Our work focuses on Quantum Graph Neural Networks (QGNNs), to solve the particle tracking reconstruction challenge. Specifically, we are looking to focus on the detailed analysis of the vanishing gradient problem, long training times, and how robust the overall approach is to noise from real quantum computers, which have been mentioned but not addressed yet in prior work. Our work aims to improve the viability of the QGNN method for particle tracking problems.
Team Name:
The Superpositioned States of America
Project Description:
Our work focuses on Quantum Graph Neural Networks (QGNNs), to solve the particle tracking reconstruction challenge. Specifically, we are looking to focus on the detailed analysis of the vanishing gradient problem, long training times, and how robust the overall approach is to noise from real quantum computers, which have been mentioned but not addressed yet in prior work. Our work aims to improve the viability of the QGNN method for particle tracking problems.
Presentation:
CERN_Project_Report.pdf
Source code:
https://github.com/amirebrahimi/QHack-2022-Hackathon https://github.com/amirebrahimi/qtrkx-gnn-tracking/
Which challenges/prizes would you like to submit your project for?
CERN AQT Google Quantum Simulation Challenge Young Scientist (we have an undergrad from IQT)