text stringlengths 0 128 |
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============================= test session starts ============================== |
platform linux -- Python 3.12.13, pytest-8.4.1, pluggy-1.6.0 -- /usr/local/bin/python3 |
rootdir: /app |
configfile: ../dev/null |
plugins: json-ctrf-0.3.5 |
collecting ... collected 11 items |
::test_required_artifacts_are_regular_hardened_and_solver_backed PASSED [ 9%] |
::test_public_outputs_match_independent_recomputation FAILED [ 18%] |
::test_public_estimates_are_calibrated_and_reduce_variance FAILED [ 27%] |
::test_hidden_four_qubit_povm_and_state FAILED [ 36%] |
::test_hidden_low_shot_nested_bias_selection FAILED [ 45%] |
::test_hidden_odd_register_with_singleton_block FAILED [ 54%] |
::test_hidden_four_outcome_singleton_schema_variant FAILED [ 63%] |
::test_hidden_four_outcome_paired_tetrahedral_variant FAILED [ 72%] |
::test_hidden_seven_qubit_near_perfect_matching_variant FAILED [ 81%] |
::test_hidden_duals_are_valid_on_random_operators FAILED [ 90%] |
::test_solver_is_byte_deterministic_on_unseen_input FAILED [100%] |
=================================== FAILURES =================================== |
_____________ test_public_outputs_match_independent_recomputation ______________ |
def test_public_outputs_match_independent_recomputation() -> None: |
> compare_outputs_to_reference(OUTPUT_DIR, INPUT_DIR) |
/verifier/test_outputs.py:692: |
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ |
output_dir = PosixPath('/root/povm_solution') |
input_dir = PosixPath('/root/povm_input') |
def compare_outputs_to_reference(output_dir: Path, input_dir: Path) -> dict[str, Any]: |
expected = reference_analysis(input_dir) |
summary = load_json(output_dir / "summary.json") |
duals_payload = load_json(output_dir / "duals.json") |
diagnostics = load_json(output_dir / "diagnostics.json") |
certificates = load_json(output_dir / "certificates.json") |
estimates = load_estimates(output_dir / "estimates.csv") |
config = expected["config"] |
assert set(summary) == { |
"schema_version", |
"instance_id", |
"num_qubits", |
"num_shots", |
"num_folds", |
"partition", |
"partition_mutual_information", |
"selected_bias_shots_per_outcome", |
"mean_variance_ratio", |
"max_duality_residual", |
} |
assert summary["schema_version"] == "1.0" |
assert summary["instance_id"] == config["instance_id"] |
assert summary["num_qubits"] == config["num_qubits"] |
assert summary["num_shots"] == expected["outcomes"].shape[0] |
assert summary["num_folds"] == config["num_folds"] |
expected_partition = [list(block) for block in expected["partition"]] |
assert summary["partition"] == expected_partition |
assert math.isclose(summary["partition_mutual_information"], expected["partition_score"], rel_tol=1e-10, abs_tol=1e-12) |
assert summary["selected_bias_shots_per_outcome"] == expected["selected_bias_shots_per_outcome"] |
assert set(duals_payload) == {"schema_version", "instance_id", "partition", "blocks"} |
assert duals_payload["schema_version"] == "1.0" |
assert duals_payload["instance_id"] == config["instance_id"] |
assert duals_payload["partition"] == expected_partition |
assert len(duals_payload["blocks"]) == len(expected["full_models"]) |
maximum_residual = 0.0 |
for actual, model in zip(duals_payload["blocks"], expected["full_models"]): |
assert set(actual) == { |
"qubits", |
"outcome_tuples", |
"frequencies", |
"duals_real", |
"duals_imag", |
"condition_number", |
"duality_residual", |
} |
assert actual["qubits"] == list(model["qubits"]) |
assert actual["outcome_tuples"] == [list(item) for item in model["outcome_tuples"]] |
frequencies = np.asarray(actual["frequencies"], dtype=float) |
submitted_duals = np.asarray(actual["duals_real"], dtype=float) + 1j * np.asarray(actual["duals_imag"], dtype=float) |
np.testing.assert_allclose(frequencies, model["frequencies"], rtol=2e-10, atol=2e-12) |
np.testing.assert_allclose(submitted_duals, model["duals"], rtol=2e-8, atol=2e-9) |
assert np.max(np.abs(submitted_duals - submitted_duals.conj().transpose(0, 2, 1))) < 1e-9 |
assert math.isclose(actual["condition_number"], model["condition_number"], rel_tol=2e-8, abs_tol=1e-8) |
assert math.isclose(actual["duality_residual"], model["duality_residual"], rel_tol=0.1, abs_tol=2e-9) |
maximum_residual = max(maximum_residual, float(actual["duality_residual"])) |
assert maximum_residual < 2e-8 |
assert math.isclose(summary["max_duality_residual"], maximum_residual, rel_tol=1e-8, abs_tol=1e-12) |
> assert diagnostics["schema_version"] == "1.0" |
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
E KeyError: 'schema_version' |
/verifier/test_outputs.py:428: KeyError |
___________ test_public_estimates_are_calibrated_and_reduce_variance ___________ |
def test_public_estimates_are_calibrated_and_reduce_variance() -> None: |
> result = compare_outputs_to_reference(OUTPUT_DIR, INPUT_DIR) |
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