QuantumKernel.evaluate

QuantumKernel.evaluate(x_vec, y_vec=None)

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.

मापदण्ड:

• 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

प्रदत्त प्रकार :

ndarray

प्रदत्त :

2D matrix, NxM

उभारता है :

• QiskitMachineLearningError --

  • A quantum instance or backend has not been provided

• ValueError --

  • unbound training parameters in the feature map circuit - 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.