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classiqdorclassiqdor
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Add tests for several notebooks
1 parent 50eef07 commit d255fc4

12 files changed

+292
-2
lines changed

‎tests/notebooks/test_3_sat_grover.py‎

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from tests.utils_for_testbook import (
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validate_quantum_program_size,
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validate_quantum_model,
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wrap_testbook,
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)
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from testbook.client import TestbookNotebookClient
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@wrap_testbook(
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"3_sat_grover",
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timeout_seconds=36,
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)
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def test_notebook(tb: TestbookNotebookClient) -> None:
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# test models
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validate_quantum_model(tb.ref("qmod"))
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validate_quantum_model(tb.ref("qmod_large"))
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# test quantum programs
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validate_quantum_program_size(
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tb.ref("qprog"),
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expected_width=10, # actual width: 10
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expected_depth=450, # actual depth: 429
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)
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validate_quantum_program_size(
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tb.ref("qprog_large"),
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expected_width=20, # actual width: 19
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expected_depth=2500, # actual depth: 2375
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)
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# test notebook content
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pass # TODO

‎tests/notebooks/test_bernstein_vazirani.py‎

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@wrap_testbook("bernstein_vazirani", timeout_seconds=20)
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def test_bernstein_vazirani(tb: TestbookNotebookClient) -> None:
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def test_notebook(tb: TestbookNotebookClient) -> None:
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# test models
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validate_quantum_model(tb.ref("qmod"))
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# test quantum programs

‎tests/notebooks/test_deutsch_jozsa.py‎

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from tests.utils_for_testbook import (
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validate_quantum_program_size,
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validate_quantum_model,
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wrap_testbook,
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)
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from testbook.client import TestbookNotebookClient
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@wrap_testbook("deutsch_jozsa", timeout_seconds=48)
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def test_notebook(tb: TestbookNotebookClient) -> None:
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# test models
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validate_quantum_model(tb.ref("qmod"))
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# test quantum programs
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validate_quantum_program_size(
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tb.ref("qprog"), expected_width=None, expected_depth=None
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)
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# test notebook content
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pass

‎tests/notebooks/test_discrete_log.py‎

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from tests.utils_for_testbook import (
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validate_quantum_program_size,
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validate_quantum_model,
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wrap_testbook,
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)
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from testbook.client import TestbookNotebookClient
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import numpy as np
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import pandas as pd
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from scipy.special import rel_entr
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@wrap_testbook("discrete_log", timeout_seconds=600)
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def test_notebook(tb: TestbookNotebookClient) -> None:
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# test models
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validate_quantum_model(tb.ref("qmod_Z5"))
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validate_quantum_model(tb.ref("qmod_Z13"))
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# test quantum programs
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validate_quantum_program_size(
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tb.ref("qprog_Z5"),
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expected_width=12, # actual width: 12
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expected_depth=5000, # actual depth: 4658
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)
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validate_quantum_program_size(
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tb.ref("qprog_Z13"),
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expected_width=22, # actual width: 20
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expected_depth=21000, # actual depth: 19770
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)
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# test notebook content
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df = _get_result_as_dataframe(tb)
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actual_values = _get_distribution(df)
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expected_values = _get_uniform_distribution(df)
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# check that the distribution we got is close to even distribution
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distance = rel_entr(actual_values, expected_values) # 1.8, 3.01, 1.7, 3.1, 0.6, 4.4
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assert distance.sum() < 10
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def _get_result_as_dataframe(tb: TestbookNotebookClient) -> pd.DataFrame:
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parsed_counts = tb.ref("result_Z5.parsed_counts")
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data_list = [
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(sample_state.state["func_res"], sample_state.shots)
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for sample_state in parsed_counts
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]
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df = pd.DataFrame(data_list).rename(columns={0: "func_res", 1: "shots"})
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return df
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def _get_distribution(df: DataFrame) -> np.ndarray:
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grouped = df.groupby("func_res").sum()
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values = grouped_data.values.flatten()
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return values
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def _get_uniform_distribution(df: DataFrame) -> np.ndarray:
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total_num_shots = data["shots"].sum()
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num_func_res_options = len(data["func_res"].unique())
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expected_values = [total_num_shots / num_func_res_options] * num_func_res_options
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return expected_values

‎tests/notebooks/test_discrete_poisson_solver.py‎

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from tests.utils_for_testbook import (
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validate_quantum_program_size,
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validate_quantum_model,
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wrap_testbook,
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)
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from testbook.client import TestbookNotebookClient
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@wrap_testbook("discrete_poisson_solver", timeout_seconds=300)
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def test_notebook(tb: TestbookNotebookClient) -> None:
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# test models
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validate_quantum_model(tb.ref("qmod"))
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# test quantum programs
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validate_quantum_program_size(
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tb.ref("qprog"),
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expected_width=16, # actual width: 16
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expected_depth=12000, # actual depth: 11538
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)
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# test notebook content
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pass # TODO

‎tests/notebooks/test_glued_trees.py‎

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from tests.utils_for_testbook import (
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validate_quantum_program_size,
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validate_quantum_model,
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wrap_testbook,
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)
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from testbook.client import TestbookNotebookClient
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@wrap_testbook("glued_trees", timeout_seconds=500)
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def test_notebook(tb: TestbookNotebookClient) -> None:
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# everything in this test is inside a function, storing results in a file
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pass # TODO: need to rewrite the notebook in order to test it

‎tests/notebooks/test_grover_max_cut.py‎

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from tests.utils_for_testbook import (
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validate_quantum_program_size,
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validate_quantum_model,
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wrap_testbook,
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)
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from testbook.client import TestbookNotebookClient
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@wrap_testbook(
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"grover_max_cut",
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timeout_seconds=220,
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)
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def test_notebook(tb: TestbookNotebookClient) -> None:
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# test models
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validate_quantum_model(tb.ref("qmod"))
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# test quantum programs
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validate_quantum_program_size(
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tb.ref("qprog"),
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expected_width=None, # actual width: ???
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expected_depth=None, # actual depth: ???
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)
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# test notebook content
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pass # TODO

‎tests/notebooks/test_hidden_shift.py‎

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"hidden_shift",
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timeout_seconds=272, # we may lower this value
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)
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def test_hidden_shift(tb: TestbookNotebookClient) -> None:
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def test_notebook(tb: TestbookNotebookClient) -> None:
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# test models
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validate_quantum_model(tb.ref("qmod_simple"))
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validate_quantum_model(tb.ref("qmod_complex"))
@@ -31,5 +31,6 @@ def test_hidden_shift(tb: TestbookNotebookClient) -> None:
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expected_width=20, # actual width: 20
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expected_depth=1700, # actual depth: 1685
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)
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# test notebook content
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pass # TODO

‎tests/notebooks/test_hybrid_qnn_for_subset_majority.py‎

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from tests.utils_for_testbook import (
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validate_quantum_program_size,
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validate_quantum_model,
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wrap_testbook,
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)
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from testbook.client import TestbookNotebookClient
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@wrap_testbook(
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"hybrid_qnn_for_subset_majority",
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timeout_seconds=120,
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)
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def test_notebook(tb: TestbookNotebookClient) -> None:
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"""
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A notebook for a hybrid classical quantum neural network.
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The test verifies that the pre-trained model is indeed well trained.
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"""
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assert (
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tb.ref("accuracy") > 0.85
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), "The network is not trained, although we load a pre-trained model."
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pass

‎tests/notebooks/test_qmc_user_defined.py‎

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from tests.utils_for_testbook import (
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validate_quantum_program_size,
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validate_quantum_model,
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wrap_testbook,
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)
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from testbook.client import TestbookNotebookClient
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@wrap_testbook("qmc_user_defined", timeout_seconds=176)
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def test_notebook(tb: TestbookNotebookClient) -> None:
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# test models
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validate_quantum_model(tb.ref("model"))
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validate_quantum_model(tb.ref("model_2"))
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validate_quantum_model(tb.ref("model_3"))
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# test quantum programs
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validate_quantum_program_size(
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tb.ref("qprog"),
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expected_width=4, # actual width: 4
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expected_depth=150, # actual depth: 104
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)
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validate_quantum_program_size(
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tb.ref("qprog_2"),
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expected_width=5, # actual width: 4
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expected_depth=350, # actual depth: 236
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)
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validate_quantum_program_size(
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tb.ref("qprog_3"),
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expected_width=7, # actual width: 7
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expected_depth=2500, # actual depth: 2008
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)
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# test notebook content
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pass # TODO

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