# QuantumKernel.evaluate¶

QuantumKernel.evaluate(x_vec, y_vec=None)[source]

Construct kernel matrix for given data and feature map

If y_vec is None, self inner product is calculated. If using statevector_simulator, only build circuits for $$\Psi(x)|0\rangle$$, then perform inner product classically.

Parameters
• x_vec (ndarray) – 1D or 2D array of datapoints, NxD, where N is the number of datapoints, D is the feature dimension

• y_vec (Optional[ndarray]) – 1D or 2D array of datapoints, MxD, where M is the number of datapoints, D is the feature dimension

Return type

ndarray

Returns

2D matrix, NxM

Raises
• A quantum instance or backend has not been provided

• ValueError

• x_vec and/or y_vec are not one or two dimensional arrays - x_vec and y_vec have have incompatible dimensions - x_vec and/or y_vec have incompatible dimension with feature map and and feature map can not be modified to match.