Fix default cvxpy solver (#1283)

### Summary

Similar to Qiskit/qiskit#11002, this PR fixes
the default solver for cvxpy to SCS. The 1.4.0 release changing the
default solver to Clarabel breaks tomography experiments because
Clarabel expects `max_iter` instead of `max_iters`. Also fixes a new
lint error in `test_drag.py`.

qiskit_experiments/library/tomography/fitters/cvxpy_utils.py

@@ -64,7 +64,9 @@ def decorated_func(*args, **kwargs):
     return decorated_func
 
 
-def solve_iteratively(problem: Problem, initial_iters: int, scale: int = 2, **solve_kwargs) -> None:
+def solve_iteratively(
+    problem: Problem, initial_iters: int, scale: int = 2, solver: str = "SCS", **solve_kwargs
+) -> None:
     """Solve a CVXPY problem increasing iterations if solution is inaccurate.
 
     If the problem is not solved with the ``initial_iters`` value of
@@ -79,6 +81,7 @@ def solve_iteratively(problem: Problem, initial_iters: int, scale: int = 2, **so
                        when solving the problem
         scale: Scale factor for increasing the initial_iters up to
                max_iters at each step (Default: 2).
+        solver: The solver to use. Defaults to the Splitting Conic Solver.
         solve_kwargs: kwargs for problem.solve method.
 
     Raises:
@@ -90,7 +93,7 @@ def solve_iteratively(problem: Problem, initial_iters: int, scale: int = 2, **so
     problem_solved = False
     while not problem_solved:
         solve_kwargs["max_iters"] = current_max_iters
-        problem.solve(**solve_kwargs)
+        problem.solve(solver=solver, **solve_kwargs)
         if problem.status in ["optimal_inaccurate", "optimal"]:
             problem_solved = True
         elif problem.status == "unbounded_inaccurate":

test/library/calibration/test_drag.py

@@ -74,6 +74,7 @@ def test_end_to_end(self, freq, betas, p0_opt):
         self.assertExperimentDone(expdata)
         result = expdata.analysis_results(1)
 
+        # pylint: disable=no-member
         self.assertTrue(abs(result.value.n - backend.experiment_helper.ideal_beta) < self.test_tol)
         self.assertEqual(result.quality, "good")
         self.assertEqual(expdata.metadata["meas_level"], MeasLevel.CLASSIFIED)