Tomography.tomography_HMLE

Tomography.tomography_HMLE(starting_matrix, coincidences, measurements, accidentals,overall_norms)

Runs tomography using hedged maximum likelyhood estimation.

Parameters:
  • starting_matrix : ndarray with shape = (2^numQubits, 2^numQubits)

    The starting predicted state.

  • coincidences : ndarray shape = (Number of measurements, NDetectors**NQubits)

    The counts of the tomography.

  • measurements_densities : ndarray with shape = (NDetectors*number of measurements,2^numQubits, 2^numQubits)

    The measurements of the tomography in density matrix form.

  • accidentals : 1darray with length = number of measurements or length = number of measurements * 2^numQubits for 2 det/qubit

    The accidental values of the tomography. Used for accidental correction.

  • overall_norms : 1darray with length = number of measurements or length = number of measurements * 2^numQubits for 2 det/qubit

    The relative weights of each measurment. Used for drift correction.

Returns:
  • rhog : ndarray with shape = (2^numQubits, 2^numQubits)

    The predicted density matrix.

  • intensity : float

    The predicted overall intensity used to normalize the state.

  • fvalp : float

    Final value of the internal optimization function. Values greater than the number of measurements indicate poor agreement with a quantum state.


Contact

In case you have any further questions about the Python code, you should direct them to Scott Turro or Joey Shallat.