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.)

Generate code coverage reports for our tests.

[edit: generalized for vectors by @bob-carpenter]

Add this signature for to_matrix:

array[N_cols] vector[N_rows] arr_Vec ;
matrix[N_rows,N_cols] mat_from_arr_Vec = to_matrix(arr_Vec) ;//proposed handling of new signature

and general signatures for to_vector for any container:

vector[N] to_vector(some container);

The reason to do this is to make it

We should have consistent import paths for control flow. The scan function is available in the aesara namespace:

import aesara

values, updates = aesara.scan(...)

On the other hand ifelse is only available in the aesara.ifelse namespace:

import aesara

out = aesara.ifelse.ifelse(...)

We should proceed with care however. ifelse.py is in the

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.