1. Quantum Datasets
  2. /Downscaled MNIST
  1. Quantum Datasets
  2. /Downscaled MNIST

Downscaled MNIST

Downscaled MNIST

Downscaled MNIST dataset visualization

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,,20d=2,\ldots,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

  • The class labels are defined as -1, 1.
  • For each dimension, 11552 labeled points are provided for training and 1902 for testing.
  • Please see the 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

Authors

Joseph Bowles, Shahnawaz Ahmed, Maria Schuld

Other

Updated

2024-12-20

version 0.1 : initial public release



Maria Schuld

Maria Schuld

Dedicated to making quantum machine learning a reality one day.

Joseph Bowles

Joseph Bowles

Quantum Machine Learning researcher at Xanadu

Shahnawaz Ahmed

Shahnawaz Ahmed

Code. Quantum. ML