log_likelyhood

log_likelyhood(t, coincidences, accidentals, m, prediction)

This is the log likelyhood function. It used in bayesian tomography.

Parameters:
  • t : ndarray

    T values of the current predicted state.

  • coincidences : ndarray with length = number of measurements or shape = (number of measurements, 2^numQubits) for 2 det/qubit

    The counts of the tomography.

  • accidentals : ndarray with length = number of measurements or shape = (number of measurements, 2^numQubits) for 2 det/qubit

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

  • m : ndarray with shape = (2^numQubits, 2^numQubits, number of measurements)

    The measurements of the tomography in density matrix form.

  • prediction : ndarray

    Predicted counts from the predicted state.

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

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

Returns:
  • val : float

    value of the optimization function.


Contact

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