EngiOptiQA: Engineering Optimization with Quantum Algorithms

Please note: EngiOptiQA is currently in a very early stage of development. As the project progresses, documentation, additional features, and enhancements will be added.

Overview

EngiOptiQA is a Python software library dedicated to Engineering Optimization with Quantum Algorithms. This project provides a set of tools to formulate engineering optimization problems suitable for quantum algorithms, including quantum annealing (QA) and variational quantum algorithms (VQAs) such as the quantum approximate optimization algorithm (QAOA).

A minimal documentation can be found under https://engioptiqa.github.io/EngiOptiQA/. To learn more about the background of EngiOptiQA and the implemented problem formulations, please refer to the corresponding publication [1].

Citation

If you use EngiOptiQA in your research or work, please consider citing it using the software's DOI and the corresponding publication Key2024.

Quick Example

Run this example for the design optimization of a rod under self-weight loading presented in Key2024, Section 3.2, solved using simulated annealing (SA):

pip install -r requirements.txt
python3 examples/structural/rod_1d/design_optimization_sa.py

The expected relative $H_1$ error for the force function of the best solution is approximately $1.59 \times 10^{-2}$:

Force:
   Rel. H1 error 1.5873e-02

Funding

This research was funded in whole or in part by the Austrian Science Fund (FWF) 10.55776/ESP2444325.

License

This project is licensed under the MIT License - see the LICENSE file for details.

References

1. Key F, Freinberger L. A Formulation of Structural Design Optimization Problems for Quantum Annealing. Mathematics. 2024; 12(3):482. https://doi.org/10.3390/math12030482