update qiskit version

pyproject.toml

@@ -80,6 +80,7 @@ Research = "https://www.cda.cit.tum.de/research/quantum/"
 [tool.pytest.ini_options]
 minversion = "7.2"
 testpaths = ["tests"]
+pythonpath = ["src"]
 addopts = ["-ra", "--strict-markers", "--strict-config", "--showlocals"]
 log_cli_level = "info"
 xfail_strict = true

src/mqt/qao/solvers.py

@@ -18,6 +18,7 @@
 from qiskit.circuit.library import TwoLocal
 from qiskit.primitives import Sampler
 from qiskit_algorithms import QAOA, SamplingVQE
+from qiskit_algorithms.optimizers import COBYLA
 from qiskit_optimization.algorithms import (
  GroverOptimizer,
  MinimumEigenOptimizer,
@@ -523,7 +524,7 @@ def solve_qaoa_qubo(
  problem: Problem,
  auto_setting: bool = False,
  num_runs: int = 10,
- optimizer: Optimizer = None,
+ optimizer: Optimizer = COBYLA(),
  reps: int = 1,
  initial_state: QuantumCircuit | None = None,
  mixer: QuantumCircuit = None,
@@ -663,7 +664,7 @@ def solve_vqe_qubo(
  problem: Problem,
  auto_setting: bool = False,
  num_runs: int = 10,
- optimizer: Optimizer | None = None,
+ optimizer: Optimizer = COBYLA(),
  ansatz: QuantumCircuit | None = None,
  initial_point: np.ndarray[Any, Any] | None = None,
  aggregation: float | Callable[[list[float]], float] | None = None,
@@ -710,7 +711,7 @@ def solve_vqe_qubo(
  compilation_time = -1
  logging.getLogger("qiskit").setLevel(logging.ERROR)
  res = []
- if auto_setting and ansatz is None:
+ if auto_setting or ansatz is None:
  ansatz = TwoLocal(num_qubits=len(self.qubo.variables), rotation_blocks="ry", entanglement_blocks="cz")
 
  vqe_mes = SamplingVQE(

tests/__init__.py

@@ -0,0 +1,5 @@
+"""MQT QAO.
+
+This file is part of the MQT QAO library released under the MIT license.
+See README.md or go to https://github.com/cda-tum/mqt-predictor for more information.
+"""

tests/test_evaluation.py

@@ -1485,6 +1485,7 @@ def test_gas_solver_basic() -> None:
  solution = solver.solve_grover_adaptive_search_qubo(problem, qubit_values=6, num_runs=10)
  if isinstance(solution, Solution):
  all_satisfy, _each_satisfy = solution.check_constraint_optimal_solution()
+ print(solution.best_solution)
  assert solution.best_solution == {"a": 1.0, "b": 0.0, "c": 1.0}
  print(solution.best_solution)
  assert solution.best_energy < -5.9 # (the range if for having no issues with numerical errors)