def test_avoid_log_keyword():
"""Test that avoid_log=True and avoid_log=False give different results."""
fac = ExpFactory(X_VALS, asymptote=A, avoid_log=False)
fac.run_classical(f_exp_down)
znl_with_log = fac.reduce()
fac.avoid_log = True
znl_without_log = fac.reduce()
assert not znl_with_log == znl_without_log
def test_failing_fit_error():
"""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()
def test_exp_factory_with_asympt(test_f: Callable[[float], float],
avoid_log: bool):
"""Test of exponential extrapolator."""
seeded_f = apply_seed_to_func(test_f, SEED)
fac = ExpFactory(X_VALS, asymptote=A, avoid_log=avoid_log)
fac.run_classical(seeded_f)
assert not fac._opt_params
zne_value = fac.reduce()
assert np.isclose(zne_value, seeded_f(0, err=0), atol=CLOSE_TOL)
# There are three parameters in the exponential ansatz
assert len(fac._opt_params) == 3
exp_vals = fac.get_expectation_values()
assert np.isclose(
fac.extrapolate(X_VALS, exp_vals, asymptote=A, avoid_log=avoid_log),
zne_value,
)
assert np.isclose(
fac.extrapolate(
X_VALS,
exp_vals,
asymptote=A,
avoid_log=avoid_log,
full_output=True,
)[0],
zne_value,
)
def test_exp_factory_no_asympt(test_f: Callable[[float], float]):
"""Test of exponential extrapolator."""
seeded_f = apply_seed_to_func(test_f, SEED)
fac = ExpFactory(X_VALS, asymptote=None)
fac.run_classical(seeded_f)
assert not fac._opt_params
assert np.isclose(fac.reduce(), seeded_f(0, err=0), atol=CLOSE_TOL)
# There are three parameters to fit in the exponential ansatz
assert len(fac._opt_params) == 3
def test_run_factory_with_number_of_shots():
"""Tests "run" method of an ExpFactory with shot_list."""
qp = random_one_qubit_identity_circuit(num_cliffords=TEST_DEPTH)
qp = measure(qp, 0)
fac = ExpFactory([1.0, 2.0, 3.0], shot_list=[10 ** 4, 10 ** 5, 10 ** 6])
fac.run(qp, basic_executor, scale_noise=scale_noise)
result = fac.reduce()
assert np.isclose(result, 1.0, atol=1.0e-1)
assert fac._instack[0] == {"scale_factor": 1.0, "shots": 10 ** 4}
assert fac._instack[1] == {"scale_factor": 2.0, "shots": 10 ** 5}
assert fac._instack[2] == {"scale_factor": 3.0, "shots": 10 ** 6}
def test_exp_factory_with_asympt(test_f: Callable[[float], float],
avoid_log: bool):
"""Test of exponential extrapolator."""
seeded_f = apply_seed_to_func(test_f, SEED)
fac = ExpFactory(X_VALS, asymptote=A, avoid_log=True)
fac.iterate(seeded_f)
assert len(fac.opt_params) == 0
assert np.isclose(fac.reduce(), seeded_f(0, err=0), atol=CLOSE_TOL)
# There are three parameters to fit in the exponential ansatz
assert len(fac.opt_params) == 3