statistical-learning VS quilc

Yeah, I use CL for data science, despite lack of suitable tools. I even ended up writing my own: https://github.com/sirherrbatka/clusters https://github.com/sirherrbatka/vellum https://github.com/sirherrbatka/vellum-plot https://github.com/sirherrbatka/statistical-learning

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.