qiskit/tools/qcvv/tomography.py
def fit_tomography_data(tomo_data, method='wizard', options=None):
    """
    Reconstruct a density matrix or process-matrix from tomography data.

    If the input data is state_tomography_data the returned operator will
    be a density matrix. If the input data is process_tomography_data the
    returned operator will be a Choi-matrix in the column-vectorization
    convention.

    Args:
        tomo_data (dict): process tomography measurement data.
        method (str): the fitting method to use.
            Available methods:
                - 'wizard' (default)
                - 'leastsq'
        options (dict or None): additional options for fitting method.

    Returns:
        numpy.array: The fitted operator.

    Available methods:
        - 'wizard' (Default): The returned operator will be constrained to be
                              positive-semidefinite.
            Options:
            - 'trace': the trace of the returned operator.
                       The default value is 1.
            - 'beta': hedging parameter for computing frequencies from
                      zero-count data. The default value is 0.50922.
            - 'epsilon: threshold for truncating small eigenvalues to zero.
                        The default value is 0
        - 'leastsq': Fitting without positive-semidefinite constraint.
            Options:
            - 'trace': Same as for 'wizard' method.
            - 'beta': Same as for 'wizard' method.
    Raises:
        Exception: if the `method` parameter is not valid.
    """

    if isinstance(method, str) and method.lower() in ['wizard', 'leastsq']:
        # get options
        trace = __get_option('trace', options)
        beta = __get_option('beta', options)
        # fit state
        rho = __leastsq_fit(tomo_data, trace=trace, beta=beta)
        if method == 'wizard':
            # Use wizard method to constrain positivity
            epsilon = __get_option('epsilon', options)
            rho = __wizard(rho, epsilon=epsilon)
        return rho
    else:
        raise Exception('Invalid reconstruction method "%s"' % method)
def fit_tomography_data(tomo_data, method='wizard', options=None):
    if isinstance(method, str) and method.lower() in ['wizard', 'leastsq']:
        # get options
        trace = __get_option('trace', options)
        beta = __get_option('beta', options)
        # fit state
        rho = __leastsq_fit(tomo_data, trace=trace, beta=beta)
        if method == 'wizard':
            # Use wizard method to constrain positivity
            epsilon = __get_option('epsilon', options)
            rho = __wizard(rho, epsilon=epsilon)
        return rho
    else:
        raise Exception('Invalid reconstruction method "%s"' % method)
def fit_tomography_data ( tomo_data , method = 'wizard' , options = None ) : if isinstance ( method , str ) and method . lower ( ) in [ 'wizard' , 'leastsq' ] : # get options trace = __get_option ( 'trace' , options ) beta = __get_option ( 'beta' , options ) # fit state rho = __leastsq_fit ( tomo_data , trace = trace , beta = beta ) if method == 'wizard' : # Use wizard method to constrain positivity epsilon = __get_option ( 'epsilon' , options ) rho = __wizard ( rho , epsilon = epsilon ) return rho else : raise Exception ( 'Invalid reconstruction method "%s"' % method )
Reconstruct a density matrix or process-matrix from tomography data.

    If the input data is state_tomography_data the returned operator will
    be a density matrix. If the input data is process_tomography_data the
    returned operator will be a Choi-matrix in the column-vectorization
    convention.

    Args:
        tomo_data (dict): process tomography measurement data.
        method (str): the fitting method to use.
            Available methods:
                - 'wizard' (default)
                - 'leastsq'
        options (dict or None): additional options for fitting method.

    Returns:
        numpy.array: The fitted operator.

    Available methods:
        - 'wizard' (Default): The returned operator will be constrained to be
                              positive-semidefinite.
            Options:
            - 'trace': the trace of the returned operator.
                       The default value is 1.
            - 'beta': hedging parameter for computing frequencies from
                      zero-count data. The default value is 0.50922.
            - 'epsilon: threshold for truncating small eigenvalues to zero.
                        The default value is 0
        - 'leastsq': Fitting without positive-semidefinite constraint.
            Options:
            - 'trace': Same as for 'wizard' method.
            - 'beta': Same as for 'wizard' method.
    Raises:
        Exception: if the `method` parameter is not valid.
Reconstruct a density matrix or process - matrix from tomography data .