Add Keras layers support

pennylane/qnn/keras.py

@@ -73,7 +73,8 @@ class KerasLayer(Layer):
 
         @qml.qnode(dev)
         def qnode(inputs, weights_0, weight_1):
-            qml.RX(inputs, wires=0)
+            qml.RX(inputs[0], wires=0)
+            qml.RX(inputs[1], wires=1)
             qml.Rot(*weights_0, wires=0)
             qml.RY(weight_1, wires=1)
             qml.CNOT(wires=[0, 1])
@@ -89,6 +90,39 @@ def qnode(inputs, weights_0, weight_1):
 
     .. UsageDetails::
 
+        The QNode must have a signature that satisfies the following conditions:
+
+        - Contain an ``inputs`` named argument for input data.
+        - All other arguments must accept an array or tensor and are treated as internal
+          weights of the QNode.
+        - All other arguments must have no default value.
+        - The ``inputs`` argument is permitted to have a default value provided the gradient with
+          respect to ``inputs`` is not required.
+        - There cannot be a variable number of positional or keyword arguments, e.g., no ``*args``
+          or ``**kwargs`` present in the signature.
+
+        The optional ``weight_specs`` argument allows for a more fine-grained
+        specification of the QNode weights, such as the method of initialization and any
+        regularization or constraints. For example, the initialization method of the ``weights``
+        argument in the example above could be specified by:
+
+        .. code-block::
+
+            weight_specs = {"weights": {"initializer": "random_uniform"}}
+
+        The values of ``weight_specs`` are dictionaries with keys given by arguments of
+        the Keras
+        `add_weight() <https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer#add_weight>`__
+        method. For the ``"initializer"`` argument, one can specify a string such as
+        ``"random_uniform"`` or an instance of an `Initializer
+        <https://www.tensorflow.org/api_docs/python/tf/keras/initializers>`__ class, such as
+        `tf.keras.initializers.RandomUniform <https://www.tensorflow.org/api_docs/python/tf/random_uniform_initializer>`__.
+
+        If ``weight_specs`` is not specified, weights will be added using the Keras default
+        initialization and without any regularization or constraints.
+
+        **Additional example**
+
         The code block below shows how a circuit composed of templates from the
         :doc:`/code/qml_templates` module can be combined with classical
         `Dense <https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense>`__ layers to learn
@@ -106,8 +140,8 @@ def qnode(inputs, weights_0, weight_1):
 
             @qml.qnode(dev)
             def qnode(inputs, weights):
-                qml.templates.AngleEmbedding(inputs, wires=list(range(n_qubits)))
-                qml.templates.StronglyEntanglingLayers(weights, wires=list(range(n_qubits)))
+                qml.templates.AngleEmbedding(inputs, wires=range(n_qubits))
+                qml.templates.StronglyEntanglingLayers(weights, wires=range(n_qubits))
                 return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))
 
             weight_shapes = {"weights": (3, n_qubits, 3)}
@@ -145,42 +179,8 @@ def qnode(inputs, weights):
         Epoch 8/8
         100/100 [==============================] - 9s 87ms/sample - loss: 0.1474
 
-        The QNode must have a signature that satisfies the following conditions:
-
-        - Contain an ``inputs`` named argument for input data.
-        - All other arguments must accept an array or tensor and are treated as internal
-          weights of the QNode.
-        - All other arguments must have no default value.
-        - The ``inputs`` argument is permitted to have a default value provided the gradient with
-          respect to ``inputs`` is not required.
-        - There cannot be a variable number of positional or keyword arguments, e.g., no ``*args``
-          or ``**kwargs`` present in the signature.
-
-        The optional ``weight_specs`` argument allows for a more fine-grained
-        specification of the QNode weights, such as the method of initialization and any
-        regularization or constraints. For example, the initialization method of the ``weights``
-        argument in the example above could be specified by:
-
-        .. code-block::
-
-            weight_specs = {"weights": {"initializer": "random_uniform"}}
-
-        The values of ``weight_specs`` are dictionaries with keys given by arguments of
-        the Keras
-        `add_weight() <https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer#add_weight>`__
-        method. For the ``"initializer"`` argument, one can specify a string such as
-        ``"random_uniform"`` or an instance of an `Initializer
-        <https://www.tensorflow.org/api_docs/python/tf/keras/initializers>`__ class, such as
-        `tf.keras.initializers.RandomUniform <https://www.tensorflow.org/api_docs/python/tf/random_uniform_initializer>`__.
-
-        If ``weight_specs`` is not specified, weights will be added using the Keras default
-        initialization and without any regularization or constraints.
-
     .. _Layer: https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer
     """
-
-    input_arg = "inputs"
-
     def __init__(
         self, qnode, weight_shapes: dict, output_dim, weight_specs: Optional[dict] = None, **kwargs
     ):
@@ -294,3 +294,12 @@ def __str__(self):
         return detail.format(self.qnode.func.__name__)
 
     __repr__ = __str__
+
+    _input_arg = "inputs"
+
+    @property
+    def input_arg(self):
+        """Name of the argument to be used as the input to the Keras
+        `Layer <https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer>`__. Set to
+        ``"inputs"``."""
+        return self._input_arg

tests/qnn/test_keras.py

@@ -107,7 +107,7 @@ def test_no_input(self, get_circuit, output_dim):
         """Test if a TypeError is raised when instantiated with a QNode that does not have an
         argument with name equal to the input_arg class attribute of KerasLayer"""
         c, w = get_circuit
-        del c.func.sig[qml.qnn.keras.KerasLayer.input_arg]
+        del c.func.sig[qml.qnn.keras.KerasLayer._input_arg]
         with pytest.raises(TypeError, match="QNode must include an argument with name"):
             KerasLayer(c, w, output_dim)
 
@@ -118,11 +118,11 @@ def test_input_in_weight_shapes(self, get_circuit, n_qubits, output_dim):
         contains the shape of the input argument given by the input_arg class attribute of
         KerasLayer"""
         c, w = get_circuit
-        w[qml.qnn.keras.KerasLayer.input_arg] = n_qubits
+        w[qml.qnn.keras.KerasLayer._input_arg] = n_qubits
         with pytest.raises(
             ValueError,
             match="{} argument should not have its dimension".format(
-                qml.qnn.keras.KerasLayer.input_arg
+                qml.qnn.keras.KerasLayer._input_arg
             ),
         ):
             KerasLayer(c, w, output_dim)
@@ -218,7 +218,7 @@ def c_dummy(inputs, w1, w2, w3, w4, w5, w6, w7, w8=None):
 
         with pytest.raises(
             TypeError,
-            match="Only the argument {} is permitted".format(qml.qnn.keras.KerasLayer.input_arg),
+            match="Only the argument {} is permitted".format(qml.qnn.keras.KerasLayer._input_arg),
         ):
             KerasLayer(c_dummy, {**w, **{"w8": 1}}, output_dim)