munich-quantum-toolkit / munich-quantum-toolkit/predictor
✨ add GNN for device selection
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Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 87
- Forks
- 23
- Avg merge
- 6h 23m
- Merged PRs (30d)
- 35
Description
Use a GNN to produce a circuit embedding and add it as an alternative to the random forest for device selection.
Mostly implemented by #430
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing issue #430, which the issue identifies as containing most of the implementation. Trace how the existing random-forest device-selection path represents circuits and determine where an alternative GNN embedding can be integrated. Done means device selection supports the GNN alternative alongside the random forest.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 45/100