DOI: 10.5281/zenodo.16363472
title: Delft Charge Stability Diagram Dataset

description:  

Delft Charge Stability Digram Dataset (D-CSD) contains 9850 quantum dot charge stability digrams measured on Si/SiGe and Ge/SiGe devices in the labs of Menno Veldhorst and Lieven Vandersypen at QuTech at Delft University of Technology.

The charge stability diagrams are provided in raw .h5 format as well as .png image. The purpose of this dataset is to serve as a standard benchmark for machine learning and quantum data analysis community.

 

The CSDs directory contains the CSD dataset, with individual .h5 files for each CSD, storing voltage and measurement values. Plots of the CSDs, with the same name as the .h5 files, are stored in CSDs/_PNGs/.

 

The dataset was obtained using an ensemble classifier: a set of neural networks fine-tuned to classify CSD images as clean or noisy, based on a defined threshold.

 

All scripts used for labeling, training the ensemble classifier, and manipulating the data are located in the dataset_preparation directory. A detailed README is included, providing a step-by-step guide for reproducing the results.

 



Acknowledgements


This research was sponsored in part by the Army Research Office (ARO) under Award No. W911NF-23-1-0110 and W911NF-17-1-0274. The views, conclusions, and recommendations contained in this document are those of the authors and are not necessarily endorsed nor should they be interpreted as representing the official policies, either expressed or implied, of the Army Research Office (ARO) or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. This research was supported by the European Union’s Horizon Europe programme under the Grant Agreement 101069515 - IGNITE. This work was supported by the Kavli Foundation. This work is also part of the project Optimal Digital-Analog Quantum Circuits with file number NGF.1582.22.026 of the research programme NGF-Quantum Delta NL 2022 which is (partly) financed by the Dutch Research Council (NWO) and the Dutch National Growth Fund initiative Quantum Delta NL. We acknowledge the European Union through ERC Starting Grant QUIST (850641) and the National Growth Fund program Quantum Delta NL (grant NGF.1582.22.001). We acknowledge The Netherlands Ministry of Defence funding under Awards No.QuBits R23/009. This project is supported by the European Union through the Horizon 2020 research and innovation programme under the Grant Agreement No. 951852 (QLSI) and no. 101174557 (QLSI2 project). We acknowledge an Advanced Grant from the European Research Council (ERC) under the European Union’s Horizon 2020 program (882848). We acknowledge the Dutch Ministry for Economic Affairs through the allowance for Topconsortia for Knowledge and Innovation (TKI) and the Netherlands Organization for Scientific Research (NWO/OCW) as part of the Frontiers of Nanoscience (NanoFront) program. This was partly supported by Intel Corporation. We also acknowledge a Spinoza award of the Netherlands Organization for Scientific Research (NWO) and QuantERA ERA-NET Cofund in Quantum Technologies implemented within the European Union’s Horizon 2020 Program.