The rapid data growth at the Large Hadron Collider (LHC) presents an unprecedented challenge for computational resources. As the High Luminosity LHC (HL-LHC) era approaches, existing classical computing infrastructures will struggle to keep up with the increasing complexity of data analysis. Machine learning techniques have already demonstrated their potential in identifying rare physics signals within massive datasets, but the computational cost of model training and optimization limits their efficiency.
Quantum Computing offers a new paradigm for tackling these challenges by leveraging Variational Quantum Algorithms (VQAs). However, training these quantum models effectively remains challenging due to barren plateaus and inefficient parameter optimization, leading to slow convergence.
This project explores the potential of AI for Quantum Computing to improve the efficiency of quantum machine learning in High Energy Physics (HEP). By optimizing variational quantum circuits with a classical meta-learning model, we aim to accelerate convergence and reduce the computational burden of quantum optimization. This approach will be tested on real or simulated LHC data, demonstrating the feasibility of quantum-enhanced data analysis for HEP.
Total project length: 175 hours.
Please use this link to access the test for this project.
Please DO NOT contact mentors directly by email. Instead, please email ml4-sci@cern.ch with Project Title and include your CV and test results. The mentors will then get in touch with you.