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 quantum computer 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 Quantum Computing can be the new paradigm (Proof of Principle).
Discovery of new physics requires the identification of rare signals against immense backgrounds. Development of machine learning methods will greatly enhance our ability to achieve this objective. However, with this ever-growing volume of data in the near future, current machine learning algorithms will require large computing resources and excessive computing time to achieve good performance. Quantum Computing in Qubit platform, where qubits are used instead of bits in classical computer, has the potential to improve the time complexity of classical algorithms.
With this project we seek to implement Quantum Machine Learning methods for LHC HEP analysis based on e.g. the Pennylane framework. This will enhance the ability of the HEP community to use Quantum Machine Learning methods.
Total project length: 175/350 hours.
Please use this link to access the test for this project. The test is due by April 3rd, however please keep in mind that it takes about 1 week to craft a good proposal and proposals need to be submitted via GSoC portal by April 4
Please DO NOT contact mentors directly by email. Instead, please email firstname.lastname@example.org with Project Title and include your CV and test results. The mentors will then get in touch with you.