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EPJ Web of Conferences

Quantum annealing algorithms for track pattern recognition

M. Saito, P. Calafiura, Heather M. Gray, Wim Lavrijsen, Lucy Linder, Y. Okumura, R. Sawada, Alex G. Smith, Jun'ichi Tanaka, Koji Terashi

Published Jan 1, 2020• 9 citations
Publisher

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.

DOI
10.1051/epjconf/202024510006

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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.
Abstract The reconstruction of charged particles will be a key computing challenge for the high-luminosity Large Hadron Collider (HL-LHC) where increased data rates lead to a large increase in running time for current pattern recognition algorithms. An alternative approach explored here expresses pattern recognition as a quadratic unconstrained binary optimization (QUBO), which allows algorithms to be run on classical and quantum annealers. While the overall timing of the proposed approach and its scaling has still to be measured and studied, we demonstrate that, in terms of efficiency and purity, the same physics performance of the LHC tracking algorithms can be achieved. More research will be needed to achieve comparable performance in HL-LHC conditions, as increasing track density decreases the purity of the QUBO track segment classifier.
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.
Abstract At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for physics analysis will need to be upgraded to scale with track density. Quantum annealing has shown promise in its ability to solve combinatorial optimization problems amidst an ongoing effort to establish evidence of a quantum speedup. As a step towards exploiting such potential speedup, we investigate a track reconstruction approach by adapting the existing geometric Denby-Peterson (Hopfield) network method to the quantum annealing framework for HL-LHC conditions. We develop additional techniques to embed the problem onto existing and near-term quantum annealing hardware. Results using simulated annealing and quantum annealing with the D-Wave 2X system on the TrackML open dataset are presented, demonstrating the successful application of a quantum annealing algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the HL-LHC while leaving open the possibility of a quantum speedup for tracking.
Abstract Charged particle reconstruction or track reconstruction is one of the most crucial components of pattern recognition in high-energy collider physics. It is known to entail enormous consumption of computing resources, especially when the particle multiplicity is high, which will be the conditions at future colliders, such as the High Luminosity Large Hadron Collider and Super Proton–Proton Collider. Track reconstruction can be formulated as a quadratic unconstrained binary optimization (QUBO) problem, for which various quantum algorithms have been investigated and evaluated with both a quantum simulator and hardware. Simulated bifurcation algorithms are a set of quantum-annealing-inspired algorithms, known to be serious competitors to other Ising machines. In this study, we show that simulated bifurcation algorithms can be employed to solve the particle tracking problem. The simulated bifurcation algorithms run on classical computers and are suitable for parallel processing and usage of graphical processing units, and they can handle significantly large amounts of data at high speed. These algorithms exhibit reconstruction efficiency and purity comparable to or sometimes improved over those of simulated annealing, but the running time can be reduced by as much as four orders of magnitude. These results suggest that QUBO models together with quantum-annealing-inspired algorithms are valuable for current and future particle tracking problems.
High-energy physics is facing a daunting computing challenge with the large datasets expected from the upcoming High-Luminosity Large Hadron Collider in the next decade and even more so at future colliders. A key challenge in the reconstruction of events of simulated data and collision data is the pattern recognition algorithms used to determine the trajectories of charged particles. The field of quantum computing shows promise for transformative capabilities and is going through a cycle of rapid development and hence might provide a solution to this challenge. This article reviews current studies of quantum computers for charged particle pattern recognition in high-energy physics. This article is part of the theme issue 'Quantum technologies in particle physics'.
Tracking charged particles in high-energy physics experiments is a computationally intensive task. With the advent of the High Luminosity LHC era, which is expected to significantly increase the number of proton-proton interactions per beam collision, the amount of data to be analysed will increase significantly. As a consequence, local pattern recognition algorithms suffer from scaling problems. In this work, we investigate the possibility of using machine learning techniques in combination with quantum computing. In particular, we represent particle trajectories as a graph data structure and train a quantum graph neural network to perform global pattern recognition. We show recent results on the application of this method, with scalability tests for increasing pileup values. We also provide insights into various aspects of code development in different quantum programming frameworks such as Pennylane and IBM Qiskit. Finally, we discuss the critical points and give an outlook of potential improvements and alternative approaches.