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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
  • QiskitMachineLearningError

    • 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.