# 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. |

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