def test_parameterize_phased_fsim_circuit(gate):
q0, q1 = cirq.LineQubit.range(2)
circuit = rqcg.random_rotations_between_two_qubit_circuit(
q0,
q1,
depth=3,
two_qubit_op_factory=lambda a, b, _: gate(a, b),
seed=52)
p_circuit = parameterize_circuit(circuit, SqrtISwapXEBOptions())
cirq.testing.assert_has_diagram(
p_circuit,
"""\
0 1
│ │
Y^0.5 X^0.5
│ │
PhFSim(theta, zeta, chi, gamma, phi)─PhFSim(theta, zeta, chi, gamma, phi)
│ │
PhX(0.25)^0.5 Y^0.5
│ │
PhFSim(theta, zeta, chi, gamma, phi)─PhFSim(theta, zeta, chi, gamma, phi)
│ │
Y^0.5 X^0.5
│ │
PhFSim(theta, zeta, chi, gamma, phi)─PhFSim(theta, zeta, chi, gamma, phi)
│ │
X^0.5 PhX(0.25)^0.5
│ │
""",
transpose=True,
)
def test_characterize_phased_fsim_parameters_with_xeb():
q0, q1 = cirq.LineQubit.range(2)
rs = np.random.RandomState(52)
circuits = [
rqcg.random_rotations_between_two_qubit_circuit(
q0,
q1,
depth=20,
two_qubit_op_factory=lambda a, b, _: cirq.SQRT_ISWAP(a, b),
seed=rs,
) for _ in range(2)
]
cycle_depths = np.arange(3, 20, 6)
sampled_df = sample_2q_xeb_circuits(
sampler=cirq.Simulator(seed=rs),
circuits=circuits,
cycle_depths=cycle_depths,
progress_bar=None,
)
# only optimize theta so it goes faster.
options = SqrtISwapXEBOptions(
characterize_theta=True,
characterize_gamma=False,
characterize_chi=False,
characterize_zeta=False,
characterize_phi=False,
)
p_circuits = [
parameterize_circuit(circuit, options) for circuit in circuits
]
with multiprocessing.Pool() as pool:
result = characterize_phased_fsim_parameters_with_xeb(
sampled_df=sampled_df,
parameterized_circuits=p_circuits,
cycle_depths=cycle_depths,
options=options,
# speed up with looser tolerances:
fatol=1e-2,
xatol=1e-2,
pool=pool,
)
opt_res = result.optimization_results[(q0, q1)]
assert np.abs(opt_res.x[0] + np.pi / 4) < 0.1
assert np.abs(opt_res.fun) < 0.1 # noiseless simulator
assert len(result.fidelities_df) == len(cycle_depths)
assert np.all(result.fidelities_df['fidelity'] > 0.95)
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
def test_get_initial_simplex():
options = SqrtISwapXEBOptions()
simplex, names = options.get_initial_simplex_and_names()
assert names == ['theta', 'zeta', 'chi', 'gamma', 'phi']
assert len(simplex) == len(names) + 1
assert simplex.shape[1] == len(names)