# Geometric Quantum Machine Learning with Neutral Atoms (Pasqal)

- Institution: CERN
- Speaker: Jogi Suda Neto
- Year: 2026
- Language: English
- Subjects: Physics, Computer science
- Source: [CERN](https://videos.cern.ch/record/3023666)
- Licence: [CERN](https://copyright.web.cern)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

This collaboration focus on neutral atom quantum computing techniques. The research collaboration aims to 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.

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.

## Lectures

1. [Geometric Quantum Machine Learning with Neutral Atoms (Pasqal)](https://invisiblenetwork.ai/learn/cern/3023666/504a6901efcf), 18 min (CERN's player)