The ambitious HL-LHC program will require enormous computing resources and datasets in the next two decades. New technologies are being sought after to replace the present computing infrastructure. A burning question is whether quantum computers can solve the ever growing demand of computing resources in High Energy Physics (HEP) in general and physics at LHC in particular. Our goal here is to explore and to demonstrate that Automated Quantum Architecture Search can be used to discover novel solutions. Discovery of new physics requires the identification of rare signals against immense backgrounds, prompting the design of quantum machine learning methods. However, developing effective quantum circuits for these tasks is currently a difficult and manual process that relies on trial and error. To fully leverage the potential of Quantum Computing, we must move beyond manual design. Automated scientific discovery, specifically using machine learning to design better quantum machine learning architectures, can transform the way we design quantum circuits and optimize classifiers for HEP data. With this project we seek to implement automated methods for discovering optimal quantum machine learning architectures for LHC HEP analysis based on the Pennylane framework.
Total project length: 175 hours.
Intermediate/Advanced
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