Match grape infidelity with manually computed one (#51)

* fix dim calc susch that grape infid computed correctly

Author: rochisha0

src/qutip_qoc/pulse_optim.py

@@ -195,34 +195,55 @@ 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):
+                    dim = objective.initial.shape[0]
                     objective.initial = qt.operator_to_vector(qt.ket2dm(objective.initial))
                 elif qt.isoper(objective.initial):
+                    dim = objective.initial.shape[0]
                     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)
+
+                algorithm_kwargs.setdefault("fid_params", {})
+                algorithm_kwargs["fid_params"].setdefault("scale_factor", 1.0 / dim)
+
             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:
+                is_state_transfer = False
                 if qt.isoper(objective.initial) and qt.isoper(objective.target):
-                    if np.isclose((objective.initial).tr(), 1) and np.isclose((objective.target).tr(), 1):
+                    if np.isclose(objective.initial.tr(), 1) and np.isclose(objective.target.tr(), 1):
+                        dim = objective.initial.shape[0]
                         objective.initial = qt.operator_to_vector(objective.initial)
                         objective.target = qt.operator_to_vector(objective.target)
+                        is_state_transfer = True
                     else:
                         objective.initial = qt.to_super(objective.initial)
                         objective.target = qt.to_super(objective.target)
+
                 if qt.isket(objective.initial):
+                    dim = objective.initial.shape[0]
                     objective.initial = qt.operator_to_vector(qt.ket2dm(objective.initial))
+                    is_state_transfer = True
+
                 if qt.isket(objective.target):
                     objective.target = qt.operator_to_vector(qt.ket2dm(objective.target))
+                    is_state_transfer = True
+
+                if is_state_transfer:
+                    algorithm_kwargs.setdefault("fid_params", {})
+                    algorithm_kwargs["fid_params"].setdefault("scale_factor", 1.0 / dim)
 
     # prepare qtrl optimizers
     qtrl_optimizers = []