def test_poly_extr():
"""Test of polynomial extrapolator."""
# test (order=1)
fac = PolyFactory(X_VALS, order=1)
fac.run_classical(f_lin)
assert np.isclose(fac.reduce(), f_lin(0, err=0), atol=CLOSE_TOL)
# test that, for some non-linear functions,
# order=1 is bad while order=2 is better.
seeded_f = apply_seed_to_func(f_non_lin, SEED)
fac = PolyFactory(X_VALS, order=1)
fac.run_classical(seeded_f)
assert not np.isclose(fac.reduce(), seeded_f(0, err=0), atol=NOT_CLOSE_TOL)
seeded_f = apply_seed_to_func(f_non_lin, SEED)
fac = PolyFactory(X_VALS, order=2)
fac.run_classical(seeded_f)
assert np.isclose(fac.reduce(), seeded_f(0, err=0), atol=CLOSE_TOL)
def test_too_few_points_for_polyfit_warning():
"""Test that the correct warning is raised if data is not enough to fit."""
fac = PolyFactory(X_VALS, order=2)
fac._instack = [
{
"scale_factor": 1.0,
"shots": 100
},
{
"scale_factor": 2.0,
"shots": 100
},
]
fac._outstack = [1.0, 2.0]
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.",
):
PolyFactory.extrapolate([1.0, 2.0], [1.0, 2.0], order=2)
def test_opt_params_poly_factory(order):
"""Tests that optimal parameters are stored after calling the reduce method.
"""
fac = PolyFactory(scale_factors=np.linspace(1, 10, 10), order=order)
assert fac.opt_params == []
fac.iterate(apply_seed_to_func(f_non_lin, seed=SEED))
zne_value = fac.reduce()
assert len(fac.opt_params) == order + 1
assert np.isclose(fac.opt_params[-1], zne_value)
def test_poly_extr():
"""Test of polynomial extrapolator."""
# test (order=1)
fac = PolyFactory(X_VALS, order=1)
fac.run_classical(f_lin)
assert np.isclose(fac.reduce(), f_lin(0, err=0), atol=CLOSE_TOL)
# test that, for some non-linear functions,
# order=1 is bad while order=2 is better.
seeded_f = apply_seed_to_func(f_non_lin, SEED)
fac = PolyFactory(X_VALS, order=1)
fac.run_classical(seeded_f)
assert not np.isclose(fac.reduce(), seeded_f(0, err=0), atol=NOT_CLOSE_TOL)
seeded_f = apply_seed_to_func(f_non_lin, SEED)
fac = PolyFactory(X_VALS, order=2)
fac.run_classical(seeded_f)
zne_value = fac.reduce()
assert np.isclose(fac.reduce(), seeded_f(0, err=0), atol=CLOSE_TOL)
exp_vals = fac.get_expectation_values()
assert np.isclose(fac.extrapolate(X_VALS, exp_vals, order=2), zne_value)
assert np.isclose(
fac.extrapolate(X_VALS, exp_vals, order=2, full_output=True)[0],
zne_value,
)
def test_with_observable_batched_factory(executor):
observable = Observable(PauliString(spec="Z"))
circuit = cirq.Circuit(cirq.H.on(cirq.LineQubit(0))) * 20
noisy_value = observable.expectation(circuit, sample_bitstrings)
zne_value = execute_with_zne(
circuit,
executor=functools.partial(executor, noise_model=cirq.depolarize),
observable=observable,
factory=PolyFactory(scale_factors=[1, 3, 5], order=2),
)
true_value = observable.expectation(
circuit, functools.partial(compute_density_matrix, noise_level=(0, )))
assert abs(zne_value - true_value) <= abs(noisy_value - true_value)
def test_too_few_points_for_polyfit_error():
"""Test that the correct error is raised if data is not enough to fit."""
fac = PolyFactory(X_VALS, order=2)
fac._instack = [
{
"scale_factor": 1.0,
"shots": 100
},
{
"scale_factor": 2.0,
"shots": 100
},
]
fac._outstack = [1.0, 2.0]
with raises(ValueError, match=r"Extrapolation order is too high."):
fac.reduce()
def test_with_observable_two_qubits(executor):
observable = Observable(PauliString(spec="XX", coeff=-1.21),
PauliString(spec="ZZ", coeff=0.7))
circuit = cirq.Circuit(cirq.H.on(cirq.LineQubit(0)),
cirq.CNOT.on(*cirq.LineQubit.range(2)))
circuit += [circuit, cirq.inverse(circuit)] * 20
noisy_value = observable.expectation(circuit, sample_bitstrings)
zne_value = execute_with_zne(
circuit,
executor=functools.partial(executor, noise_model=cirq.depolarize),
observable=observable,
factory=PolyFactory(scale_factors=[1, 3, 5], order=2),
)
true_value = observable.expectation(
circuit, functools.partial(compute_density_matrix, noise_level=(0, )))
assert abs(zne_value - true_value) <= 3 * abs(noisy_value - true_value)
def test_shot_list_errors():
"""Tests errors related to the "shot_lists" argument."""
with raises(IndexError, match=r"must have the same length."):
PolyFactory(X_VALS, order=2, shot_list=[1, 2])
with raises(TypeError, match=r"valid iterator of integers"):
PolyFactory(X_VALS, order=2, shot_list=[1.0, 2])
def test_order_is_too_high_for_scale_factors():
"""Test that a wrong initialization error is raised."""
with raises(ValueError, match=r"The extrapolation order cannot exceed"):
_ = PolyFactory(X_VALS, order=10)
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)