Abstract
Accurate determination of particle track reconstruction parameters will be a major challenge for the High Luminosity Large Hadron Collider (HL-LHC) experiments. The expected increase in the number of simultaneous collisions at the HL-LHC and the resulting high detector occupancy will make track reconstruction algorithms extremely demanding in terms of time and computing resources. The increase in number of hits will increase the complexity of track reconstruction algorithms. In addition, the ambiguity in assigning hits to particle tracks will be increased due to the finite resolution of the detector and the physical “closeness” of the hits. Thus, the reconstruction of charged particle tracks will be a major challenge to the correct interpretation of the HL-LHC data. Most methods currently in use are based on Kalman filters which are shown to be robust and to provide good physics performance. However, they are expected to scale worse than quadratically. Designing an algorithm capable of reducing the combinatorial background at the hit level, would provide a much “cleaner” initial seed to the Kalman filter, strongly reducing the total processing time. One of the salient features of Quantum Computers is the ability to evaluate a very large number of states simultaneously, making them an ideal instrument for searches in a large parameter space. In fact, different R&D initiatives are exploring how Quantum Tracking Algorithms could leverage such capabilities. In this paper, we present our work on the implementation of a quantum-based track finding algorithm aimed at reducing combinatorial background during the initial seeding stage. We use the publicly available dataset designed for the kaggle TrackML challenge.
Highlights
Latest developments in Quantum Computing, significantly reduced the time needed to resolve certain problems"[1]. This resulted in a search for new methods to boost current algorithms whose computational complexity depend on the size of the dataset worse than polynomial
The simulated dataset and the challenge was created by CERN scientists to invite machine learning experts to come up with novel methods to track reconstruction
We present an exploratory look at the HepTrkX[8] project from a Quantum Computing perspective to evaluate the capabilities of Quantum Computing along with Deep Learning algorithms [3]
Summary
Latest developments in Quantum Computing, significantly reduced the time needed to resolve certain problems"[1]. This resulted in a search for new methods to boost current algorithms whose computational complexity depend on the size of the dataset worse than polynomial. Researchers are trying new methods to tackle the problem such as the use of Graph Neural Networks[3] and Quantum Computing[4,5,6]. The HepTrkX team proposed a Graph Neural Network implementation for particle track reconstruction that uses the kaggle TrackML challenge dataset[3, 7]. The simulated dataset and the challenge was created by CERN scientists to invite machine learning experts to come up with novel methods to track reconstruction. We present an exploratory look at the HepTrkX[8] project from a Quantum Computing perspective to evaluate the capabilities of Quantum Computing along with Deep Learning algorithms [3]
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