munich-quantum-toolkit / munich-quantum-toolkit/predictor
✨ Add GNN-based classifier
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 87
- Forks
- 23
- Avg merge
- 6h 23m
- Merged PRs (30d)
- 35
Description
What's the problem this feature will solve?
Recent work with GNNs has explored the idea of using Graph-Neural-Networks to extract information from quantum circuits (e.g.; https://arxiv.org/abs/2507.19093, or https://arxiv.org/abs/2210.16724).
Describe the solution you'd like
It would be nice to use a GNN for predicting the most suitable device in the ML part and compare the results against the RandomForest approach.
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 examining the existing ML implementation and its RandomForest approach, then trace how quantum circuits and device-selection data enter the predictor. The issue names no files or tests, so the implementation entry point must be located in the repository. Done means adding a GNN-based classifier and comparing its device-prediction results with RandomForest.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, quantum-computing
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100