# QuantumKernel¶

class QuantumKernel(feature_map=None, enforce_psd=True, batch_size=900, quantum_instance=None, user_parameters=None)[소스]

기반 클래스: object

Quantum Kernel.

The general task of machine learning is to find and study patterns in data. For many algorithms, the datapoints are better understood in a higher dimensional feature space, through the use of a kernel function:

$K(x, y) = \langle f(x), f(y)\rangle.$

Here K is the kernel function, x, y are n dimensional inputs. f is a map from n-dimension to m-dimension space. $$\langle x, y \rangle$$ denotes the dot product. Usually m is much larger than n.

The quantum kernel algorithm calculates a kernel matrix, given datapoints x and y and feature map f, all of n dimension. This kernel matrix can then be used in classical machine learning algorithms such as support vector classification, spectral clustering or ridge regression.

매개변수

Attributes

 feature_map Return feature map quantum_instance Return quantum instance unbound_feature_map Return unbound feature map user_param_binds Return a copy of the current user parameter mappings for the feature map circuit. user_parameters Return the vector of user parameters.

Methods

 assign_user_parameters(values) Assign user parameters in the QuantumKernel feature map. bind_user_parameters(values) Alternate function signature for assign_user_parameters construct_circuit(x[, y, measurement, ...]) Construct inner product circuit for given datapoints and feature map. evaluate(x_vec[, y_vec]) Construct kernel matrix for given data and feature map Return a list of any unbound user parameters in the feature map circuit.