def test_iteration_warnings():
"""Test that the correct warning is raised beyond the iteration limit."""
fac = LinearFactory(X_VALS)
with warns(
ConvergenceWarning,
match=r"Factory iteration loop stopped before convergence.",
):
fac.iterate(lambda scale_factor: 1.0, max_iterations=3)
def test_linear_extr():
"""Tests extrapolation with a LinearFactory."""
seeded_f = apply_seed_to_func(f_lin, SEED)
fac = LinearFactory(X_VALS)
assert not fac._opt_params
fac.run_classical(seeded_f)
assert np.isclose(fac.reduce(), seeded_f(0, err=0), atol=CLOSE_TOL)
assert np.allclose(fac._opt_params, [B, A], atol=CLOSE_TOL)
def test_full_output_keyword():
"""Tests the full_output keyword in extrapolate method."""
zlim = LinearFactory.extrapolate([1, 2], [1, 2])
assert np.isclose(zlim, 0.0)
zlim, opt_params = LinearFactory.extrapolate(
[1, 2], [1, 2], full_output=True
)
assert len(opt_params) == 2
assert np.isclose(zlim, 0.0)
assert np.isclose(0.0, opt_params[1])
assert np.isclose(1.0, opt_params[0])
def test_equal_simple():
fac = LinearFactory(scale_factors=[1, 2, 3])
assert fac != 1
copied_fac = copy(fac)
assert copied_fac == fac
copied_fac._already_reduced = True
assert copied_fac != fac
fac._instack = [{"scale_factor": 1, "shots": 100}]
copied_fac = deepcopy(fac)
assert copied_fac == fac
copied_fac._instack[0].update({"shots": 101})
assert copied_fac != fac
def test_full_output_keyword():
"""Tests the full_output keyword in extrapolate method."""
zne_limit = LinearFactory.extrapolate([1, 2], [1, 2])
assert np.isclose(zne_limit, 0.0)
(
zne_limit,
zne_std,
opt_params,
params_cov,
zne_curve,
) = LinearFactory.extrapolate([1, 2], [1, 2], full_output=True)
assert len(opt_params) == 2
assert np.isclose(zne_limit, 0.0)
assert np.isclose(0.0, opt_params[1])
assert np.isclose(1.0, opt_params[0])
assert zne_std is None
assert params_cov is None
assert np.isclose(zne_curve(0), 0.0)
assert np.isclose(zne_curve(2), 2.0)
def test_linear_extr():
"""Tests extrapolation with a LinearFactory."""
seeded_f = apply_seed_to_func(f_lin, SEED)
fac = LinearFactory(X_VALS)
assert not fac._opt_params
fac.run_classical(seeded_f)
zne_value = fac.reduce()
assert np.isclose(zne_value, seeded_f(0, err=0), atol=CLOSE_TOL)
assert np.allclose(fac._opt_params, [B, A], atol=CLOSE_TOL)
exp_vals = fac.get_expectation_values()
assert np.isclose(fac.extrapolate(X_VALS, exp_vals), zne_value)
assert np.isclose(
fac.extrapolate(X_VALS, exp_vals, full_output=True)[0], zne_value,
)
def test_get_methods_of_factories():
"""Tests the get methods of a factory"""
x_values = [0, 0, 1]
y_values = [-1, 1, 0]
fac = LinearFactory(x_values)
fac._instack = [
{"scale_factor": 0},
{"scale_factor": 0},
{"scale_factor": 1},
]
fac._outstack = y_values
zne_reduce = fac.reduce()
assert np.allclose(fac.get_expectation_values(), y_values)
assert np.allclose(fac.get_extrapolation_curve()(0.0), zne_reduce)
assert np.allclose(fac.get_optimal_parameters(), [0.0, 0.0])
assert np.allclose(
fac.get_parameters_covariance(), [[3.0, -1.0], [-1.0, 1.0]]
)
assert np.allclose(fac.get_scale_factors(), x_values)
assert np.allclose(fac.get_zero_noise_limit(), zne_reduce)
assert np.allclose(fac.get_zero_noise_limit_error(), 1.0)
def test_push_after_already_reduced_warning():
"""Tests a warning is raised if new data is pushed in a factory
which was already reduced."""
fac = LinearFactory([1, 2])
fac.push({"scale_factor": 1.0}, 1.0)
fac.push({"scale_factor": 2.0}, 2.0)
fac.reduce()
with warns(
ExtrapolationWarning,
match=r"You are pushing new data into a factory object",
):
fac.push({"scale_factor": 3.0}, 3.0)
# Assert no warning is raised when .reset() is used
fac.reset()
fac.push({"scale_factor": 1.0}, 2.0)
fac.push({"scale_factor": 2.0}, 1.0)
assert np.isclose(3.0, fac.reduce())
"""Test error handling for a failing fit."""
fac = ExpFactory(X_VALS, asymptote=None)
fac._instack = [{"scale_factor": x} for x in X_VALS]
fac._outstack = [1.0, 2.0, 1.0, 2.0, 1.0]
with raises(
ExtrapolationError, match=r"The extrapolation fit failed to converge."
):
fac.reduce()
# test also the static "extrapolate" method.
with raises(
ExtrapolationError, match=r"The extrapolation fit failed to converge."
):
ExpFactory.extrapolate(X_VALS, [1.0, 2.0, 1.0, 2.0, 1.0])
@mark.parametrize("fac", [LinearFactory([1, 1, 1]), ExpFactory([1, 1, 1])])
def test_failing_fit_warnings(fac):
"""Test that the correct warning is raised for an ill-conditioned fit."""
fac._instack = [{"scale_factor": 1.0} for _ in range(4)]
fac._outstack = [1, 1, 1, 1]
with warns(
ExtrapolationWarning,
match=r"The extrapolation fit may be ill-conditioned.",
):
fac.reduce()
# test also the static "extrapolate" method.
with warns(
ExtrapolationWarning,
match=r"The extrapolation fit may be ill-conditioned.",
):
fac.extrapolate([1, 1, 1, 1], [1.0, 1.0, 1.0, 1.0])
)
from mitiq.benchmarks.utils import noisy_simulation
from mitiq.zne import mitigate_executor
SCALE_FUNCTIONS = [
fold_gates_at_random,
fold_gates_from_left,
fold_gates_from_right,
fold_global,
]
FACTORIES = [
AdaExpFactory(steps=3, scale_factor=1.5, asymptote=0.25),
ExpFactory([1.0, 1.4, 2.1], asymptote=0.25),
RichardsonFactory([1.0, 1.4, 2.1]),
LinearFactory([1.0, 1.6]),
PolyFactory([1.0, 1.4, 2.1], order=2),
]
def test_rb_circuits():
depths = range(2, 10, 2)
# test single qubit RB
for trials in [2, 3]:
circuits = rb_circuits(n_qubits=1, num_cliffords=depths, trials=trials)
for qc in circuits:
# we check the ground state population to ignore any global phase
wvf = qc.final_wavefunction()
zero_prob = abs(wvf[0]**2)
assert np.isclose(zero_prob, 1)
def test_less_than_two_scale_factors_error():
"""Test less than 2 scale_factors."""
with raises(ValueError, match=r"At least 2 scale factors are necessary"):
_ = LinearFactory([1])