Microsoft.Quantum.MachineLearning namespace

This namespace provides functions and operations used in quantum machine learning applications.

Description

To learn more about this quantum machine learning library, see Introduction to the Quantum Machine Learning Library in Q# documentation.

Operations

Name	Summary
ApplySequentialClassifier	Given the structure and parameterization of a sequential classifier, applies the classifier to a register of qubits.
EstimateClassificationProbabilities	Given a set of samples and a sequential classifier, estimates the classification probability for those samples by repeatedly measuring the output of the classifier on each sample.
EstimateClassificationProbability	Given a sample and a sequential classifier, estimates the classification probability for that sample by repeatedly measuring the output of the classifier on the given sample.
EstimateGradient	Estimates the training gradient for a sequential classifier at a particular model and for a given encoded input.
TrainSequentialClassifier	Given the structure of a sequential classifier, trains the classifier on a given labeled training set.
TrainSequentialClassifierAtModel	Given the structure of a sequential classifier, trains the classifier on a given labeled training set, starting from a particular model.
ValidateSequentialClassifier	Validates a given sequential classifier against a given set of pre-labeled samples.

Functions

Name	Summary
ApproximateInputEncoder	Given a set of coefficients and a tolerance, returns a state preparation operation that prepares each coefficient as the corresponding amplitude of a computational basis state, up to the given tolerance.
CombinedStructure	Given one or more layers of controlled rotations, returns a single layer with model parameter index shifted such that distinct layers are parameterized by distinct model parameters.
CyclicEntanglingLayer	Returns an array of singly controlled rotations along a given axis, arranged cyclically across a register of qubits, and parameterized by distinct model parameters.
DefaultTrainingOptions	Returns a default set of options for training classifiers.
FeatureRegisterSize	Returns the number of qubits required to encode a particular feature vector.
InferredLabel	Given a of classification probability and a bias, returns the label inferred from that probability.
InferredLabels	Given an array of classification probabilities and a bias, returns the label inferred from each probability.
InputEncoder	Given a set of coefficients and a tolerance, returns a state preparation operation that prepares each coefficient as the corresponding amplitude of a computational basis state.
LocalRotationsLayer	Returns an array of uncontrolled (single-qubit) rotations along a given axis, with one rotation for each qubit in a register, parameterized by distinct model parameters.
Misclassifications	Given a set of inferred labels and a set of correct labels, returns indices for where each set of labels differs.
NMisclassifications	Given a set of inferred labels and a set of correct labels, returns the number of indices at which each set of labels differ.
NQubitsRequired	Returns the number of qubits required to apply a given sequential classifier.
PartialRotationsLayer	Returns an array of single-qubit rotations along a given axis, parameterized by distinct model parameters.
Sampled	Samples a given array, using the given schedule.
ScheduleLength	Returns the number of elements in a given sampling schedule.
_Features
_Label

User-defined types

Name	Summary
ControlledRotation	Describes a controlled rotation in terms of its target and control indices, rotation axis, and index into a model parameter vector.
LabeledSample	A sample, labeled with a class to which that sample belongs.
SamplingSchedule	A schedule for drawing batches from a set of samples.
SequentialModel	Describes a quantum classifier model composed of a sequence of parameterized and controlled rotations, an assignment of rotation angles, and a bias between the two classes recognized by the model.
StateGenerator	Describes an operation that prepares a given input to a sequential classifier.
TrainingOptions	A collection of options to be used in training quantum classifiers.
ValidationResults	The results from having validated a classifier against a set of samples.