FastQuantum is a research project exploring the intersection of machine learning and quantum computing. Its main objective is to develop an AI system capable of predicting the optimal parameters for efficiently running quantum algorithms. In the long term, the ambition is to go even further by creating a model able to predict quantum algorithm results themselves—a challenging goal that remains out of reach for now but guides the project’s future direction.
FastQuantum currently focuses on using Graph Neural Networks (GNNs) and Quantum Neural Networks (QNNs) to learn how to predict optimal parameters for quantum algorithms.
Many quantum algorithms—such as MaxCut or Vertex Cover—can be represented as graphs. This makes GNNs a natural fit: they can capture the structure of the problem instance and learn meaningful patterns directly from the graph topology. In parallel, QNNs allow the model to integrate quantum-inspired representations that may generalize better to circuits with quantum-specific behavior.
Clone the repository and install the required dependencies:
git clone https://github.com/PoCInnovation/FastQuantum.git
cd FastQuantum
pip install -r requirements.txtForthcoming
Forthcoming
You're invited to join this project ! Check out the contributing guide.
If you're interested in how the project is organized at a higher level, please contact the current project manager.
Developers
 Elie Stroun |
 Gregroire Caseaux |
 Noa Smoter |
 Pierre Beaud |
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Manager
 Sacha Henneveux |
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