https://github.com/3it-inpaqt/qdsd-dataset
Dataset of quantum dots stability diagrams for machine learning application
Science Score: 13.0%
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Low similarity (7.6%) to scientific vocabulary
Repository
Dataset of quantum dots stability diagrams for machine learning application
Basic Info
- Host: GitHub
- Owner: 3it-inpaqt
- Language: Python
- Default Branch: main
- Size: 22.3 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Quantum Dots Stability Diagrams Dataset
Dataset of Quantum Dots Stability Diagrams (QDSD) for machine learning application. This dataset is used for offline quantum dot autotuning.
Download data
The experimental data should be downloaded from QDSD, and (at least) the
file originals.zip should be downloaded and unzipped in the data folder.
The folder is organized as follows:
- originals.zip - The original data we received from experimentalists (before any processing), grouped by origins. No data processing was applied.
- raw_clean.zip - Compressed files containing all data. Each CSV file is a stability diagram. The CSV has 3
columns:
x, y, z. Wherexandyare the swiped gate voltages in Volt andzis the measured electric current in Amper. No data processing has been applied yet. - interpolated_csv.zip - Compressed files containing all diagrams as CSV 2D arrays. Interpolation and float rounding applied (data loss).
- interpolated_images.zip - Compressed files containing all diagrams as PNG images. They are mainly used for transition line and charge area labeling. Interpolation and extreme values filter applied (data loss).
- labels.json - Transition line and charge area labels (exported from Labelbox)
Data flow
Data processing
The data processing is kept minimal to be as close as possible to the reality of experimentation. However, in some cases, the alteration of data was necessary to be adapted to machine learning applications.
Interpolation
To have the same constant voltage step between measurements in all stability diagrams, we interpolate the data.
The 'nearest' interpolation method is used to upscale or downscale the resolution of the x or y axes when it is
necessary.
Filter extreme values
To visually represent the diagrams (for labeling), it was necessary to remove extreme values. This was done by limiting the values between the 1st and the 99th percentile for each diagram.
Rounding
In some case the voltage value is rounded to 6 decimals (microvolt).
Processing Scripts
- data_cleanup/: originals => raw_clean
Convert the specific file structure to a standard one. - rawtoimages/: rawclean => interpolatedcsv & interpolated_images
Interpolate data to have plottable images ready to be annotated.
Data contribution
The original data have been provided by different research groups based on the following references:
- Rochette et al. 2019 (referred as
michel_pioro_ladriere) - Gaudreau et al. 2009 (referred as
louis_gaudreau) - Stuyck et al. 2021 (referred as
eva_dupont_ferrier)
Owner
- Name: Integrated Nanoelectronics and Packaging for AI and Quantum Technologies (INPAQT)
- Login: 3it-inpaqt
- Kind: organization
- Location: Sherbrooke, Canada
- Website: nano-electronique/groupe-inpaqthttps://www.usherbrooke.ca/ln2/recherche/
- Repositories: 18
- Profile: https://github.com/3it-inpaqt
Sherbrooke University – Institut Interdisciplinaire d'Innovation Technologique (3IT) – Laboratoire Nanotechnologies et Nanosystèmes (LN2)
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Dependencies
- configargparse >=1.5
- labelbox >=3.47
- matplotlib >=3.7
- numpy >=1.24
- pandas >=2.0
- scipy >=1.10
- shapely >=2.0