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[Testing] Reduce testing time of measurement protocols #638

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merged 9 commits into from
Jan 6, 2025
Merged

[Testing] Reduce testing time of measurement protocols #638

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317162e
shadows avoid tomography comparison and combine observables for measu…
Dec 30, 2024
d863426
combine two observables in list for chemistry ham
Dec 30, 2024
79e6f7f
reduce shadow size for chem 2 test
Dec 30, 2024
41d0be3
disabling long tomo tests
Dec 30, 2024
325789a
Merge remote-tracking branch 'origin/main' into cm/refactor_measurements
Jan 6, 2025
602147d
Merge remote-tracking branch 'origin/main' into cm/refactor_measurements
Jan 6, 2025
b52136e
reduce zz_hamiltonian to 3 qubits
Jan 6, 2025
22d12d6
fix lint
Jan 6, 2025
27d31bb
comment zz hamiltonian as it requires 1 min
Jan 6, 2025
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65 changes: 16 additions & 49 deletions tests/qadence/test_measurements/test_shadows.py
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Original file line number Diff line number Diff line change
Expand Up @@ -124,14 +124,12 @@ def test_estimations_comparison_exact(
(QuantumCircuit(2, blocks), values2, DiffMode.GPSR),
],
)
@pytest.mark.parametrize("observable", [Z(0) ^ 2, X(1)])
def test_estimations_comparison_tomo_forward_pass(
circuit: QuantumCircuit,
values: dict,
diff_mode: DiffMode,
observable: AbstractBlock,
def test_estimations_shadow_forward_pass(
circuit: QuantumCircuit, values: dict, diff_mode: DiffMode
) -> None:
pyq_backend = backend_factory(BackendName.PYQTORCH, diff_mode=diff_mode)
# combine observables to avoid repeating measurements
observable = [Z(0) ^ 2, X(1)]
(conv_circ, conv_obs, embed, params) = pyq_backend.convert(circuit, observable)
pyq_exp_exact = pyq_backend.expectation(conv_circ, conv_obs, embed(params, values))
model = QuantumModel(
Expand All @@ -140,18 +138,11 @@ def test_estimations_comparison_tomo_forward_pass(
backend=BackendName.PYQTORCH,
diff_mode=DiffMode.GPSR,
)
options = {"n_shots": 100000}
estimated_exp_tomo = model.expectation(
values=values,
measurement=Measurements(protocol=Measurements.TOMOGRAPHY, options=options),
)
new_options = {"accuracy": 0.1, "confidence": 0.1}
options = {"accuracy": 0.1, "confidence": 0.1}
estimated_exp_shadow = model.expectation(
values=values,
measurement=Measurements(protocol=Measurements.SHADOW, options=new_options),
) # N = 54400.
assert torch.allclose(estimated_exp_tomo, pyq_exp_exact, atol=1.0e-2)
assert torch.allclose(estimated_exp_shadow, pyq_exp_exact, atol=0.1)
measurement=Measurements(protocol=Measurements.SHADOW, options=options),
)
assert torch.allclose(estimated_exp_shadow, pyq_exp_exact, atol=0.1)


Expand All @@ -161,17 +152,19 @@ def test_chemistry_hamiltonian_1() -> None:

circuit = load("./tests/test_files/chem_circ.json")
assert isinstance(circuit, QuantumCircuit)
hamiltonian = load("./tests/test_files/chem_ham.json")
assert isinstance(hamiltonian, AbstractBlock)
# combine observables to avoid repeating measurements
hamiltonians = [load("./tests/test_files/chem_ham.json"), ising_hamiltonian(circuit.n_qubits)]
for hamiltonian in hamiltonians:
assert isinstance(hamiltonian, AbstractBlock)
# Restrict shadow size for faster tests.
kwargs = {"accuracy": 0.1, "confidence": 0.1, "shadow_size": 1000}
param_values = {"theta_0": torch.tensor([1.0])}

model = QuantumModel(
circuit=circuit,
observable=hamiltonian,
observable=hamiltonians, # type: ignore[arg-type]
backend=BackendName.PYQTORCH,
diff_mode=DiffMode.GPSR,
diff_mode=DiffMode.AD,
)
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exact = model.expectation(values=param_values)
estim = model.expectation(
Expand All @@ -181,32 +174,6 @@ def test_chemistry_hamiltonian_1() -> None:
assert torch.allclose(estim, exact, atol=0.3)


@pytest.mark.flaky(max_runs=5)
def test_chemistry_hamiltonian_2() -> None:
from qadence import load

circuit = load("./tests/test_files/chem_circ.json")
assert isinstance(circuit, QuantumCircuit)
hamiltonian = ising_hamiltonian(2)
assert isinstance(hamiltonian, AbstractBlock)
# Restrict shadow size for faster tests.
kwargs = {"accuracy": 0.1, "confidence": 0.1, "shadow_size": 1000}
param_values = {"theta_0": torch.tensor([1.0])}

model = QuantumModel(
circuit=circuit,
observable=hamiltonian,
backend=BackendName.PYQTORCH,
diff_mode=DiffMode.GPSR,
)
exact = model.expectation(values=param_values)
estim = model.expectation(
values=param_values,
measurement=Measurements(protocol=Measurements.SHADOW, options=kwargs),
)
assert torch.allclose(estim, exact, atol=0.2)


def open_chem_obs() -> AbstractBlock:
directory = os.getcwd()
with open(os.path.join(directory, "tests/test_files/h4.json"), "r") as js:
Expand All @@ -215,18 +182,18 @@ def open_chem_obs() -> AbstractBlock:


@pytest.mark.flaky(max_runs=5)
def test_chemistry_hamiltonian_3() -> None:
def test_chemistry_hamiltonian_2() -> None:
circuit = QuantumCircuit(4, kron(Z(0), H(1), Z(2), X(3)))
hamiltonian = open_chem_obs()
param_values: dict = dict()

kwargs = {"accuracy": 0.1, "confidence": 0.1, "shadow_size": 5000}
kwargs = {"accuracy": 0.1, "confidence": 0.1, "shadow_size": 1000}

model = QuantumModel(
circuit=circuit,
observable=hamiltonian,
backend=BackendName.PYQTORCH,
diff_mode=DiffMode.GPSR,
diff_mode=DiffMode.AD,
)
exact = model.expectation(values=param_values)
estim = model.expectation(
Expand Down
32 changes: 17 additions & 15 deletions tests/qadence/test_measurements/test_tomography.py
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Original file line number Diff line number Diff line change
Expand Up @@ -457,7 +457,7 @@ def test_basic_tomography_for_parametric_circuit_forward_pass(
"observable, acceptance",
[
(total_magnetization(4), MIDDLE_ACCEPTANCE),
(zz_hamiltonian(4), MIDDLE_ACCEPTANCE),
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# (zz_hamiltonian(4), MIDDLE_ACCEPTANCE), # skiping zz as it is very heavy
# (ising_hamiltonian(4), MIDDLE_ACCEPTANCE),
(
add(
Expand All @@ -482,7 +482,6 @@ def test_forward_and_backward_passes_with_qnn(observable: AbstractBlock, accepta
batch_size = 5
kwargs = {"n_shots": 1000000}

# fm = fourier_feature_map(n_qubits)
fm = feature_map(n_qubits, fm_type=BasisSet.CHEBYSHEV)
ansatz = hea(n_qubits, depth=2)
circuit = QuantumCircuit(n_qubits, fm, ansatz)
Expand Down Expand Up @@ -571,12 +570,13 @@ def test_partial_derivatives_with_qnn(observable: AbstractBlock, acceptance: flo
ones_like(dexpval_tomo_phi),
create_graph=True,
)[0]
d2expval_tomo_phi2theta = autograd.grad(
d2expval_tomo_phi2,
list(params.values()),
ones_like(d2expval_tomo_phi2),
create_graph=True,
)[0]
##### this derivative is very long, disabling for CI
# d2expval_tomo_phi2theta = autograd.grad(
# d2expval_tomo_phi2,
# list(params.values()),
# ones_like(d2expval_tomo_phi2),
# create_graph=True,
# )[0]
expectation_exact = model_with_psr.expectation(values=values)
dexpval_exact_phi = autograd.grad(
expectation_exact,
Expand All @@ -590,6 +590,7 @@ def test_partial_derivatives_with_qnn(observable: AbstractBlock, acceptance: flo
ones_like(expectation_exact),
create_graph=True,
)[0]

dexpval_exact_phitheta = autograd.grad(
dexpval_exact_phi,
list(params.values()),
Expand All @@ -602,18 +603,19 @@ def test_partial_derivatives_with_qnn(observable: AbstractBlock, acceptance: flo
ones_like(dexpval_exact_phi),
create_graph=True,
)[0]
d2expval_exact_phi2theta = autograd.grad(
d2expval_exact_phi2,
list(params.values()),
ones_like(d2expval_exact_phi2),
create_graph=True,
)[0]
##### this derivative is very long, disabling for CI
# d2expval_exact_phi2theta = autograd.grad(
# d2expval_exact_phi2,
# list(params.values()),
# ones_like(d2expval_exact_phi2),
# create_graph=True,
# )[0]
assert allclose(expectation_tomo, expectation_exact, atol=acceptance)
assert allclose(dexpval_tomo_phi, dexpval_exact_phi, atol=acceptance)
assert allclose(dexpval_tomo_theta, dexpval_exact_theta, atol=acceptance)
assert allclose(dexpval_tomo_phitheta, dexpval_exact_phitheta, atol=acceptance)
assert allclose(d2expval_tomo_phi2, d2expval_exact_phi2, atol=HIGH_ACCEPTANCE)
assert allclose(d2expval_tomo_phi2theta, d2expval_exact_phi2theta, atol=HIGH_ACCEPTANCE)
# assert allclose(d2expval_tomo_phi2theta, d2expval_exact_phi2theta, atol=HIGH_ACCEPTANCE)


@pytest.mark.skip(
Expand Down
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