TNO Quantum: Variational classifier

TNO Quantum provides generic software components aimed at facilitating the development of quantum applications.

The tno.quantum.ml.classifiers.vc package provides a VariationalClassifier class, which has been implemented in accordance with the scikit-learn estimator API. This means that the classifier can be used as any other (binary and multiclass) scikit-learn classifier and combined with transforms through Pipelines. In addition, the VariationalClassifier makes use of PyTorch tensors, optimizers, and loss functions.

Limitations in (end-)use: the content of this software package may solely be used for applications that comply with international export control laws.

Documentation

Documentation of the tno.quantum.ml.classifiers.vc package can be found here.

Install

Easily install the tno.quantum.ml.classifiers.vc package using pip:

$ python -m pip install tno.quantum.ml.classifiers.vc

If you wish to run the tests you can use:

$ python -m pip install 'tno.quantum.ml.classifiers.vc[tests]'

Example

Here's an example of how the VariationalClassifier class can be used for classification based on the Iris dataset: Note that tno.quantum.ml.datasets is required for this example.

from tno.quantum.ml.classifiers.vc import VariationalClassifier
from tno.quantum.ml.datasets import get_iris_dataset

X_training, y_training, X_validation, y_validation = get_iris_dataset()
vc = VariationalClassifier().fit(X_training, y_training, n_iter=5)
predictions = vc.predict(X_validation)