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) * (0.5**0.5)
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, 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_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) * (0.5**0.5)
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,
)
