def compose(self, other, qargs=None, front=False):
"""Return the composition channel self∘other.
Args:
other (SparsePauliOp): an operator object.
qargs (list or None): a list of subsystem positions to compose other on.
front (bool or None): If False compose in standard order other(self(input))
otherwise compose in reverse order self(other(input))
[default: False]
Returns:
SparsePauliOp: The composed operator.
Raises:
QiskitError: if other cannot be converted to an Operator or has
incompatible dimensions.
"""
# pylint: disable=invalid-name
if qargs is None:
qargs = getattr(other, 'qargs', None)
if not isinstance(other, SparsePauliOp):
other = SparsePauliOp(other)
# Validate composition dimensions and qargs match
self._op_shape.compose(other._op_shape, qargs, front)
# Implement composition of the Pauli table
x1, x2 = PauliTable._block_stack(self.table.X, other.table.X)
z1, z2 = PauliTable._block_stack(self.table.Z, other.table.Z)
c1, c2 = PauliTable._block_stack(self.coeffs, other.coeffs)
if qargs is not None:
ret_x, ret_z = x1.copy(), z1.copy()
x1 = x1[:, qargs]
z1 = z1[:, qargs]
ret_x[:, qargs] = x1 ^ x2
ret_z[:, qargs] = z1 ^ z2
table = np.hstack([ret_x, ret_z])
else:
table = np.hstack((x1 ^ x2, z1 ^ z2))
# Take product of coefficients and add phase correction
coeffs = c1 * c2
# We pick additional phase terms for the products
# X.Y = i * Z, Y.Z = i * X, Z.X = i * Y
# Y.X = -i * Z, Z.Y = -i * X, X.Z = -i * Y
if front:
plus_i = (x1 & ~z1 & x2 & z2) | (x1 & z1 & ~x2 & z2) | (~x1 & z1
& x2 & ~z2)
minus_i = (x2 & ~z2 & x1 & z1) | (x2 & z2 & ~x1
& z1) | (~x2 & z2 & x1 & ~z1)
else:
minus_i = (x1 & ~z1 & x2 & z2) | (x1 & z1 & ~x2
& z2) | (~x1 & z1 & x2 & ~z2)
plus_i = (x2 & ~z2 & x1 & z1) | (x2 & z2 & ~x1 & z1) | (~x2 & z2
& x1 & ~z1)
coeffs *= 1j**np.array(np.sum(plus_i, axis=1), dtype=int)
coeffs *= (-1j)**np.array(np.sum(minus_i, axis=1), dtype=int)
return SparsePauliOp(table, coeffs)
def compose(self, other, qargs=None, front=False):
if qargs is None:
qargs = getattr(other, "qargs", None)
if not isinstance(other, SparsePauliOp):
other = SparsePauliOp(other)
# Validate composition dimensions and qargs match
self._op_shape.compose(other._op_shape, qargs, front)
# Implement composition of the Pauli table
x1, x2 = PauliTable._block_stack(self.table.X, other.table.X)
z1, z2 = PauliTable._block_stack(self.table.Z, other.table.Z)
c1, c2 = PauliTable._block_stack(self.coeffs, other.coeffs)
if qargs is not None:
ret_x, ret_z = x1.copy(), z1.copy()
x1 = x1[:, qargs]
z1 = z1[:, qargs]
ret_x[:, qargs] = x1 ^ x2
ret_z[:, qargs] = z1 ^ z2
table = np.hstack([ret_x, ret_z])
else:
table = np.hstack((x1 ^ x2, z1 ^ z2))
# Take product of coefficients and add phase correction
coeffs = c1 * c2
# We pick additional phase terms for the products
# X.Y = i * Z, Y.Z = i * X, Z.X = i * Y
# Y.X = -i * Z, Z.Y = -i * X, X.Z = -i * Y
if front:
plus_i = (x1 & ~z1 & x2 & z2) | (x1 & z1 & ~x2 & z2) | (~x1 & z1
& x2 & ~z2)
minus_i = (x2 & ~z2 & x1 & z1) | (x2 & z2 & ~x1
& z1) | (~x2 & z2 & x1 & ~z1)
else:
minus_i = (x1 & ~z1 & x2 & z2) | (x1 & z1 & ~x2
& z2) | (~x1 & z1 & x2 & ~z2)
plus_i = (x2 & ~z2 & x1 & z1) | (x2 & z2 & ~x1 & z1) | (~x2 & z2
& x1 & ~z1)
coeffs *= 1j**np.array(np.sum(plus_i, axis=1), dtype=int)
coeffs *= (-1j)**np.array(np.sum(minus_i, axis=1), dtype=int)
return SparsePauliOp(table, coeffs)
