Quantum machine learning algorithms


The package contains core algorithms such as classifiers and classifiers.

Machine Learning Base Classes

TrainableModelBase class for ML model that defines a scikit-learn like interface for Estimators.
ObjectiveFunctionAn abstract objective function.
SerializableModelMixinProvides convenient methods for saving and loading models.

Machine Learning Objective Functions

BinaryObjectiveFunctionAn objective function for binary representation of the output.
MultiClassObjectiveFunctionAn objective function for multiclass representation of the output.
OneHotObjectiveFunctionAn objective function for one hot encoding representation of the output.



Algorithms for data classification.

PegasosQSVCImplements Pegasos Quantum Support Vector Classifier algorithm.
QSVCQuantum Support Vector Classifier that extends the scikit-learn sklearn.svm.SVC classifier and introduces an additional quantum_kernel parameter.
NeuralNetworkClassifierImplements a basic quantum neural network classifier.
VQCA convenient Variational Quantum Classifier implementation.


Quantum Support Vector Regressor.

QSVRQuantum Support Vector Regressor that extends the scikit-learn sklearn.svm.SVR regressor and introduces an additional quantum_kernel parameter.
NeuralNetworkRegressorImplements a basic quantum neural network regressor.
VQRA convenient Variational Quantum Regressor implementation.

Distribution Learners

DiscriminativeNetworkBase class for discriminative Quantum or Classical Neural Networks.
GenerativeNetworkBase class for generative Quantum and Classical Neural Networks.
NumPyDiscriminatorDiscriminator based on NumPy
PyTorchDiscriminatorDiscriminator based on PyTorch
QuantumGeneratorQuantum Generator.