QuipNetwork/xq-pyPublic

A python implementation of the Quip Network's quantum virtual machine

AI summary: A Python implementation of the Quip Network's quantum virtual machine.

Stars
5.5K
Forks
14
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2
Open issues
0
Open PRs
1
Contributors
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Commits
3
Branches
3

No licenseCreated Apr 2, 2026Last push 2mo ago+-8 this month

Star history

since Apr 5, 2026
02K4KApr 2026May 2026Jun 2026Aug 2026
5.5K stars as of Aug 7, 2026, tracked back to Apr 5, 2026. Historical curve reconstructed from public GitHub event archives, calibrated to the current total.

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3 commits in the last yearLessMore

What xq-py does

xq-py is a Python-based implementation of the Quip Network's quantum virtual machine, designed to make quantum simulations highly accessible to researchers and data scientists. While not as fast as its Rust counterpart, this Python port prioritizes ease of use, rapid prototyping, and seamless integration with the extensive Python scientific ecosystem (like NumPy and SciPy). It allows users to construct, simulate, and debug quantum circuits directly in Jupyter notebooks, acting as an educational and research bridge to the Quip Network.

Data scientists, quantum researchers, and educators who prefer the Python ecosystem for simulation and prototyping.

  • Native Python API: provides a Pythonic interface for building and running quantum circuits without compiling code.
  • Jupyter integration: perfect for interactive exploration and visualization of quantum states within notebooks.
  • Scientific ecosystem compatibility: easily interfaces with NumPy arrays for advanced mathematical analysis of quantum states.
  • Rapid prototyping: allows developers to quickly test algorithm logic before implementing highly optimized versions.
  • Educational focus: provides clear, readable code to help students understand the inner workings of a quantum VM.

Where teams use it

Interactive Research

Data scientists use the library in Jupyter notebooks to interactively analyze the probability distributions of quantum states.

Algorithm Prototyping

Researchers quickly draft and test new quantum algorithms in Python before committing to a production implementation.

Educational Workshops

Instructors use the Python VM in classrooms to teach quantum computing concepts without requiring complex build setups.

Integration Testing

Developers use the Python implementation to write high-level integration tests for the broader Quip ecosystem.

Getting started: pip install xqvm-py

README

main branch

xqvm-py

Getting started

To make it easy for you to get started with GitLab, here's a list of recommended next steps.

Already a pro? Just edit this README.md and make it your own. Want to make it easy? Use the template at the bottom!

Add your files

cd existing_repo
git remote add origin https://gitlab.com/piqued/xqvm-py.git
git branch -M main
git push -uf origin main

Integrate with your tools

Collaborate with your team

Test and Deploy

Use the built-in continuous integration in GitLab.


Editing this README

When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to makeareadme.com for this template.

Suggestions for a good README

Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.

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Usage

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Apr 10, 2026daily#8+341