3 table

Development in Regional Labour Market in Germany: a Comparative Analysis of the Forecasting Performance of Competing Statistical Models

The aim of this paper is to forecast regional employment patterns in West German regions. After a brief exposition of key labour market issues, Artificial Neural Network (ANN) techniques are proposed as a new tool to generate reliable short term employment forecasts at a regional level. A variety of ANN models are developed and compared. Comparison with methods commonly applied to panel data, such as GMM (Generalised Method of Moments), confirms the ability of ANNs to capture complex data structures in a multi-regional context

A Rank-Order Test on the Statistical Performance of Neural Network Models for Regional Labor Market Forecasts

Using a panel of 439 German regions we evaluate and compare the performance of various Neural Network (NN) models as forecasting tools for regional employment growth. Because of relevant differences in data availability between the former East and West Germany, the NN models are computed separately for the two parts of the country. The comparisons of the models and their ex post forecasts are carried out by means of a non-parametric test: viz. the Friedman statistic. The Friedman statistic tests the consistency of model results obtained in terms of their rank order. Since there is no normal distribution assumption, this methodology is an interesting substitute for a standard analysis of variance. Furthermore, the Friedman statistic is indifferent to the scale on which the data are measured. The evaluation of the ex post forecasts suggests that NN models are generally able to correctly identify the fastest-growing and the slowest-growing regions, and hence predict rather well the correct ranking of regions in terms of their employment growth. The comparison among NN models \u2013 on the basis of several criteria \u2013 suggests that the choice of the variables used in the model may influence the model\u2019s performance and the reliability of its forecasts