A Quantum Graph Neural Network Approach to Particle Track Reconstruction

@article{Tuysuz2020AQG,
  title={A Quantum Graph Neural Network Approach to Particle Track Reconstruction},
  author={Cenk Tuysuz and Federico Carminati and Bilge Demirkoz and Daniel Dobos and Fabio Fracas and Kristiane Novotny and Karolos Potamianos and Sofia Vallecorsa and J. R. Vlimant},
  journal={arXiv: Quantum Physics},
  year={2020}
}
Unprecedented increase of complexity and scale of data is expected in computation necessary for the tracking detectors of the High Luminosity Large Hadron Collider (HL-LHC) experiments. While currently used Kalman filter based algorithms are reaching their limits in terms of ambiguities from increasing number of simultaneous collisions, occupancy, and scalability (worse than quadratic), a variety of machine learning approaches to particle track reconstruction are explored. It has been… Expand

Figures and Tables from this paper

Hybrid Quantum-Classical Graph Convolutional Network
TLDR
This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data that demonstrates an advantage over classical multilayer perceptron and convolutionAL neural networks in the aspect of number of parameters. Expand
Investigating Quantum Speedup for Track Reconstruction: Classical and Quantum Computational Complexity Analysis
Physics of Information and Quantum Technologies Group, Instituto de Telecomunicações, Portugal Instituto Superior Técnico, Universidade de Lisboa, Portugal Department of Mathematics, ClarksonExpand
Quantum speedup for track reconstruction in particle accelerators
D. Magano, 2 A. Kumar, 3 M. Kālis, A. Locāns, A. Glos, S. Pratapsi, 2 G. Quinta, M. Dimitrijevs, A. Rivošs, P. Bargassa, 6 J. Seixas, 7 A. Ambainis, and Y. Omar 2 Physics of Information and QuantumExpand
Performance of Particle Tracking Using a Quantum Graph Neural Network
TLDR
This work explores the possibility of converting a novel Graph Neural Network model, that proven itself for the track reconstruction task, to a Hybrid graph Neural Network in order to benefit the exponentially growing Hilbert Space. Expand
Quantum annealing algorithms for track pattern recognition
TLDR
This work demonstrated to perform the track pattern recognition by using the D-Wave annealing machine and the Fujitsu Digital Annealer, finding a drop in performance is found at a high pileup condition, corresponding to the HL-LHC pileup environment. Expand

References

SHOWING 1-10 OF 12 REFERENCES
Charged particle tracking with quantum annealing-inspired optimization
TLDR
It is found that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the LHC while leaving open the possibility of a quantum speedup for tracking. Expand
Novel deep learning methods for track reconstruction
For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems. A variety of models were studied that drew inspiration fromExpand
The Tracking Machine Learning Challenge: Accuracy Phase
This paper reports the results of an experiment in high energy physics: using the power of the “crowd” to solve difficult experimental problems linked to tracking accurately the trajectory ofExpand
Hierarchical quantum classifiers
TLDR
It is shown how quantum algorithms based on two tensor network structures can be used to classify both classical and quantum data and may enable classification of two-dimensional images and entangled quantum data more efficiently than is possible with classical methods. Expand
Matrix Product State–Based Quantum Classifier
TLDR
This letter shows that MPS, as a one-dimensional array of tensors, can be used to classify classical and quantum data, and investigates its performance by considering different parameters on the ibmqx4 quantum computer and proves that M PS circuits can beused to attain better accuracy. Expand
PennyLane: Automatic differentiation of hybrid quantum-classical computations
TLDR
PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation, and it extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. Expand
Chapter 1: High Luminosity Large Hadron Collider HL-LHC
The Large Hadron Collider (LHC) is one of the largest scientific instruments ever built. Since opening up a newenergy frontier for exploration in 2010, it has gathered a global user community ofExpand
Quantum machine learning in high energy physics
TLDR
The first generation of ideas that use quantum machine learning on problems in HEP are reviewed and an outlook on future applications is provided. Expand
Particle Track Reconstruction with Quantum Algorithms
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 ofExpand
High-Luminosity Large Hadron Collider (HL-LHC) : Preliminary Design Report
TLDR
The present document describes the technologies and components that will be used to realise the High Luminosity LHC and is intended to serve as the basis for the detailed engineering design of HL-LHC. Expand
...
1
2
...