quilc VS magicl

Yes, they use it for their quantum compiler, at RHL Laboratories (it was maybe initiated even at Rigetti). https://github.com/quil-lang/quilc

QUILC is probably the most interesting project. It is an open-source automatic, retargetable, optimizing compiler for Quil. It can take nearly any quantum computer architecture description and compile+optimize a Quil program for that architecture.

The QVM does all manner of quantum computer simulations. It specifically simulates a Quil program, with both classical and quantum operators. The QVM has lots of different modes of operation:

You can do this with a compiler like quilc. A program like

This is a new physical implementation of a particular mathematical operation, on a specific modality of qubit. The same mathematical operation can be computed on any other quantum computer in production today; very easily so if you use a compiler like QUILC [0].

[0] https://github.com/quil-lang/quilc

For people who maybe already have a job, just want to dip their toes in, or some other life thing that prohibits them from being employed, I’ve done pro bono mentorship sessions to interested individuals, helping them contribute to open source software around this stack, like the quantum compiler. Always happy to discuss that for serious applicants.

compiler code

Yes, simulator, compiler, paper is some of it.

Speaking about Common Lisp, the only commercial-level compiler implementation that I know of is https://github.com/rigetti/quilc by /u/stylewarning et al.

(Link didn't work for me)

https://github.com/quil-lang/magicl/blob/master/src/high-lev...

use MAGICL. [1] It is optionally and transparently accelerated by BLAS/LAPACK.

[1] https://github.com/quil-lang/magicl/blob/master/doc/high-lev...

Maybe have a look at how magicl does this?

This is essentially the first option, which is already supported by MAGICL by loading MAGICL/EXT-LAPACK [1].

[1] https://github.com/quil-lang/magicl#extensions

I think what the magicl team has done is brilliant - allowing multiple implementations is awesome.

Second up is magicl, especially useful if performance is a concern. This might not be as extensive as numcl, but it's been battle tested in the industry over the last decade or so. Because this uses generic functions, so long as you are using not-very-small arrays, performance should not be a concern for you. And even if you are, you could write your own functions that use the low-level functions that magicl's backends define. Otherwise performance can be at par with numpy.

This Dev How-To describes (I hope in enough detail) how to add these specialized routines to MAGICL.

I agree... and I do don't want be the person who has not rallied. I just took a look at guicho's issue from 2019. And here, you yourself have admitted that the high level interface is less than ideal and needs more work. However, the very point that magicl is an industry standard could imply that potentially radical backward-incompatible changes can be hard. But, honestly, I want to discuss this, time permitting!

Common Lisp is a great language to build new tools for data science, but currently has pretty awful library support existing data science workflows. Common Lisp is sorely lacking in high-quality statistics, plotting, and sparse arrays. There’s been a long work-in-progress library to bring flexible and high-performance linear algebra to Lisp, but it needs more contributors.