CTD2020: A Quantum Graph Network Approach to Particle Track Reconstruction

Tüysüz, Cenk; Carminati, F.; Demirköz, B.; Dobos, D.; Fracas, Fabio; Novotny, Kristiane; Potamianos, K.; Vallecorsa, S.; Vlimant, J. R.

doi:10.5281/zenodo.4088474

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Ref. [68,69] explored how GNNs can be applied to the problem of pattern recognition. They developed a hybrid quantum-classical algorithm, called a Quantum GNN (QGNN) that relies on a series of quantum edge and quantum node networks.…”

Section: Quantum Machine Learning With Quantum Circuitsmentioning

confidence: 99%

Quantum Computing Applications in Future Colliders

Gray

Terashi

2022

Front. Phys.

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High-energy physics is facing a daunting computing challenge with the large amount of data expected from the HL-LHC and other future colliders. In addition, the landscape of computation has been expanding dramatically with technologies beyond the standard x86 CPU architecture becoming increasingly available. Both of these factors necessitate an extensive and broad-ranging research and development campaign. As quantum computation has been evolving rapidly over the past few years, it is important to evaluate how quantum computation could be one potential avenue for development for future collider experiments. A wide variety of applications have been considered by different authors. We review here selected applications of quantum computing to high-energy physics, including topics in simulation, reconstruction, and the use of machine learning, and their challenges. In addition, recent advances in quantum computing technology to enhance such applications are briefly highlighted. Finally, we will discuss how such applications might transform the workflows of future collider experiments and highlight other potential applications.

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Section: Quantum Machine Learning With Quantum Circuitsmentioning

confidence: 99%

Quantum Computing Applications in Future Colliders

Gray

Terashi

2022

Front. Phys.

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“…The edge network and the node network are iterated a number of times until a final quantum edge network is applied to obtain the final segment classification. From [38]. (Online version in colour.…”

Section: Pattern Recognition With Quantum Graph Neural Networkmentioning

confidence: 99%

“…Graph neural networks are a popular technique from machine learning that are currently being explored for a wide range of high-energy physics applications including track pattern recognition [ 37 ]. Reference [ 38 ] explores how this algorithm could be extended to become a quantum graph neural network, which would run on a circuit-based quantum computer.…”

Section: Pattern Recognition With Quantum Graph Neural Networkmentioning

confidence: 99%

“…

Figure 8 The architecture of the quantum graph neural network. From [ 38 ]. (Online version in colour.)…”

Section: Pattern Recognition With Quantum Graph Neural Networkmentioning

confidence: 99%

“…

Figure 9 The validation results for a single epoch of training of a quantum graph neural network with different iteration values. From [ 38 ]. (Online version in colour.)…”

Section: Pattern Recognition With Quantum Graph Neural Networkmentioning

confidence: 99%

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Quantum pattern recognition algorithms for charged particle tracking

Gray

2021

Phil. Trans. R. Soc. A.

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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’.

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Hybrid quantum classical graph neural networks for particle track reconstruction

Tüysüz

Rieger

Novotny³

et al. 2021

Quantum Mach. Intell.

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The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC (HL-LHC). This increase in luminosity will significantly increase the number of particles interacting with the detector. The interaction of particles with a detector is referred to as “hit”. The HL-LHC will yield many more detector hits, which will pose a combinatorial challenge by using reconstruction algorithms to determine particle trajectories from those hits. This work explores the possibility of converting a novel graph neural network model, that can optimally take into account the sparse nature of the tracking detector data and their complex geometry, to a hybrid quantum-classical graph neural network that benefits from using variational quantum layers. We show that this hybrid model can perform similar to the classical approach. Also, we explore parametrized quantum circuits (PQC) with different expressibility and entangling capacities, and compare their training performance in order to quantify the expected benefits. These results can be used to build a future road map to further develop circuit-based hybrid quantum-classical graph neural networks.

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CTD2020: A Quantum Graph Network Approach to Particle Track Reconstruction

Cited by 5 publications

References 4 publications

Quantum Computing Applications in Future Colliders

Quantum Computing Applications in Future Colliders

Quantum pattern recognition algorithms for charged particle tracking

Hybrid quantum classical graph neural networks for particle track reconstruction

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