fix dual_annealing bug and add tests

src/qutip_qoc/optimizer.py

@@ -320,10 +320,17 @@ def global_local_optimization(
         optimizer = sp.optimize.dual_annealing
 
         # if not specified through optimizer_kwargs "maxiter"
-        optimizer_kwargs.setdefault(
-            "maxiter",
-            optimizer_kwargs.get("max_iter", algorithm_kwargs.get("glob_max_iter", 0)),
+        keys = ["maxiter", "max_iter", "glob_max_iter", "niter"]
+        value = next(
+            (optimizer_kwargs.pop(key, None) or algorithm_kwargs.get(key))
+            for key in keys
+            if optimizer_kwargs.get(key) or algorithm_kwargs.get(key)
         )
+        optimizer_kwargs.setdefault("maxiter", value)
+
+        # remove remaining keys
+        for key in keys[1:]:
+            optimizer_kwargs.pop(key, None)
 
         if len(bounds) != 0:  # realizes boundaries through optimizer
             optimizer_kwargs.setdefault("bounds", np.concatenate(bounds))
@@ -375,8 +382,11 @@ def global_local_optimization(
     result.n_iters = min_res.nit
     if result.message is None:
         result.message = (
-            "Local minimizer: "
-            + min_res["lowest_optimization_result"].message
+            (
+                "Local minimizer: " + min_res["lowest_optimization_result"].message
+                if opt_method == "basinhopping"
+                else ""  # dual_annealing does not return a local minimizer message
+            )
             + " Global optimizer: "
             + min_res.message[0]
         )

src/qutip_qoc/pulse_optim.py

@@ -122,6 +122,10 @@ def optimize_pulses(
         integrator_kwargs = {}
 
     time_options = control_parameters.pop("__time__", {})
+    if time_options:  # convert to list of bounds if not already
+        if not isinstance(time_options["bounds"][0], (list, tuple)):
+            time_options["bounds"] = [time_options["bounds"]]
+
     alg = algorithm_kwargs.get("alg", "GRAPE")  # works with most input types
 
     Hd_lst, Hc_lst = [], []
@@ -313,13 +317,10 @@ def optimize_pulses(
 
                 else:
                     # Set the initial parameters
-                    pgen.set_optim_var_vals(np.array(x0[j]))
+                    pgen.init_pulse(init_param_vals=np.array(x0[j]))
                     init_amps[:, j] = pgen.gen_pulse()
 
             # Initialise the starting amplitudes
-            # NOTE: any initial CRAB guess pulse is only used
-            # to modulate or offset the initial amplitudes
-            # depending on init_pulse_params: pulse_action
             dyn.initialize_controls(init_amps)
             # And store the (random) initial parameters
             init_params = qtrl_optimizer._get_optim_var_vals()

tests/test_fidelity.py

@@ -74,6 +74,10 @@ def grad_sin(t, p, idx):
     },
 )
 
+PSU_state2state_dual_annealing = PSU_state2state._replace(
+    optimizer_kwargs={"method": "dual_annealing", "niter": 5},
+)
+
 # SU (must depend on global phase) state to state transfer
 SU_state2state = PSU_state2state._replace(
     objectives=[Objective(initial, H, initial)],
@@ -174,6 +178,8 @@ def sin_jax(t, p):
         pytest.param(PSU_unitary_jax, id="PSU unitary gate (JAX)"),
         pytest.param(SU_unitary_jax, id="SU unitary gate (JAX)"),
         pytest.param(TRCDIFF_map_jax, id="TRACEDIFF map synthesis (JAX)"),
+        # Options
+        pytest.param(PSU_state2state_dual_annealing, id="Dual annealing (GOAT)"),
     ]
 )
 def tst(request):

tests/test_result.py

@@ -77,7 +77,13 @@ def grad_sin(t, p, idx):
         "seed": 0,
     },
 )
+# ----------------------- CRAB --------------------
 
+# state to state transfer with initial parameters (not amplitudes)
+state2state_param_crab = state2state_goat._replace(
+    objectives=[Objective(initial, H, target)],
+    algorithm_kwargs={"alg": "CRAB", "fid_err_targ": 0.01},
+)
 # ----------------------- JAX ---------------------
 
 
@@ -110,12 +116,6 @@ def sin_z_jax(t, r, **kwargs):
     algorithm_kwargs={"alg": "JOPT", "fid_err_targ": 0.01},
 )
 
-# state to state transfer with initial parameters
-# instead of initial amplitudes
-state2state_param_crab = state2state_goat._replace(
-    objectives=[Objective(initial, H, target)],
-    algorithm_kwargs={"alg": "CRAB", "fid_err_targ": 0.01},
-)
 
 # ------------------- discrete CRAB / GRAPE  control ------------------------
 
@@ -157,8 +157,7 @@ def sin_z_jax(t, r, **kwargs):
     params=[
         pytest.param(state2state_grape, id="State to state (GRAPE)"),
         pytest.param(state2state_crab, id="State to state (CRAB)"),
-        # TODO: reactivate test after qutip_qtrl PR was merged
-        # pytest.param(state2state_param_crab, id="State to state (param. CRAB)"),
+        pytest.param(state2state_param_crab, id="State to state (param. CRAB)"),
         pytest.param(state2state_goat, id="State to state (GOAT)"),
         pytest.param(state2state_jax, id="State to state (JAX)"),
     ]