SimCATS_GaAs_v1_random_variations_v2

Dataset: SimCATS_GaAs_v1_random_variations_v2 Simulated data from the geometric SimCATS model (GitHub Repository, Paper) for benchmarking of semiconductor quantum dot tuning algorithms. Generated using this Jupyter Notebook and used for the final evaluation in Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams. Key Facts Contains pink, white & random telegraph noise, transition blurring, and dot jumps Random variations of charge transitions, sensor, and distortions 1.000 randomly sampled configurations with 100 CSDs each (in total: 100.000 CSDs) Usage To load the data, e.g. for calculating metrics, please have a look at SimCATS-Datasets (GitHub Repository, ReadTheDocs). The dataset can be loaded as numpy arrays using the function load_dataset or as PyTorch Dataset class (for machine learning purposes) using the class SimcatsDataset.