TNO Quantum

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

VC

VC is part of TNO Quantum and provides a Python implementation of a Variational Quantum Classifier.

The implementation (see the VariationalClassifier class) has been done in accordance with the scikit-learn estimator API, which 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.

Quick Install

VC can be installed using pip as follows:

pip install tno.quantum.ml.classifiers.vc

Optionally (though required for the examples below), you can install the test datasets:

pip install tno.quantum.ml.datasets

Example

Here’s an example of how the VariationalClassifier class can be used for classification based on the Iris dataset:

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)

More elaborate examples can be found in the examples repository.

Backends

Internally, the VariationalClassifier class makes use of hybrid classical/quantum models to represent a function to be learned. These quantum models give rise to circuits which can be run on different backends, such as quantum hardware and quantum simulators.

Backends are selected by closely following the PennyLane interface for specifying devices. As a result, PennyLane plugins are also supported. Here’s an example showcasing how Quantum Inspire hardware can be used through the PennyLane-QuantumInspire plugin (note that you will need to install the plugin as described here):

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

X_training, y_training, X_validation, y_validation = get_wine_dataset(
    n_features=5, n_classes=3, random_seed=0
)
vc = VariationalClassifier(
    backend={
        "name": "quantuminspire.qi",
        "options": {"backend": "Starmon-5", "project": "my project"},
    },
)
vc = vc.fit(X_training, y_training)
predictions_validation = vc.predict(X_validation)

As an alternative example, here’s how the default.qubit simulator provided by PennyLane can be specified by the backend keyword:

backend={"name": "default.qubit", "options": {}}

Quantum Models

A quantum model defines a quantum circuit and a strategy for classical post-processing of measurements. Currently, three models are available:

The "expected_values_model", as implemented by the ExpectedValuesModel class (see get_qfunc() for details on the post-processing strategy).
The "modulo_model", as implemented by the ModuloModel class.
The "parity_model", as implemented by the ParityModel class.

For example, the "modulo_model" can be specified with the model keyword as follows:

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(
    n_features=4, n_classes=3, random_seed=0
)
vc = VariationalClassifier(
    model={
        "name": "modulo_model",
        "options": {"n_layers": 2, "n_trainable_sublayers": 2, "scaling": 0.5},
    },
)
vc = vc.fit(X_training, y_training)
predictions_validation = vc.predict(X_validation)

Developing New Quantum Models

Quantum models are developed using PennyLane and can be found in the models module. New models can be implemented by extending from QModel and adding them to get_model().

Support for PyTorch Optimizers

The currently supported optimizers can be found in the optimizers module. Other PyTorch optimizers can be added in get_optimizer().

Contents:

vc package

(End)use Limitations

The content of this software may solely be used for applications that comply with international export control laws.