The ambitious HL-LHC program will require enormous computing resources in the next two decades. New technologies are being sought after to replace the present computing infrastructure. A burning question is whether a quantum computer can solve the ever growing demand of 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. Developing machine learning methods will greatly enhance our ability to achieve this objective. With this project, we seek to implement Quantum Machine Learning (QML) methods for LHC HEP analysis based on some QML frameworks (PennyLane, Cirq, Bloqade, …). This will enhance the ability of the HEP community to use QML methods.
Total project length: 175/350 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.