Merge pull request #195 from Zhaoyilunnn/master

close #192, close #194

quafu/algorithms/gradients/param_shift.py

@@ -35,6 +35,8 @@ def __call__(self, obs: Hamiltonian, params: List[float]):
             estimator (Estimator): estimator to calculate expectation values
             params (List[float]): params to optimize
         """
+        if self._est._backend != "sim":
+            return self.grad(obs, params)
         return self.new_grad(obs, params)
 
     def _gen_param_shift_vals(self, params):
@@ -46,7 +48,6 @@ def _gen_param_shift_vals(self, params):
         minus_params = params - offsets * np.pi / 2
         return plus_params.tolist() + minus_params.tolist()
 
-    # TODO: delete after 0.4.1
     def grad(self, obs: Hamiltonian, params: List[float]):
         """grad.
 

quafu/circuits/quantum_circuit.py

@@ -70,6 +70,8 @@ def __init__(self, qnum: int, cnum: Optional[int] = None, name="", *args, **kwar
         self._parameter_grads = {}
         self._variables = []
         self._has_wrap = False
+        self._def_para_name = "theta_inner"  # default parameter name prefix
+        self._para_id = 0  # current parameter index
 
     @property
     def parameterized_gates(self):
@@ -299,6 +301,17 @@ def update_params(self, paras_list: List[Any]):
         for gate, paras in zip(self.parameterized_gates, paras_list):
             gate.update_params(paras)
 
+    def angles2parameters(self):
+        """Transform all float value params to `Parameter`"""
+        for g in self.gates:
+            if all(isinstance(x, float) for x in g.paras):
+                for i in range(len(g.paras)):
+                    g.paras[i] = Parameter(
+                        f"{self._def_para_name}_{self._para_id}", value=g.paras[i]
+                    )
+                    self._para_id += 1
+        self.get_parameter_grads()
+
     def layered_circuit(self) -> np.ndarray:
         """
         Make layered circuit from the gate sequence self.gates.