Application of quantum computing techniques in particle tracking at LHC

After the next planned upgrades to the LHC, the luminosity it delivers will more than double, substantially increasing the already large demand on computing resources. Therefore an efficient way to reconstruct physical objects is required. Recent studies show that one of the quantum computing techniques, quantum annealing (QA), can be used to perform particle tracking with efficiency higher than 90% in the high pileup region in the high luminosity environment. The algorithm starts by determining the connection between the hits, and classifies 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. Tracking performance with a different setup of the original algorithm is also studied with data collected by the ATLAS experiment

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