The code in this repository implements the algorithms and simulations in Approaches to Constrained Quantum Approximate Optimization. The ansatz\ directory contains multiple files which implement different variational ansatzes for solving the Maximum Independent Set (MIS) problem. The functions for executing the variational optimizations are in mis.py, each different ansatz has its own function and take a target graph as input.
The code in this repo can be installed as a Python module by following the instructions below. Either Python 3.6 or a newer version should be used. We highly recommend the use of Python virtual environments to simplify the installation experience and reduce the chance of package conflicts.
git clone https://github.com/teaguetomesh/quantum-constrained-optimization.git
python3 -m venv your_new_venv # create a new Python virtual environment
source your_new_venv/bin/activate # activate that virtual environment
cd quantum-constrained-optimization
pip install -r requirements.txt # install the package dependecies
pip install -e . # install the qcopt packageAfter the qcopt package has been installed it can be imported into your Python scripts via:
import qcoptIf you use the code in this repository, please cite our paper:
Zain H. Saleem, Teague Tomesh, Bilal Tariq, and Martin Suchara, Approaches to
Constrained Quantum Approximate Optimization, arXiv preprint, arXiv:2010.06660 (2021).