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.

Inference

Algorithms for inference.

QBayesian

Implements a quantum Bayesian inference (QBI) algorithm that has been developed in [1].

Regressors

Algorithms for data regression.

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.