def test_pauli_expect_op_vector(self):
""" pauli expect op vector test """
paulis_op = ListOp([X, Y, Z, I])
converted_meas = self.expect.convert(~StateFn(paulis_op))
plus_mean = (converted_meas @ Plus)
np.testing.assert_array_almost_equal(plus_mean.eval(), [1, 0, 0, 1],
decimal=1)
sampled_plus = self.sampler.convert(plus_mean)
np.testing.assert_array_almost_equal(sampled_plus.eval(), [1, 0, 0, 1],
decimal=1)
minus_mean = (converted_meas @ Minus)
np.testing.assert_array_almost_equal(minus_mean.eval(), [-1, 0, 0, 1],
decimal=1)
sampled_minus = self.sampler.convert(minus_mean)
np.testing.assert_array_almost_equal(sampled_minus.eval(),
[-1, 0, 0, 1],
decimal=1)
zero_mean = (converted_meas @ Zero)
np.testing.assert_array_almost_equal(zero_mean.eval(), [0, 0, 1, 1],
decimal=1)
sampled_zero = self.sampler.convert(zero_mean)
np.testing.assert_array_almost_equal(sampled_zero.eval(), [0, 0, 1, 1],
decimal=1)
sum_zero = (Plus + Minus) * (.5**.5)
sum_zero_mean = (converted_meas @ sum_zero)
np.testing.assert_array_almost_equal(sum_zero_mean.eval(),
[0, 0, 1, 1],
decimal=1)
sampled_zero_mean = self.sampler.convert(sum_zero_mean)
# !!NOTE!!: Depolarizing channel (Sampling) means interference
# does not happen between circuits in sum, so expectation does
# not equal expectation for Zero!!
np.testing.assert_array_almost_equal(sampled_zero_mean.eval(),
[0, 0, 0, 2],
decimal=1)
for i, op in enumerate(paulis_op.oplist):
mat_op = op.to_matrix()
np.testing.assert_array_almost_equal(
zero_mean.eval()[i],
Zero.adjoint().to_matrix() @ mat_op @ Zero.to_matrix(),
decimal=1)
np.testing.assert_array_almost_equal(
plus_mean.eval()[i],
Plus.adjoint().to_matrix() @ mat_op @ Plus.to_matrix(),
decimal=1)
np.testing.assert_array_almost_equal(
minus_mean.eval()[i],
Minus.adjoint().to_matrix() @ mat_op @ Minus.to_matrix(),
decimal=1)
def test_pauli_expect_op_vector(self):
""" pauli expect op vector test """
paulis_op = ListOp([X, Y, Z, I])
converted_meas = self.expect.convert(~StateFn(paulis_op))
plus_mean = (converted_meas @ Plus)
np.testing.assert_array_almost_equal(plus_mean.eval(), [1, 0, 0, 1],
decimal=1)
sampled_plus = self.sampler.convert(plus_mean)
np.testing.assert_array_almost_equal(sampled_plus.eval(), [1, 0, 0, 1],
decimal=1)
minus_mean = (converted_meas @ Minus)
np.testing.assert_array_almost_equal(minus_mean.eval(), [-1, 0, 0, 1],
decimal=1)
sampled_minus = self.sampler.convert(minus_mean)
np.testing.assert_array_almost_equal(sampled_minus.eval(),
[-1, 0, 0, 1],
decimal=1)
zero_mean = (converted_meas @ Zero)
np.testing.assert_array_almost_equal(zero_mean.eval(), [0, 0, 1, 1],
decimal=1)
sampled_zero = self.sampler.convert(zero_mean)
np.testing.assert_array_almost_equal(sampled_zero.eval(), [0, 0, 1, 1],
decimal=1)
sum_zero = (Plus + Minus) * (.5**.5)
sum_zero_mean = (converted_meas @ sum_zero)
np.testing.assert_array_almost_equal(sum_zero_mean.eval(),
[0, 0, 1, 1],
decimal=1)
sampled_zero = self.sampler.convert(sum_zero)
np.testing.assert_array_almost_equal(
(converted_meas @ sampled_zero).eval(), [0, 0, 1, 1], decimal=1)
for i, op in enumerate(paulis_op.oplist):
mat_op = op.to_matrix()
np.testing.assert_array_almost_equal(
zero_mean.eval()[i],
Zero.adjoint().to_matrix() @ mat_op @ Zero.to_matrix(),
decimal=1)
np.testing.assert_array_almost_equal(
plus_mean.eval()[i],
Plus.adjoint().to_matrix() @ mat_op @ Plus.to_matrix(),
decimal=1)
np.testing.assert_array_almost_equal(
minus_mean.eval()[i],
Minus.adjoint().to_matrix() @ mat_op @ Minus.to_matrix(),
decimal=1)
def test_state_to_matrix(self):
""" state to matrix test """
np.testing.assert_array_equal(Zero.to_matrix(), np.array([1, 0]))
np.testing.assert_array_equal(One.to_matrix(), np.array([0, 1]))
np.testing.assert_array_almost_equal(Plus.to_matrix(),
(Zero.to_matrix() + One.to_matrix()) / (np.sqrt(2)))
np.testing.assert_array_almost_equal(Minus.to_matrix(),
(Zero.to_matrix() - One.to_matrix()) / (np.sqrt(2)))
# TODO Not a great test because doesn't test against validated values
# or test internal representation. Fix this.
gnarly_state = (One ^ Plus ^ Zero ^ Minus * .3) @ \
StateFn(Statevector.from_label('r0+l')) + (StateFn(X ^ Z ^ Y ^ I) * .1j)
gnarly_mat = gnarly_state.to_matrix()
gnarly_mat_separate = (One ^ Plus ^ Zero ^ Minus * .3).to_matrix()
gnarly_mat_separate = np.dot(gnarly_mat_separate,
StateFn(Statevector.from_label('r0+l')).to_matrix())
gnarly_mat_separate = gnarly_mat_separate + (StateFn(X ^ Z ^ Y ^ I) * .1j).to_matrix()
np.testing.assert_array_almost_equal(gnarly_mat, gnarly_mat_separate)