added weights initialization example (#2735)

pennylane/qnn/torch.py

@@ -104,7 +104,10 @@ def qnode(inputs, weights_0, weight_1):
 
         **Initializing weights**
 
-        The optional ``init_method`` argument of :class:`~.TorchLayer` allows for the initialization
+        If ``init_method`` is not specified, weights are randomly initialized from the uniform
+        distribution on the interval :math:`[0, 2 \pi]`.
+
+        Alternative a): The optional ``init_method`` argument of :class:`~.TorchLayer` allows for the initialization
         method of the QNode weights to be specified. The function passed to the argument must be
         from the `torch.nn.init <https://pytorch.org/docs/stable/nn.init.html>`__ module. For
         example, weights can be randomly initialized from the normal distribution by passing:
@@ -113,8 +116,39 @@ def qnode(inputs, weights_0, weight_1):
 
             init_method = torch.nn.init.normal_
 
-        If ``init_method`` is not specified, weights are randomly initialized from the uniform
-        distribution on the interval :math:`[0, 2 \pi]`.
+        Alternative b): Two dictionaries ``weight_shapes`` and ``init_method`` are passed, whose ``keys`` match the ``args`` of the qnode.
+
+        .. code-block::
+
+            @qml.qnode(dev)
+            def qnode(inputs, weights_0, weights_1, weights_2, weight_3, weight_4):
+                qml.templates.AngleEmbedding(inputs, wires=range(n_qubits))
+                qml.templates.StronglyEntanglingLayers(weights_0, wires=range(n_qubits))
+                qml.templates.BasicEntanglerLayers(weights_1, wires=range(n_qubits))
+                qml.Rot(*weights_2, wires=0)
+                qml.RY(weight_3, wires=1)
+                qml.RZ(weight_4, wires=1)
+                qml.CNOT(wires=[0, 1])
+                return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))
+
+
+            weight_shapes = {
+                "weights_0": (3, n_qubits, 3),
+                "weights_1": (3, n_qubits),
+                "weights_2": 3,
+                "weight_3": 1,
+                "weight_4": (1,),
+            }
+
+            init_method = {
+                "weights_0": torch.nn.init.normal_,
+                "weights_1": torch.nn.init.uniform,
+                "weights_2": torch.tensor([1., 2., 3.]),
+                "weight_3": torch.tensor(1.),  # scalar when shape is not an iterable and is <= 1
+                "weight_4": torch.tensor([1.]),
+            }
+
+            qlayer = qml.qnn.TorchLayer(qnode, weight_shapes=weight_shapes, init_method=init_method)
 
         **Full code example**