Update CHANGELOG for release v0.10

.github/CHANGELOG.md

@@ -36,7 +36,7 @@
   [documentation](https://pennylane.ai/en/stable/code/api/pennylane.beta.plugins.DefaultQubitTF.html)
   for more details.
 
-* The ``default.tensor`` plugin has been significantly upgraded. It now allows two different
+* The [``default.tensor`` plugin](https://github.com/XanaduAI/pennylane/blob/master/pennylane/beta/plugins/default_tensor.py) has been significantly upgraded. It now allows two different
   tensor network representations to be used: `"exact"` and `"mps"`. The former uses a 
   exact factorized representation of quantum states, while the latter uses a matrix product state 
   representation.
@@ -51,37 +51,52 @@
 
   A PennyLane QNode can be converted into a `torch.nn` layer using the `qml.qnn.TorchLayer` class:
 
-  ```python
-  @qml.qnode(dev)
-  def qnode(inputs, weights_0, weight_1):
-     # define the circuit
-     # ...
+  ```pycon
+  >>> @qml.qnode(dev)
+  ... def qnode(inputs, weights_0, weight_1):
+  ...    # define the circuit
+  ...    # ...
 
-  weight_shapes = {"weights_0": 3, "weight_1": 1}
-  qlayer = qml.qnn.TorchLayer(qnode, weight_shapes)
+  >>> weight_shapes = {"weights_0": 3, "weight_1": 1}
+  >>> qlayer = qml.qnn.TorchLayer(qnode, weight_shapes)
   ```
 
   A hybrid model can then be easily constructed:
 
-  ```python
-  model = torch.nn.Sequential(qlayer, torch.nn.Linear(2, 2))
+  ```pycon
+  >>> model = torch.nn.Sequential(qlayer, torch.nn.Linear(2, 2))
   ```
 * Added a new "reversible" differentiation method which can be used in simulators, but not hardware.
+
   The reversible approach is similar to backpropagation, but trades off extra computation for
   enhanced memory efficiency. Where backpropagation caches the state tensors at each step during
-  a simulated evolution, the reversible method only caches the final pre-measurement state. Compared to 
-  the parameter-shift method, the reversible method can be faster or slower, depending on the 
-  density and location of parametrized gates in a circuit (circuits with higher density of 
-  parametrized gates near the end of the circuit will see a benefit).
+  a simulated evolution, the reversible method only caches the final pre-measurement state. 
+
+  Compared to the parameter-shift method, the reversible method can be faster or slower, 
+  depending on the density and location of parametrized gates in a circuit 
+  (circuits with higher density of   parametrized gates near the end of the circuit will see a benefit).
   [(#670)](https://github.com/XanaduAI/pennylane/pull/670)
 
+  ```pycon
+  >>> dev = qml.device("default.qubit", wires=2)
+  ... @qml.qnode(dev, diff_method="reversible")
+  ... def circuit(x):
+  ...     qml.RX(x, wires=0)
+  ...     qml.RX(x, wires=0)
+  ...     qml.CNOT(wires=[0,1])
+  ...     return qml.expval(qml.PauliZ(0))
+  >>> qml.grad(circuit)(0.5)
+  (array(-0.47942554),)
+  ```
+
 <h4>New templates and cost functions</h4>  
 
 * Added the new templates `UCCSD`, `SingleExcitationUnitary`, and`DoubleExcitationUnitary`,
   which together implement the Unitary Coupled-Cluster Singles and Doubles (UCCSD) ansatz
   to perform VQE-based quantum chemistry simulations using PennyLane-QChem.
-  [(#654)](https://github.com/XanaduAI/pennylane/pull/654)
+  [(#622)](https://github.com/XanaduAI/pennylane/pull/622)
   [(#638)](https://github.com/XanaduAI/pennylane/pull/638)
+  [(#654)](https://github.com/XanaduAI/pennylane/pull/654)
   [(#659)](https://github.com/XanaduAI/pennylane/pull/659)
   [(#622)](https://github.com/XanaduAI/pennylane/pull/622)