Merge pull request #1029 from qiboteam/docs

Parameter docs

doc/source/api-reference/qibo.rst

@@ -1189,6 +1189,24 @@ variational model.
    :exclude-members: ParallelBFGS
 
 
+.. _Parameter:
+
+Parameter
+---------
+
+It can be useful to define custom parameters in an optimization context. For
+example, the rotational angles which encodes information in a Quantum Neural Network
+are usually built as a combination of features and trainable parameters. For
+doing this, the :class:`qibo.parameter.Parameter` class can be used. It allows
+to define custom parameters which can be inserted into a :class:`qibo.models.circuit.Circuit`.
+Moreover, it automatically precomputes the analytical derivative of the parameter
+function, which can be used to calculate the derivatives of a variational model
+with respect to its parameters.
+
+.. automodule:: qibo.parameter
+    :members:
+    :member-order: bysource
+
 .. _Gradients:
 
 Gradients

src/qibo/parameter.py

@@ -5,7 +5,7 @@
 
 
 def calculate_derivatives(func):
-    """Calculates derivatives w.r.t to all parameters of a target function `func`."""
+    """Calculates derivatives w.r.t. to all parameters of a target function `func`."""
     vars = []
     for i in range(func.__code__.co_argcount):
         vars.append(sp.Symbol(f"p{i}"))
@@ -43,11 +43,10 @@ class Parameter:
 
     Args:
         func (function): lambda function which builds the gate parameter. If both features and trainable parameters
-        compose the function, it must be passed by first providing the features and then the parameters, as
-        described in the code example above.
+            compose the function, it must be passed by first providing the features and then the parameters, as
+            described in the code example above.
         features (list or np.ndarray): array containing possible input features x.
         trainable (list or np.ndarray): array with initial trainable parameters theta.
-        nofeatures (bool): flag to explicitly ban the updating of the features. This simplifies the task of updating Parameter objects simultaneously when some have embedded features and some do not.
     """
 
     def __init__(self, func, trainable=None, features=None):
@@ -106,7 +105,7 @@ def nfeat(self):
 
     @property
     def ncomponents(self):
-        """Returns the number of elements which compose the Parameter"""
+        """Return the number of elements which compose the Parameter"""
         return self.nparams + self.nfeat
 
     def trainable_parameter_indices(self, start_index):