def test_benchmark_2q_xeb_fidelities(circuits_cycle_depths_sampled_df, pass_cycle_depths):
circuits, cycle_depths, sampled_df = circuits_cycle_depths_sampled_df
if pass_cycle_depths:
fid_df = benchmark_2q_xeb_fidelities(sampled_df, circuits, cycle_depths)
else:
fid_df = benchmark_2q_xeb_fidelities(sampled_df, circuits)
assert len(fid_df) == len(cycle_depths)
assert sorted(fid_df['cycle_depth'].unique()) == cycle_depths.tolist()
assert np.all(fid_df['fidelity'] > 0.98)
fit_df = fit_exponential_decays(fid_df)
for _, row in fit_df.iterrows():
assert list(row['cycle_depths']) == list(cycle_depths)
assert len(row['fidelities']) == len(cycle_depths)
def test_benchmark_2q_xeb_fidelities_parallel():
circuits = rqcg.generate_library_of_2q_circuits(
n_library_circuits=5,
two_qubit_gate=cirq.ISWAP**0.5,
max_cycle_depth=4)
cycle_depths = [2, 3, 4]
graph = _gridqubits_to_graph_device(cirq.GridQubit.rect(2, 2))
combs = rqcg.get_random_combinations_for_device(
n_library_circuits=len(circuits),
n_combinations=2,
device_graph=graph,
random_state=10)
sampled_df = sample_2q_xeb_circuits(
sampler=cirq.Simulator(),
circuits=circuits,
cycle_depths=cycle_depths,
combinations_by_layer=combs,
)
fid_df = benchmark_2q_xeb_fidelities(sampled_df, circuits, cycle_depths)
n_pairs = sum(len(c.pairs) for c in combs)
assert len(fid_df) == len(cycle_depths) * n_pairs
fit_df = fit_exponential_decays(fid_df)
for _, row in fit_df.iterrows():
assert list(row['cycle_depths']) == list(cycle_depths)
assert len(row['fidelities']) == len(cycle_depths)
def test_benchmark_2q_xeb_fidelities():
q0, q1 = cirq.LineQubit.range(2)
circuits = [
rqcg.random_rotations_between_two_qubit_circuit(
q0,
q1,
depth=50,
two_qubit_op_factory=lambda a, b, _: SQRT_ISWAP(a, b),
seed=52) for _ in range(2)
]
cycle_depths = np.arange(3, 50, 9)
sampled_df = sample_2q_xeb_circuits(sampler=cirq.Simulator(seed=53),
circuits=circuits,
cycle_depths=cycle_depths)
fid_df = benchmark_2q_xeb_fidelities(sampled_df, circuits, cycle_depths)
assert len(fid_df) == len(cycle_depths)
for _, row in fid_df.iterrows():
assert row['cycle_depth'] in cycle_depths
assert row['fidelity'] > 0.98
fit_df = fit_exponential_decays(fid_df)
for _, row in fit_df.iterrows():
assert list(row['cycle_depths']) == list(cycle_depths)
assert len(row['fidelities']) == len(cycle_depths)
def test_benchmark_2q_xeb_subsample_depths(circuits_cycle_depths_sampled_df):
circuits, _, sampled_df = circuits_cycle_depths_sampled_df
cycle_depths = [10, 20]
fid_df = benchmark_2q_xeb_fidelities(sampled_df, circuits, cycle_depths)
assert len(fid_df) == len(cycle_depths)
assert sorted(fid_df['cycle_depth'].unique()) == cycle_depths
cycle_depths = [11, 21]
with pytest.raises(ValueError):
_ = benchmark_2q_xeb_fidelities(sampled_df, circuits, cycle_depths)
cycle_depths = [10, 100_000]
with pytest.raises(ValueError):
_ = benchmark_2q_xeb_fidelities(sampled_df, circuits, cycle_depths)
cycle_depths = []
with pytest.raises(ValueError):
_ = benchmark_2q_xeb_fidelities(sampled_df, circuits, cycle_depths)
def test_parallel_full_workflow(use_pool):
circuits = rqcg.generate_library_of_2q_circuits(
n_library_circuits=5,
two_qubit_gate=cirq.ISWAP**0.5,
max_cycle_depth=4,
random_state=8675309,
)
cycle_depths = [2, 3, 4]
graph = _gridqubits_to_graph_device(cirq.GridQubit.rect(2, 2))
combs = rqcg.get_random_combinations_for_device(
n_library_circuits=len(circuits),
n_combinations=2,
device_graph=graph,
random_state=10)
sampled_df = sample_2q_xeb_circuits(
sampler=cirq.Simulator(),
circuits=circuits,
cycle_depths=cycle_depths,
combinations_by_layer=combs,
)
if use_pool:
pool = multiprocessing.Pool()
else:
pool = None
fids_df_0 = benchmark_2q_xeb_fidelities(sampled_df=sampled_df,
circuits=circuits,
cycle_depths=cycle_depths,
pool=pool)
options = SqrtISwapXEBOptions(characterize_zeta=False,
characterize_gamma=False,
characterize_chi=False)
p_circuits = [
parameterize_circuit(circuit, options) for circuit in circuits
]
result = characterize_phased_fsim_parameters_with_xeb_by_pair(
sampled_df=sampled_df,
parameterized_circuits=p_circuits,
cycle_depths=cycle_depths,
options=options,
# super loose tolerances
fatol=5e-2,
xatol=5e-2,
pool=pool,
)
if pool is not None:
pool.terminate()
assert len(result.optimization_results) == graph.number_of_edges()
for opt_res in result.optimization_results.values():
assert np.abs(opt_res.fun) < 0.1 # noiseless simulator
assert len(
result.fidelities_df) == len(cycle_depths) * graph.number_of_edges()
assert np.all(result.fidelities_df['fidelity'] > 0.90)
before_after_df = before_and_after_characterization(
fids_df_0, characterization_result=result)
for _, row in before_after_df.iterrows():
assert len(row['fidelities_0']) == len(cycle_depths)
assert len(row['fidelities_c']) == len(cycle_depths)
assert 0 <= row['a_0'] <= 1
assert 0 <= row['a_c'] <= 1
assert 0 <= row['layer_fid_0'] <= 1
assert 0 <= row['layer_fid_c'] <= 1