GSoC 2025 Projects Related To QMLHEP

QMLHEP project implements Quantum Machine Learning methods for physics analysis.

Project Proposals

• Implementation of Quantum Generative Adversarial Networks to Perform High Energy Physics Analysis at the LHC

• Quantum Kolmogorov-Arnold Networks for High Energy Physics Analysis at the LHC

• Quantum Foundation Model for High Energy Physics

• Quantum Machine Learning for Exoplanet Characterization

• Quantum Graph Neural Networks for High Energy Physics Analysis at the LHC

• Equivariant quantum neural networks for High Energy Physics Analysis at the LHC

• Quantum transformer for High Energy Physics Analysis at the LHC

• Learning quantum representations of classical high energy physics data with contrastive learning

• Quantum Particle transformer for High Energy Physics Analysis at the LHC

• Q-MAML - Quantum Model-Agnostic Meta-Learning for Variational Quantum Algorithms for High Energy Physics Analysis at the LHC

• Quantum Diffusion Model for High Energy Physics