qvm VS pip

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

Whenever you are working on a Python project that has external dependencies installed with pip, it is strongly recommended to first create a virtual environment.

Unfortunately that feature is easy to break: https://github.com/pypa/pip/issues/9644

I think I did my part https://github.com/pypa/pip/issues/6409

$ sudo pip install y$ sudo pip install yaml WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip. Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue. To avoid this problem you can invoke Python with '-m pip' instead of running pip directly. ERROR: Could not find a version that satisfies the requirement yaml (from versions: none) ERROR: No matching distribution found for yaml

What are you implying will happen?

Using the build-in tools, you can save the exact versions of dependencies (i.e. a lock file) using "pip freeze >dependencies.txt". This should give you the exact same set of packages in two years' time.

If you want to be even more sure, you can also store hashes in the lock file. This has to be generated by a separate tools at the moment [1][2] but can be consumed by the built-in tools [3], so "pip install -r requirements.txt" is still all you need in two years' time.

[1] https://github.com/pypa/pip/issues/4732

[2] https://pip-tools.readthedocs.io/en/latest/#using-hashes

[3] https://pip.pypa.io/en/stable/topics/secure-installs/#hash-c...

Here’s the issue, https://github.com/pypa/pip/issues/11664. I think the idea would be to have some file/json description of environment that could be passed to pip to allow it to fully cross compile. They are open to supporting it just needs contributor to be found to implement it and go through review/discussion.

To be fair the only alternative is fixing Python, and even then you still would have to wait a good 5 years at least for all the old Python versions to dwindle.

It doesn't look like the fixing effort is progressing very quickly: https://github.com/pypa/pip/issues/8606

To their credit, at least they didn't close it "works as intended" which I imagine a lot of projects would.

Another good benchmark to trying to resolve apache-airflow[all]==1.10.13 using the state of PyPi on 2020-12-02, I give instructions here on how to reproduce that workflow: https://github.com/pypa/pip/issues/11836. Including a benchmark how how many extra packages your resolver should visit.