quilc VS Petalisp

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.

While not "as fast as C" (C is not the absolute pinnacle of performance), Common Lisp is incredibly fast compared to the majority of programming languages around today. There is even a huge amount of ongoing work being done to make it faster still. We are seeing many interesting projects that make better use of the hardware in your computer (e.g. https://github.com/marcoheisig/Petalisp).

i think lisp-stat library is actually being developed. however one numerical cl library that doesnt get enough mention and is being constantly developed is petalisp for HPC

https://github.com/marcoheisig/Petalisp

However, if you have a lisp library that puts those semantics to use, then you could get it to employ magicl/ext-blas and cl-bmas to speed it up. (petalisp looks relevant, but I lack the background to compare it with APL.)

> his means cl pagckages can be "done".

this is true if there is nothing functional that can be added to a package. however its very much not true for ml frameworks right now. new things are being added all the time in the field. however even in the package i linked you have the necessary ingredients for any deep learning model: cuda and back propagation. the other person mentioned convolution which i think is pretty trivial to implement but still, if you expect everything for you to be ready made then you should probably stick to tf and pytorch. if you want to explore the cutting edge and push the boundaries then i think common lisp is a good tool. as an aside it might also be interesting to note that a common lisp package (Petalisp) is being used for high performance computing by a german university

https://github.com/marcoheisig/Petalisp

One trick is that Common Lisp can generate and compile code at runtime, whereas static languages typically do not have a compiler available at runtime. This lets you make your own lazy person's JIT/staged compiler, which is useful if some part of the problem is not known at compile-time. Such an approach has been used at least for array munging, type munging and regular expression munging.