Data for benchmarking machine learning models, taken from Better than classical? The subtle art of benchmarking quantum machine learning models. The Downscaled MNIST classification task is a simplified version of the famous MNIST handwritten digits dataset. This version involves distinguishing between digits 3 and 5 rather than the full range 0-9.
Description of the dataset
This collection provides 19 datasets that consist of flat input vectors of dimension d=2,…,20. The inputs were produced by fitting a PCA dimensionality reduction model on the original MNIST training sets, and using the same model to reduce the images from the test set.
Additional details
Source code
tab to check how the data was generated.Example usage
[ds] = qml.data.load("other", name="mnist-pca")
ds.train['4']['inputs'] # points in 4-dimensional space
ds.test['4']['labels'] # labels for the points above
Joseph Bowles, Shahnawaz Ahmed, Maria Schuld
version 0.1 : initial public release
Maria Schuld
Dedicated to making quantum machine learning a reality one day.
Joseph Bowles
Quantum Machine Learning researcher at Xanadu
Shahnawaz Ahmed
Code. Quantum. ML