Deprecate init module #1689
Deprecate init module #1689
Conversation
|
Hello. You may have forgotten to update the changelog!
|
| :class:`Operation <pennylane.operation.Operation>` class. Unless a device knows how to implement this class on a | ||
| quantum computer, it queries the operation's ``expand()`` method, which expresses the operation as a | ||
| decomposition of other operations. More precisely, this method returns a :class:`tape <pennylane.tape.QuantumTape>` | ||
| instance that represents the queue of the decomposition. |
There was a problem hiding this comment.
I sneaked in a little overhaul of this and the following file...
There was a problem hiding this comment.
Conceptually, there is no difference in PennyLane between a template or ansatz and a gate - they are
This is great to have! I have a received a lot of questions regarding the difference lately.
| @@ -37,12 +37,13 @@ def apply(func, qnode_collection): | |||
| As we are using the ``'torch'`` interface, we now apply ``torch.sum`` | |||
| to the QNodeCollection: | |||
|
|
|||
| >>> cost = qml.apply(torch.sum, qnodes) | |||
| >>> cost = qml.collections.apply(torch.sum, qnodes) | |||
There was a problem hiding this comment.
That was a bug...qml.apply is now another function.
| >>> | ||
| >>> with OperationRecorder() as rec: | ||
| >>> qml.templates.layers.StronglyEntanglingLayers(weights, wires=[0, 1]) | ||
| >>> qml.templates.StronglyEntanglingLayers(weights, wires=[0, 1]) |
There was a problem hiding this comment.
cosmetic change...
| @@ -485,7 +485,7 @@ def circuit(weights): | |||
| qml.templates.StronglyEntanglingLayers(weights, wires=[0, 1]) | |||
| return qml.expval(qml.PauliZ(0)) | |||
|
|
|||
| weights = jnp.array(qml.init.strong_ent_layers_normal(n_wires=2, n_layers=2)) | |||
| weights = jnp.array(np.random.random(qml.templates.StronglyEntanglingLayers.shape(n_layers=2, n_wires=2))) | |||
There was a problem hiding this comment.
A bit convoluted, but random number generation with jax is complicated, so I decided to parse here...
| @@ -1430,7 +1430,7 @@ def test_qnode_collection_integration(self): | |||
|
|
|||
| assert qnodes.interface == "tf" | |||
|
|
|||
| weights = tf.Variable(qml.init.strong_ent_layers_normal(n_wires=2, n_layers=2)) | |||
| weights = tf.Variable(np.random.random(qml.templates.StronglyEntanglingLayers.shape(n_layers=2, n_wires=2))) | |||
There was a problem hiding this comment.
We could use the tf.random.uniform function but would have to parse to Variable anyways...
| weights = jnp.array(qml.init.strong_ent_layers_normal(n_wires=2, n_layers=2)) | ||
| weights = jnp.array( | ||
| np.random.random(qml.templates.StronglyEntanglingLayers.shape(n_layers=2, n_wires=2)) | ||
| ) |
There was a problem hiding this comment.
Here and in the following I am creating numpy arrays and parsing them...the reason to not use native jax.random functions is that some tests compare to data produced with a seed, and they fail in such a case.
Codecov Report
@@ Coverage Diff @@
## master #1689 +/- ##
=======================================
Coverage 99.20% 99.20%
=======================================
Files 201 201
Lines 15080 15121 +41
=======================================
+ Hits 14960 15001 +41
Misses 120 120
Continue to review full report at Codecov.
|
There was a problem hiding this comment.
@mariaschuld thanks for getting to this so quickly 🚅 looks great from my end!
Only thing to do before merging is to add this deprecation to the changelog
| :class:`Operation <pennylane.operation.Operation>` class. Unless a device knows how to implement this class on a | ||
| quantum computer, it queries the operation's ``expand()`` method, which expresses the operation as a | ||
| decomposition of other operations. More precisely, this method returns a :class:`tape <pennylane.tape.QuantumTape>` | ||
| instance that represents the queue of the decomposition. |
There was a problem hiding this comment.
Conceptually, there is no difference in PennyLane between a template or ansatz and a gate - they are
This is great to have! I have a received a lot of questions regarding the difference lately.
doc/introduction/templates.rst
Outdated
| shape = BasicEntanglerLayers.shape(n_layers=2, n_wires=n_wires) | ||
| weights = np.random.random(size=shape) |
| qml.inv(bell_state_preparation(wires=[0, 1])) | ||
| return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1)) | ||
| def circuit(a, b): | ||
| MyTemplate(a, b, wires=range(n_wires)) |
|
|
||
| .. code-block:: python3 | ||
|
|
||
| @qml.template |
There was a problem hiding this comment.
👍 Probably we should also remove this from the codebase lol (@antalszava)
| @@ -8,21 +8,10 @@ Templates | |||
|
|
|||
There was a problem hiding this comment.
I haven't visited the template gallery in a while, it's looking really awesome
| @@ -37,12 +37,13 @@ def apply(func, qnode_collection): | |||
| As we are using the ``'torch'`` interface, we now apply ``torch.sum`` | |||
| to the QNodeCollection: | |||
|
|
|||
| >>> cost = qml.apply(torch.sum, qnodes) | |||
| >>> cost = qml.collections.apply(torch.sum, qnodes) | |||
Co-authored-by: Josh Izaac <josh146@gmail.com>
Co-authored-by: Josh Izaac <josh146@gmail.com>
Co-authored-by: Josh Izaac <josh146@gmail.com>
…ane into deprecate_init_module
The init module contains simple functions that generate random arrays of the correct dimension for use in templates. This approach was superseded by having the templates define these functions as a class method. But the module is still in the API reference, and may be used in the docs and demos.
This PR adds deprecation warnings to the functions in the init module and replaces its usage in the docs. It also updates the templates docs here and there, because some sections were outdated.
Drawbacks
A separate PR should remove usage in the demos.