can we use Jax instead of autograde for greater speed while training complex circuits?
Hi @kareem_essafty! Not at the moment, but this is on our radar as a feature to add
I’d love to get involved please voluntarily
We welcome community contributions
Note that we maintain a high standard of code in PennyLane (including thorough documentation & testing). Please see here for our guidelines, in particular the section on Pull Requests.
Feel free to hack away at a jax interface and see whether you can get it working. It’s best to keep lines of communication with the pennylane dev team open if you’ll be working on it.
So sorry for the late reply, I’ll start working on this asap. Besides that, is it okay if I use cython syntax or something like predefined c files that contain the repeated operations?
Hi @kareem_essafty, is there any particular reason you don’t want to write in pure python (like the rest of PL)? Something particular to Jax?
Apart from jax or autograd, based on my humble experience tensor operations or matrix multiplication in general are a bit time consuming. Defining these using cython or in c++ and use any binding method does actually help. In computer vision for example i always encounter some delays and using c++ or opencl with python actually makes it really fast.
Regarding jax, I only want to see pennylane natively supporters GPU, it is a wonderful quantum machine learning library and you sir and the wonderful team have made the qubit simulator actually faster and better than the earlier versions.