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Quantum machine learning algorithms (qiskit_machine_learning.algorithms)

The package contains core algorithms such as classifiers and classifiers.

Machine Learning Base Classes

TrainableModel

Base class for ML model that defines a scikit-learn like interface for Estimators.

ObjectiveFunction

An abstract objective function.

SerializableModelMixin

Provides convenient methods for saving and loading models.

Machine Learning Objective Functions

BinaryObjectiveFunction

An objective function for binary representation of the output.

MultiClassObjectiveFunction

An objective function for multiclass representation of the output.

OneHotObjectiveFunction

An objective function for one hot encoding representation of the output.

Algorithms

Classifiers

Algorithms for data classification.

PegasosQSVC

Implements Pegasos Quantum Support Vector Classifier algorithm.

QSVC

Quantum Support Vector Classifier that extends the scikit-learn sklearn.svm.SVC classifier and introduces an additional quantum_kernel parameter.

NeuralNetworkClassifier

Implements a basic quantum neural network classifier.

VQC

A convenient Variational Quantum Classifier implementation.

Regressors

Quantum Support Vector Regressor.

QSVR

Quantum Support Vector Regressor that extends the scikit-learn sklearn.svm.SVR regressor and introduces an additional quantum_kernel parameter.

NeuralNetworkRegressor

Implements a basic quantum neural network regressor.

VQR

A convenient Variational Quantum Regressor implementation.

Distribution Learners

DiscriminativeNetwork

Base class for discriminative Quantum or Classical Neural Networks.

GenerativeNetwork

Base class for generative Quantum and Classical Neural Networks.

NumPyDiscriminator

Discriminator based on NumPy

PyTorchDiscriminator

Discriminator based on PyTorch

QuantumGenerator

Quantum Generator.

QGAN

The Quantum Generative Adversarial Network algorithm.