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

Description

The ambitious HL-LHC program will require enormous computing resources in the next two decades. New technologies are being sought to replace the present computing infrastructure. A burning question is whether quantum computers can solve the ever-growing demand for computing resources in High-Energy Physics (HEP) in general and physics at LHC in particular. Discovery of new physics requires the identification of rare signals against immense backgrounds. The development of machine learning methods will greatly enhance our ability to achieve this objective. With this project we seek to implement Quantum Machine Learning methods for LHC HEP analysis based on the Pennylane framework. This will enhance the ability of the HEP community to use Quantum Machine Learning methods.

Duration

Total project length: 175/350 hours.

Task ideas

Implement a Quantum Graph Neural Network (QGNN) method based on a suitable framework e.g. Pennylane.
Apply the quantum machine learning method to a benchmark high-energy physics analysis and benchmark the quantum machine learning performance compared to classical machine learning methods

Expected results

Trained Quantum Graph Neural Network with e.g. Pennylane framework.
Apply the Quantum Machine Learning method to LHC physics analysis and compare to classical machine learning methods.

Test

Please use this link to access the test for this project.

Requirements

Solid knowledge of machine learning and deep learning
Knowledge of quantum mechanics desired
Strong python skills
Ability to work independently and proactive on a research project

Difficulty Level

Intermediate/Advanced

Mentors

Rui Zhang (University of Wisconsin-Madison)
Alkaid Cheng (University of Wisconsin-Madison)
Sergei Gleyzer (University of Alabama)
KC Kong (University of Kansas)
Roy Forestano (University of Florida)

Links

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.

Corresponding Project

QMLHEP