Application of quantum computing techniques in particle tracking at LHC

In the near future, the LHC detector will deliver higher luminosity, causing the demand on large amount of computing resources. Therefore an efficient way to reconstruct physical objects are required. Recent studies showed that one of the quantum computing techniques, quantum annealing (QA), can be used to perform the particle tracking with efficiency higher than 90% in the high pileup region in the high luminosity environment. The algorithm starts from determining the connection between the hits, and classify the topological objects with their pattern. The current study aims to improve the pre-processing efficiency in the QA-based tracking algorithm by implementing a graph neural network (GNN), which is expected to efficiently generate the topological object needed for the annealing process. Moreover, the tracking performances with data collected from ATLAS experiment are also included.