Abstract
The High-Luminosity Large Hadron Collider (HL-LHC) starts from 2027 to extend the physics discovery potential at the energy frontier. The HL-LHC produces experimental data with a much higher luminosity, requiring a large amount of computing resources mainly due to the complexity of a track pattern recognition algorithm. Quantum annealing might be a solution for an efficient track pattern recognition in the HL-LHC environment. We demonstrated to perform the track pattern recognition by using the D-Wave annealing machine and the Fujitsu Digital Annealer. The tracking efficiency and purity for the D-Wave quantum annealer are comparable with those for a classical simulated annealing at a low pileup condition, while a drop in performance is found at a high pileup condition, corresponding to the HL-LHC pileup environment. The tracking efficiency and purity for the Fujitsu Digital Annealer are nearly the same as the classical simulated annealing.
Highlights
After the discovery of a Higgs boson, the main targets in the energy frontier experiments at Large Hadron Collider (LHC) are a precise measurement of the Standard Model processes and the discovery of new physics phenomena beyond the Standard Model
The tracking efficiency and purity for the D-Wave quantum annealer are comparable with those for a classical simulated annealing at a low pileup condition, while a drop in performance is found at a high pileup condition, corresponding to the High-Luminosity Large Hadron Collider (HL-LHC) pileup environment
We demonstrated a new method of track finding with annealing device
Summary
After the discovery of a Higgs boson, the main targets in the energy frontier experiments at Large Hadron Collider (LHC) are a precise measurement of the Standard Model processes and the discovery of new physics phenomena beyond the Standard Model. Recent novel techniques could solve the track pattern recognition task with a better performance. The other is an annealing-based quantum device, which solves problem by slowly changing the Hamiltonian and searching for the lowest energy state with a quantum fluctuation. It is not a general-purpose device, but is expected to solve the specific type of problem efficiently such as the minimization of Ising Hamiltonian. Based on the previous study [13], we demonstrate the algorithms to solve a track pattern recognition using an annealing technique with the D-Wave quantum annealer and the Fujitsu Digital Annealer. 80 2018 estimates: MC fast calo sim + standard reco MC fast calo sim + fast reco
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