# 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](https://scikit-learn.org/stable/developers/develop.html).
This means that the classifier can be used as any other (binary and multiclass)
scikit-learn classifier and combined with transforms through
[Pipelines](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html).
In addition, the `VariationalClassifier` makes use of
[PyTorch](https://pytorch.org/docs/stable/tensors.html) 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](https://tno-quantum.github.io/ml.classifiers.vc/).
## Install
Easily install the `tno.quantum.ml.classifiers.vc` package using pip:
```console
$ python -m pip install tno.quantum.ml.classifiers.vc
```
If you wish to run the tests you can use:
```console
$ 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](https://en.wikipedia.org/wiki/Iris_flower_data_set):
Note that `tno.quantum.ml.datasets` is required for this example.
```python
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()
vc = vc.fit(X_training, y_training)
predictions_validation = vc.predict(X_validation)
```
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"description": "# TNO Quantum: Variational classifier\r\n\r\nTNO Quantum provides generic software components aimed at facilitating the development\r\nof quantum applications.\r\n\r\nThe `tno.quantum.ml.classifiers.vc` package provides a `VariationalClassifier` class, which has been implemented \r\nin accordance with the\r\n[scikit-learn estimator API](https://scikit-learn.org/stable/developers/develop.html).\r\nThis means that the classifier can be used as any other (binary and multiclass)\r\nscikit-learn classifier and combined with transforms through\r\n[Pipelines](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html).\r\nIn addition, the `VariationalClassifier` makes use of\r\n[PyTorch](https://pytorch.org/docs/stable/tensors.html) tensors, optimizers, and loss\r\nfunctions.\r\n\r\n*Limitations in (end-)use: the content of this software package may solely be used for applications that comply with international export control laws.*\r\n\r\n## Documentation\r\n\r\nDocumentation of the `tno.quantum.ml.classifiers.vc` package can be found [here](https://tno-quantum.github.io/ml.classifiers.vc/).\r\n\r\n\r\n## Install\r\n\r\nEasily install the `tno.quantum.ml.classifiers.vc` package using pip:\r\n\r\n```console\r\n$ python -m pip install tno.quantum.ml.classifiers.vc\r\n```\r\n\r\nIf you wish to run the tests you can use:\r\n```console\r\n$ python -m pip install 'tno.quantum.ml.classifiers.vc[tests]'\r\n```\r\n\r\n## Example\r\n\r\nHere's an example of how the `VariationalClassifier` class can be used for\r\nclassification based on the\r\n[Iris dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set):\r\nNote that `tno.quantum.ml.datasets` is required for this example.\r\n\r\n```python\r\nfrom tno.quantum.ml.classifiers.vc import VariationalClassifier\r\nfrom tno.quantum.ml.datasets import get_iris_dataset\r\n\r\nX_training, y_training, X_validation, y_validation = get_iris_dataset()\r\nvc = VariationalClassifier()\r\nvc = vc.fit(X_training, y_training)\r\npredictions_validation = vc.predict(X_validation)\r\n```\r\n",
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