qvm VS magicl

Quil's semantics are based off of an idea called the "quantum abstract machine". A piece of software which emulates the quantum abstract machine is called the Quantum Virtual Machine or QVM. It's open source and available here.

More interestingly, the QVM repository includes a program called the dqvm which is the QVM but able to be run on an MPI cluster. This doesn't use any advanced state representation (such as matrix product states) and instead just very cleverly arranges for huge wavefunctions to be distributed across a cluster of arbitrary size and worked on in parallel.

and/or in-line assembly code, and still can't optimize specific matrix shapes and structures, or do algebraic simplifications to eliminate work altogether.

The regex library FTA is a great, and clean, example of a long standing practice of compiling regexen, except it doesn't use any fancy VMs or any fancy JITs, just "when you see this regex, automatically turn it into this Common Lisp code, and let the Lisp compiler handle the rest."

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

[0b] COMPILE-OPERATOR: https://github.com/quil-lang/qvm/blob/master/src/compile-gat...

[1] https://www.gnu.org/software/libjit/

quantum computer simulator

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

You can also do this with purely free and open source software like [0].

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

I wish I could try running the DQVM, the distributed quantum simulator written in Common Lisp [0], on this thing.

[0] https://github.com/quil-lang/qvm/tree/master/dqvm

(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.