qiskit/quantum_info/operators/pauli.py
def to_spmatrix(self):
        r"""
        Convert Pauli to a sparse matrix representation (CSR format).

        Order is q_{n-1} .... q_0, i.e., $P_{n-1} \otimes ... P_0$

        Returns:
            scipy.sparse.csr_matrix: a sparse matrix with CSR format that
            represnets the pauli.
        """
        mat = sparse.coo_matrix(1)
        for z, x in zip(self._z, self._x):
            if not z and not x:  # I
                mat = sparse.bmat([[mat, None], [None, mat]], format='coo')
            elif z and not x:  # Z
                mat = sparse.bmat([[mat, None], [None, -mat]], format='coo')
            elif not z and x:  # X
                mat = sparse.bmat([[None, mat], [mat, None]], format='coo')
            else:  # Y
                mat = mat * 1j
                mat = sparse.bmat([[None, -mat], [mat, None]], format='coo')

        return mat.tocsr()
def to_spmatrix(self):
        r"""
        Convert Pauli to a sparse matrix representation (CSR format).

        Order is q_{n-1} .... q_0, i.e., $P_{n-1} \otimes ... P_0$

        Returns:
            scipy.sparse.csr_matrix: a sparse matrix with CSR format that
            represnets the pauli.
        """
        mat = sparse.coo_matrix(1)
        for z, x in zip(self._z, self._x):
            if not z and not x:  # I
                mat = sparse.bmat([[mat, None], [None, mat]], format='coo')
            elif z and not x:  # Z
                mat = sparse.bmat([[mat, None], [None, -mat]], format='coo')
            elif not z and x:  # X
                mat = sparse.bmat([[None, mat], [mat, None]], format='coo')
            else:  # Y
                mat = mat * 1j
                mat = sparse.bmat([[None, -mat], [mat, None]], format='coo')

        return mat.tocsr()
def to_spmatrix ( self ) : mat = sparse . coo_matrix ( 1 ) for z , x in zip ( self . _z , self . _x ) : if not z and not x : # I mat = sparse . bmat ( [ [ mat , None ] , [ None , mat ] ] , format = 'coo' ) elif z and not x : # Z mat = sparse . bmat ( [ [ mat , None ] , [ None , - mat ] ] , format = 'coo' ) elif not z and x : # X mat = sparse . bmat ( [ [ None , mat ] , [ mat , None ] ] , format = 'coo' ) else : # Y mat = mat * 1j mat = sparse . bmat ( [ [ None , - mat ] , [ mat , None ] ] , format = 'coo' ) return mat . tocsr ( )
r"""
        Convert Pauli to a sparse matrix representation (CSR format).

        Order is q_{n-1} .... q_0, i.e., $P_{n-1} \otimes ... P_0$

        Returns:
            scipy.sparse.csr_matrix: a sparse matrix with CSR format that
            represnets the pauli.
r Convert Pauli to a sparse matrix representation ( CSR format ) .