automatic-differentiation

In operations_broadcast_test.go there are some tests that are not yet filled in. The point is to test that broadcasting works for different shapes. The semantics of broadcast probably isn't clear, so please do send me a message for anything.

This is a good first issue for anyone looking to get interested

I'm using TF 2.0, and I get this error when I import tangent, due to a list of non-differentiable functions that includes tf.to_float (line 60), which is deprecated:

https://www.tensorflow.org/versions/r1.14/api_docs/python/tf/to_float

Feature details

Due to the similarity, it is easy to confuse qml.X and qml.PauliX, especially since other methods of specifying circuits, e.g., QASM, use x for PauliX. But if a user uses qml.X in their circuit on a qubit device, nothing happens to inform them that the incorrect operation is being used:

@qml.qnode(dev)
def circ():
    qml.PauliX(wires=0)
    qml.Hada

I found that function mod2pi is not implemented yet, but mod works. Is there any list of implemented functions? Minimal working example is:

using Zygote

# This is working
gradient(x -> mod(x, 2pi), 1.)

# This is not
gradient(x -> mod2pi(x), 1.)

https://en.cppreference.com/w/c/numeric/math/logb

This section of the Scan documentation has several issues:

https://aesara.readthedocs.io/en/latest/library/scan.html#using-shared-variables-gibbs-sampling

1. Binomial no longer accepts n, p as keyword arguments
2. Outputs info is wrong because it uses a float vector, but the returned output type is int64
3. aesara.dot is no longer a thing
4. updates are not actually optional, t

Description

Describe the issue as clearly as possible. Issues are meant for bug reports and feature requests. The issue should contain enough information for a developer to put together a solution.

If this is a general question, please post to the forums.

Example

Here's the current errors.

mkdir -p doc/api
doxygen doxygen/doxygen.cfg
warn

Debugging Kotlin∇ code within IntelliJ IDEA can be somewhat cumbersome due to the functional API structure (lots of deeply-nested stack traces and context switching). To facilitate more user-friendly debugging, we should add support for visual debugging by exposing Kaliningraph’s built-in graph visualization capabilities. For example, the use

The init module has been deprecated, and the recommend approach for generating initial weights is to use the Template.shape method:

>>> from pennylane.templates import StronglyEntanglingLayers
>>> qml.init.strong_ent_layers_normal(n_layers=3, n_wires=2) # deprecated
>>> np.random.random(StronglyEntanglingLayers.shape(n_layers=3, n_wires=2))  # new approach

We should upd

Lots has changed since the docs were first written. #152 addresses a number of things, but there are a few more things that we might want to consider:

• changing all references to autodiff / automatic differentiation to AD / algorithmic differentiation, with a terminology box in the docs somewhere, explaining what we're on about.
• In the "On writing good rrule and frule " bit, we should consi

At the moment profiles.h (in pkg/profiles) lacks many (any?) comments. Also lots of variables are declared somewhat separately from where they are associated with heap storage.

Both these make it a bit hard to read.

It would be nicer if it was called PROFILES.h too.