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

It seems that the utility function qml.utils.expand is no longer used anywhere as of #2609.
It therefore could be removed. A differentiable, more up-to-date version is available in qml.operation.expand_matrix.

Implementation

No response

How important would you say this feature is?

1: Not important. Would be nice to have.

Additional information

_No resp

As mentioned in FluxML/Zygote.jl#1212.

https://llvm.org/docs/NewPassManager.html

The tricky part is to keep our custom command line options working.

Currently, we are not running CI with Mac OS so verifying continued correct behavior on OS X is more difficult. Github Actions provides access to Mac OS Intel machines so we may want to start running these in CI.

Description

Add adjoint-Jacobian specialization for reverse mode for the fast Fourier transform (FFT) and its inverse.

Example

FFT case

If y = fft(x), then the adjoint-Jacobian is just the inverse FFT applied to the adjoint of the result,

adjoint(x) += ifft(adjoint(y))

Inverse FFT case

If y = ifft(x), then the adjoint-Jacobian update rule is inve

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.