qiskit/tools/qi/qi.py
def vectorize(density_matrix, method='col'):
    """Flatten an operator to a vector in a specified basis.

    Args:
        density_matrix (ndarray): a density matrix.
        method (str): the method of vectorization. Allowed values are
            - 'col' (default) flattens to column-major vector.
            - 'row' flattens to row-major vector.
            - 'pauli'flattens in the n-qubit Pauli basis.
            - 'pauli-weights': flattens in the n-qubit Pauli basis ordered by
               weight.

    Returns:
        ndarray: the resulting vector.
    Raises:
        Exception: if input state is not a n-qubit state
    """
    density_matrix = np.array(density_matrix)
    if method == 'col':
        return density_matrix.flatten(order='F')
    elif method == 'row':
        return density_matrix.flatten(order='C')
    elif method in ['pauli', 'pauli_weights']:
        num = int(np.log2(len(density_matrix)))  # number of qubits
        if len(density_matrix) != 2**num:
            raise Exception('Input state must be n-qubit state')
        if method == 'pauli_weights':
            pgroup = pauli_group(num, case='weight')
        else:
            pgroup = pauli_group(num, case='tensor')
        vals = [np.trace(np.dot(p.to_matrix(), density_matrix))
                for p in pgroup]
        return np.array(vals)
    return None
def vectorize(density_matrix, method='col'):
    density_matrix = np.array(density_matrix)
    if method == 'col':
        return density_matrix.flatten(order='F')
    elif method == 'row':
        return density_matrix.flatten(order='C')
    elif method in ['pauli', 'pauli_weights']:
        num = int(np.log2(len(density_matrix)))  # number of qubits
        if len(density_matrix) != 2**num:
            raise Exception('Input state must be n-qubit state')
        if method == 'pauli_weights':
            pgroup = pauli_group(num, case='weight')
        else:
            pgroup = pauli_group(num, case='tensor')
        vals = [np.trace(np.dot(p.to_matrix(), density_matrix))
                for p in pgroup]
        return np.array(vals)
    return None
def vectorize ( density_matrix , method = 'col' ) : density_matrix = np . array ( density_matrix ) if method == 'col' : return density_matrix . flatten ( order = 'F' ) elif method == 'row' : return density_matrix . flatten ( order = 'C' ) elif method in [ 'pauli' , 'pauli_weights' ] : num = int ( np . log2 ( len ( density_matrix ) ) ) # number of qubits if len ( density_matrix ) != 2 ** num : raise Exception ( 'Input state must be n-qubit state' ) if method == 'pauli_weights' : pgroup = pauli_group ( num , case = 'weight' ) else : pgroup = pauli_group ( num , case = 'tensor' ) vals = [ np . trace ( np . dot ( p . to_matrix ( ) , density_matrix ) ) for p in pgroup ] return np . array ( vals ) return None
Flatten an operator to a vector in a specified basis.

    Args:
        density_matrix (ndarray): a density matrix.
        method (str): the method of vectorization. Allowed values are
            - 'col' (default) flattens to column-major vector.
            - 'row' flattens to row-major vector.
            - 'pauli'flattens in the n-qubit Pauli basis.
            - 'pauli-weights': flattens in the n-qubit Pauli basis ordered by
               weight.

    Returns:
        ndarray: the resulting vector.
    Raises:
        Exception: if input state is not a n-qubit state
Flatten an operator to a vector in a specified basis .