Geometric Quantum Machine Learning with Neutral Atoms
In collaboration with
The collaboration will focus on neutral atom quantum computing techniques. The research collaboration will contribute to advance knowledge and tools that will be strategic to both Parties. The substantial innovation in the field of AI in the past years together with the rapid prototyping of quantum technologies, has enabled the definition of quantum machine learning. It is an active field of research which seeks to take advantage of the capabilities of both quantum computers and machine learning techniques, adapting the latter to the strengths of the former.
Overview
Starting from a deep understanding of current classic and quantum implementation of theoretical and computational models for graph neural network, we will focus on scalability, symmetry properties and generalization. In particular, geometric deep learning (GDL) will be a key focus of this work package. The possibility of testing and implementing those model for HEP use cases represent an important test bed for future LHC computing requirements and quantum technologies.
Highlights in 2025
We were able to finalize the hiring of a PhD student and started the literature review part and setup of next goals.
Next Steps
The collaboration between Pasqal and the team at CERN QTI will transform the preliminary theoretical result into customized algorithms for High Energy Physics use case, taking advantage of the quantum platform developed by Pasqal.
Project Coordinator
Michele Grossi
Technical Team
Jogi Suda Neto, Michele Grossi, Cenk Tüysuz, Andrea Gentile
Collaboration Liaisons
Jogi Suda Neto, Michele Grossi, Cenk Tüysuz, Andrea Gentile