10.26165/JUELICH-DATA/5PB3GTHader, FabianFabianHader0000-0003-3490-3227ZEA-2Fuchs, FabianFabianFuchs0009-0009-1060-6093Fleitmann, SarahSarahFleitmann0000-0002-7699-5708ZEA-2Jülich DATA2024Computer and Information ScienceEngineeringSimulationQuantum DotsCharge-Stability-DiagramCSDHader, FabianFabianHaderZEA-22024-10-092024-11-1810.5281/zenodo.139032851.1CC0 Waiver<h1>Dataset: SimCATS_GaAs_v1_random_variations_v2</h1> <p>Simulated data from the geometric SimCATS model (<a title="SimCATS GitHub Repository" href="https://github.com/f-hader/SimCATS" target="_blank" rel="noopener">GitHub Repository</a>, <a title="SimCATS Paper" href="https://ieeexplore.ieee.org/document/10640289" target="_blank" rel="noopener">Paper</a>) for benchmarking of semiconductor quantum dot tuning algorithms.</p> <p>Generated using <a title="Jupyter Notebook: Dataset Generation" href="https://github.com/f-hader/SimCATS-Datasets/blob/v2.4.0/notebooks/dataset_gen%5Brandom_variations%5D.ipynb" target="_blank" rel="noopener">this Jupyter Notebook</a> and used for the final evaluation in <a title="Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams" href="https://doi.org/10.36227/techrxiv.172963185.53119182/v1" target="_blank" rel="noopener">Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams</a>.</p> <h2>Key Facts</h2> <ul> <li>Contains pink, white &amp; random telegraph noise, transition blurring, and dot jumps</li> <li>Random variations of charge transitions, sensor, and distortions</li> <li>1.000 randomly sampled configurations with 100 CSDs each (in total: 100.000 CSDs)&nbsp;&nbsp;</li> </ul> <h2>Usage</h2> <p>To load the data, e.g. for calculating metrics, please have a look at SimCATS-Datasets (<a title="SimCATS-Datasets GitHub Repository" href="https://github.com/f-hader/SimCATS-Datasets" target="_blank" rel="noopener">GitHub Repository</a>, <a title="ReadTheDocs" href="https://simcats-datasets.readthedocs.io/" target="_blank" rel="noopener">ReadTheDocs</a>). The dataset can be loaded as numpy arrays using the function <a title="load_dataset documentation" href="https://simcats-datasets.readthedocs.io/en/latest/autoapi/simcats_datasets/loading/index.html#simcats_datasets.loading.load_dataset" target="_blank" rel="noopener">load_dataset</a> or as PyTorch Dataset class (for machine learning purposes) using the class <a title="SimcatsDataset documentation" href="https://simcats-datasets.readthedocs.io/en/latest/autoapi/simcats_datasets/loading/pytorch/index.html#simcats_datasets.loading.pytorch.SimcatsDataset" target="_blank" rel="noopener">SimcatsDataset</a>.</p>