Quantum Circuit Optimisation and Compilation using the ZX-calculus

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QuiZX: A quick backend for PyZX

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PyZX is a Python library for quantum circuit optimisation and compiling using the ZX-calculus. It's great for hacking, learning, and trying things out in Jupyter notebooks. However, it's written to maximise clarity and fun, not performance.

This is a port of some of the core functionality of PyZX to the Rust programming language. This is a modern systems programming language, which enables writing software that is very fast and memory efficient.

See the CONTRIBUTING.md file for detailed instructions for building the libraries from source and contributing to the project.

A bit about performance

As a very anecdotal example of the performance difference, the program spider_chain builds a chain of 1 million green spiders and fuses them all. In PyZX, you can fuse all the spiders in a ZX-diagram as follows:

from pyzx.basicrules import *

success = True
while success:
    success = any(fuse(g, g.edge_s(e), g.edge_t(e)) for e in g.edges())

In QuiZX, the Rust code is slightly more verbose, but similar in spirit:

use quizx::basic_rules::*;

loop {
    match g.find_edge(|v0,v1,_| check_spider_fusion(&g, v0, v1)) {
        Some((v0,v1,_)) => spider_fusion_unchecked(&mut g, v0, v1),
        None => break,
    };
}

On my laptop, the PyZX code takes about 98 seconds to fuse 1 million spiders, whereas the QuiZX code takes 17 milliseconds.

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