diff --git a/src/qutip_qoc/objective.py b/src/qutip_qoc/objective.py
index d91caac..986961a 100644
--- a/src/qutip_qoc/objective.py
+++ b/src/qutip_qoc/objective.py
@@ -80,12 +80,6 @@ def __init__(self, initial, H, target, weight=1):
         self.target = target
         self.weight = weight
 
-        # Check if any Hamiltonian in H is a superoperator
-        if any(qt.issuper(H_i) for H_i in (H if isinstance(H, list) else [H])):
-            # Convert initial and target accordingly
-            self.initial = qt.to_super(self.initial)
-            self.target = qt.to_super(self.target)
-
     def __getstate__(self):
         """
         Extract picklable information from the objective.
diff --git a/src/qutip_qoc/pulse_optim.py b/src/qutip_qoc/pulse_optim.py
index 94d424c..935bf18 100644
--- a/src/qutip_qoc/pulse_optim.py
+++ b/src/qutip_qoc/pulse_optim.py
@@ -11,6 +11,8 @@
 from qutip_qoc._optimizer import _global_local_optimization
 from qutip_qoc._time import _TimeInterval
 
+import qutip as qt
+
 try:
     from qutip_qoc._rl import _RL
     _rl_available = True
@@ -28,6 +30,7 @@ def optimize_pulses(
     optimizer_kwargs=None,
     minimizer_kwargs=None,
     integrator_kwargs=None,
+    optimization_type=None,
 ):
     """
     Run GOAT, JOPT, GRAPE, CRAB or RL optimization.
@@ -124,6 +127,11 @@ def optimize_pulses(
         Options for the solver, see :obj:`MESolver.options` and
         `Integrator <./classes.html#classes-ode>`_ for a list of all options.
 
+    optimization_type : str, optional
+        Type of optimization. By default, QuTiP-QOC will try to automatically determine 
+        whether this is a *state transfer* or a *gate synthesis* problem. Set this 
+        flag to ``"state_transfer"`` or ``"gate_synthesis"`` to set the mode manually.
+
     Returns
     -------
     result : :class:`qutip_qoc.Result`
@@ -187,10 +195,43 @@ def optimize_pulses(
             "maxiter": algorithm_kwargs.get("max_iter", 1000),
             "gtol": algorithm_kwargs.get("min_grad", 0.0 if alg == "CRAB" else 1e-8),
         }
+    # Iterate over objectives and convert initial and target states based on the optimization type
+    for objective in objectives:
+        H_list = objective.H if isinstance(objective.H, list) else [objective.H]
+        if any(qt.issuper(H_i) for H_i in H_list):
+            if isinstance(optimization_type, str) and optimization_type.lower() == "state_transfer":
+                if qt.isket(objective.initial):
+                    objective.initial = qt.operator_to_vector(qt.ket2dm(objective.initial))
+                elif qt.isoper(objective.initial):
+                    objective.initial = qt.operator_to_vector(objective.initial)
+                if qt.isket(objective.target):
+                    objective.target = qt.operator_to_vector(qt.ket2dm(objective.target))
+                elif qt.isoper(objective.target):
+                    objective.target = qt.operator_to_vector(objective.target)
+            elif isinstance(optimization_type, str) and optimization_type.lower() == "gate_synthesis":
+                objective.initial = qt.to_super(objective.initial)
+                objective.target = qt.to_super(objective.target)
+            elif optimization_type is None:
+                if qt.isoper(objective.initial) and qt.isoper(objective.target):
+                    if np.isclose((objective.initial).tr(), 1) and np.isclose((objective.target).tr(), 1):
+                        objective.initial = qt.operator_to_vector(objective.initial)
+                        objective.target = qt.operator_to_vector(objective.target)
+                    else:
+                        objective.initial = qt.to_super(objective.initial)
+                        objective.target = qt.to_super(objective.target)
+                if qt.isket(objective.initial):
+                    objective.initial = qt.operator_to_vector(qt.ket2dm(objective.initial))
+                if qt.isket(objective.target):
+                    objective.target = qt.operator_to_vector(qt.ket2dm(objective.target))
 
     # prepare qtrl optimizers
     qtrl_optimizers = []
     if alg == "CRAB" or alg == "GRAPE":
+        dyn_type = "GEN_MAT"
+        for objective in objectives:
+            if any(qt.isoper(H_i) for H_i in (objective.H if isinstance(objective.H, list) else [objective.H])):
+                dyn_type = "UNIT"
+
         if alg == "GRAPE":  # algorithm specific kwargs
             use_as_amps = True
             minimizer_kwargs.setdefault("method", "L-BFGS-B")  # gradient
@@ -247,7 +288,7 @@ def optimize_pulses(
                 "accuracy_factor": None,  # deprecated
                 "alg_params": alg_params,
                 "optim_params": algorithm_kwargs.get("optim_params", None),
-                "dyn_type": algorithm_kwargs.get("dyn_type", "GEN_MAT"),
+                "dyn_type": algorithm_kwargs.get("dyn_type", dyn_type),
                 "dyn_params": algorithm_kwargs.get("dyn_params", None),
                 "prop_type": algorithm_kwargs.get(
                     "prop_type", "DEF"