🐛 Fix notebook execution errors

docs/conf.py

@@ -98,6 +98,7 @@
 # -- Options for {MyST}NB ----------------------------------------------------
 
 nb_execution_mode = "cache"
+nb_execution_raise_on_error = True
 
 
 class CDAStyle(UnsrtStyle):

src/mqt/problemsolver/resource_estimation/error_budget_optimization/generate_data.py

@@ -225,7 +225,7 @@ def generate_data(
             transpiled_qc = transpile(qc, basis_gates=QISKIT_STD_GATES, optimization_level=1)
             try:
                 # Estimate logical counts
-                counts = estimate(transpiled_qc)["logicalCounts"]
+                counts = estimate(transpiled_qc, skip_transpilation=True)["logicalCounts"]
                 if counts["rotationCount"] == 0:
                     continue  # Skip circuits without rotations, as we want to ensure distributing error budgets among all three types.
                 # Optimize error budgets
@@ -235,6 +235,7 @@ def generate_data(
                 )
             except Exception:
                 logger.exception(f"Error processing circuit {qc.name}.")
+                raise
 
             # Collect results
             specific_data = OrderedDict(counts)
@@ -259,6 +260,7 @@ def generate_data(
                 )
             except Exception:
                 logger.exception(f"Error processing logical counts entry {c}.")
+                raise
 
             # Collect results
             specific_data = OrderedDict(counts)

tests/resource_estimation/test_error_budget_optimization.py

@@ -8,17 +8,24 @@
 
 from __future__ import annotations
 
+import pytest
+
 from mqt.problemsolver.resource_estimation.error_budget_optimization import evaluate, generate_data, train
 
 
-def test_error_budget_optimization() -> None:
+@pytest.mark.parametrize("benchmark", ["qft", "ae"])
+def test_error_budget_optimization(benchmark: str) -> None:
     total_error_budget = 0.1
-    benchmarks_and_sizes = [("qft", [3, 4, 5])]
+    benchmarks_and_sizes = [(benchmark, [3, 4, 5])]
     data = generate_data(
         total_error_budget=total_error_budget,
         number_of_randomly_generated_distributions=10,
         benchmarks_and_sizes=benchmarks_and_sizes,
     )
+    assert len(data) == 3
+    assert [row["numQubits"] for row in data] == [3, 4, 5]
+    for row in data:
+        assert row["logical"] + row["t_states"] + row["rotations"] == pytest.approx(total_error_budget)
     model, x_test, y_test = train(data)
     y_pred = model.predict(x_test)
     evaluate(x_test, y_pred, total_error_budget)