iqm.benchmarks.compressive_gst.mgst.low_level_jit.dK_dMdM

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iqm.benchmarks.compressive_gst.mgst.low_level_jit.dK_dMdM#

iqm.benchmarks.compressive_gst.mgst.low_level_jit.dK_dMdM(X: ndarray, K: ndarray, E: ndarray, rho: ndarray, J: ndarray, y: ndarray, mle: bool = False) → tuple[ndarray, ndarray, ndarray]#

Compute first order derivative and products.

Compute the derivatives of the objective function with respect to K and the product of derivatives of the measurement map with respect to K.

Parameters:

  • X (ndarray) – A 3D array containing input matrices, of shape (n_matrices, n_rows, n_columns).

  • K (ndarray) – A 1D array representing the matrix K.

  • E (ndarray) – A 2D array representing the POVM elements, of shape (n_povm, r).

  • rho (ndarray) – A 1D array representing the density matrix.

  • J (ndarray) – A 2D array representing indices for which the objective function will be evaluated.

  • y (ndarray) – A 2D array of shape (n_povm, len(J)) containing target values.

  • mle (bool) – If True, the log-likelihood objective function is used, otherwise the least squares objective function is used

Returns:

A tuple containing the derivatives of K, dM10, and dM11, each of which is a numpy.ndarray.

Return type:

tuple[ndarray, ndarray, ndarray]

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