In the near future, the LHC detector will deliver much more data to be processed. Therefore, new techniques are required to deal with such a large amount of data. 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% even in the dense environment. The algorithm starts from determining the connection between the hits, and classifies the objects with their pattern as doublet (pair of hits), triplet (three hits in a roll) or quadruplet (four hits in a roll). In order to perform the QA process, all these objects have to be constructed into a Quadratic Unconstrained Binary Optimization (QUBO) format. The current study aims to reduce the computational cost in the QA-based tracking algorithm by implementing a graph neural network (GNN) in the pre-processing stage to select input object for QUBO, and by optimizing the tightness of the selection. Moreover, the tracking performances between the standard QA-based tracking algorithm and the GNN-QA tracking algorithm are also compared.
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