Evaluation of synthetic and experimental training data in supervised machine learning applied to charge state detection of quantum dots

Automated tuning of gate-defined quantum dots is a requirement for large scale semiconductor based qubit initialisation. An essential step of these tuning procedures is charge state detection based on charge stability diagrams. Using supervised machine learning to perform this task requires a large dataset for models to train on. In order to avoid hand labelling experimental data, synthetic data has been explored as an alternative. While providing a significant increase in the size of the training dataset compared to using experimental data, using synthetic data means that classifiers are trained on data sourced from a different distribution than the experimental data that is part of the tuning process. Here we evaluate the prediction accuracy of a range of machine learning models trained on simulated and experimental data and their ability to generalise to experimental charge stability diagrams in two dimensional electron gas and nanowire devices. We find that classifiers perform best on either purely experimental or a combination of synthetic and experimental training data, and that adding common experimental noise signatures to the synthetic data does not dramatically improve the classification accuracy. These results suggest that experimental training data as well as realistic quantum dot simulations and noise models are essential in charge state detection using supervised machine learning

Comparison of an 8-Channel and a 32-Channel Coil for High-Resolution fMRI at 7T

Multi-channel receive array rf-coils have become widely available for fMRI. The improved SNR and possibility of acquisition acceleration through parallel imaging are especially beneficial for high-resolution studies. In this study, an 8-channel and a 32-channel coil were compared in a high-resolution finger tapping fMRI experiment at 7T. 1.3mm3 resolution data acquired with the 32-channel coil provided higher image- and temporal SNR and yielded higher BOLD sensitivity measures, notably higher cluster sizes in MI/SI and increased z-scores, though not an increase in percent signal change. For sub-millimeter resolution fMRI data acquired with the 32-channel coil smaller clusters were found, though percent signal changes were significantly larger, due to reduced partial volume effects. These results demonstrate the utility of the use of an array coil with a large number of receive elements for high-resolution fMRI at ultra-high field

Large Scale Deployment of PV Units in Existing Distribution Networks: Optimization of the Installation Layout

In fixed PV installations, the azimuth and tilt of the panels are normally chosen with the objective of maximizing the plant capacity factor. In this paper, we show that when considering distribution networks with densely clustered PV plants, there exist installation criteria other than the conventional that achieves larger amount of PV generated electricity without violating distribution network constraints. In particular, we formulate an optimization problem to determine the siting, sizing, azimuth and tilt of the panels in order to maximize the PV production over a year while respecting the power grid voltage and line ampacity constraints. As a case study, we consider a Swiss distribution network and synthetic irradiance data for the considered area using a clear-sky model. We use the case study to compare the conventional way of installing PV panels vs the proposed method and show that the latter can nearly achieve a 6% increase in the generated PV electricity

Comparison of an 8-Channel and a 32-Channel Coil for High-Resolution fMRI at 7 T

Multi-channel receive array rf-coils have become widely available for fMRI. The improved SNR and possibility of acquisition acceleration through parallel imaging are especially beneficial for high-resolution studies. In this study, an 8-channel and a 32-channel coil were compared in a high-resolution finger tapping fMRI experiment at 7 T. 1.3 mm3 resolution data acquired with the 32-channel coil provided higher image- and temporal SNR and yielded higher BOLD sensitivity measures, notably higher cluster sizes in MI/SI and increased z-scores, though not an increase in percent signal change. For sub-millimeter resolution fMRI data acquired with the 32-channel coil smaller clusters were found, though percent signal changes were significantly larger, due to reduced partial volume effects. These results demonstrate the utility of the use of an array coil with a large number of receive elements for high-resolution fMRI at ultra-high field

QCoDeS/Qcodes: QCoDeS 0.42.1

<h2>Improved:</h2> <ul> <li>Avoid warning from OpenTelemetry when exporting datasets to netcdf. (#5529)</li> </ul&gt