Every Algorithm has a Beginning!
The success of a QAOA depends, among many factors, on the choice of initial parameters. Choose poorly, and training will be lengthy and complex. Choose wisely, and you may stand a chance to see the QAOA succeed!
Finding good initial parameters for the Quantum Approximate Optimisation Algorithm is very complex. In this task, you'll have a go at this problem!
Don't worry too much about the solution: the goal is for you to have fun discovering QAOA and trying to think about how to improve quantum optimisation through classical methods.
First, you will need to install OpenQAOA. The simplest way to do that is
- In a new environment, install OpenQAOA. It is simple; you just need to run
pip install openqaoa:)
(if you face issues with the instal, give us a shout! Most likely, you can fix most errors by simply creating a new environment running on python3.8: follow this tutorial from Conda and then install openqaoa via pip )
- Now that you are set, you can start exploring OpenQAOA. To get started, you can:
- Check out OpenQAOA's official documentation: - Run your first OpenQAOA workflow
- Check out the intro notebooks such as OpenQAOA_tutorial.ipynb that have been created especially for this challenge
- Go to the challenge notebook!
If you need help, the fastest way for us to get back to you is to jump on our discord.
And remember, OpenQAOA is an opensource package, and as such, we always look for new collaborators :)