def test_expval(self):
"""Test expectation_value method"""
psi = Statevector([1, 0, 0, 1]) / np.sqrt(2)
rho = DensityMatrix(psi)
for label, target in [('II', 1), ('XX', 1), ('YY', -1), ('ZZ', 1),
('IX', 0), ('YZ', 0), ('ZX', 0), ('YI', 0)]:
with self.subTest(msg="<{}>".format(label)):
op = Pauli(label)
expval = rho.expectation_value(op)
self.assertAlmostEqual(expval, target)
psi = Statevector([np.sqrt(2), 0, 0, 0, 0, 0, 0, 1 + 1j]) / 2
rho = DensityMatrix(psi)
for label, target in [('XXX', np.sqrt(2) / 2),
('YYY', -np.sqrt(2) / 2), ('ZZZ', 0), ('XYZ', 0),
('YIY', 0)]:
with self.subTest(msg="<{}>".format(label)):
op = Pauli(label)
expval = rho.expectation_value(op)
self.assertAlmostEqual(expval, target)
labels = ['XXX', 'IXI', 'YYY', 'III']
coeffs = [3.0, 5.5, -1j, 23]
spp_op = SparsePauliOp.from_list(list(zip(labels, coeffs)))
expval = rho.expectation_value(spp_op)
target = 25.121320343559642 + 0.7071067811865476j
self.assertAlmostEqual(expval, target)
def _paulivec_data(state):
"""Return paulivec data for plotting.
Args:
state (DensityMatrix or Statevector): an N-qubit state.
Returns:
tuple: (labels, values) for Pauli vector.
Raises:
VisualizationError: if input is not an N-qubit state.
"""
rho = SparsePauliOp.from_operator(DensityMatrix(state))
if rho.num_qubits is None:
raise VisualizationError("Input is not a multi-qubit quantum state.")
return rho.paulis.to_labels(), np.real(rho.coeffs)
def test_expval(self):
"""Test expectation_value method"""
psi = Statevector([1, 0, 0, 1]) / np.sqrt(2)
rho = DensityMatrix(psi)
for label, target in [
("II", 1),
("XX", 1),
("YY", -1),
("ZZ", 1),
("IX", 0),
("YZ", 0),
("ZX", 0),
("YI", 0),
]:
with self.subTest(msg=f"<{label}>"):
op = Pauli(label)
expval = rho.expectation_value(op)
self.assertAlmostEqual(expval, target)
psi = Statevector([np.sqrt(2), 0, 0, 0, 0, 0, 0, 1 + 1j]) / 2
rho = DensityMatrix(psi)
for label, target in [
("XXX", np.sqrt(2) / 2),
("YYY", -np.sqrt(2) / 2),
("ZZZ", 0),
("XYZ", 0),
("YIY", 0),
]:
with self.subTest(msg=f"<{label}>"):
op = Pauli(label)
expval = rho.expectation_value(op)
self.assertAlmostEqual(expval, target)
labels = ["XXX", "IXI", "YYY", "III"]
coeffs = [3.0, 5.5, -1j, 23]
spp_op = SparsePauliOp.from_list(list(zip(labels, coeffs)))
expval = rho.expectation_value(spp_op)
target = 25.121320343559642 + 0.7071067811865476j
self.assertAlmostEqual(expval, target)