Reconstructing the trajectories of charged particles as they traverse several detector layers is a key ingredient for event reconstruction at any LHC experiment. The limited bandwidth available, together with the high rate of tracks per second, makes this problem exceptionally challenging from the computational perspective. With this in mind, Quantum Computing is being explored as a new technology for future detectors, where larger datasets will further complicate this task. Furthermore, when choosing such alternative sustainability will play a crucial role and needs to be studied in detail. This project will consist in the implementation of both Quantum and Classical Machine Learning algorithms for track reconstruction, and using open-source, realistic event simulations to benchmark them from both a physics performance and an energy consumption perspective.
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