Reconstructing charged particle track segments with a quantum-enhanced support vector machine

I. Shapoval and P. Calafiura, Quantum Associative Memory in HEP Track Pattern Recognition, EPJ Web Conf. 214

Original

1902

Earlier work this paper cites.

S. Amrouche et al. , The Tracking Machine Learning challenge : Accuracy phase, in The NeurIPS ’18 Competition: From Machine Learning to Intelligent Conversations (2019) arXiv:1904.06778 [hep-ex]

Original

1904

Earlier work this paper cites.

A. Y. Wei, P. Naik, A. W. Harrow, and J. Thaler, Quantum Algorithms for Jet Clustering, Phys. Rev. D 101

Original

1908

Earlier work this paper cites.

A. Zlokapa, A. Anand, J.-R. Vlimant, J. M. Duarte, J. Job, D. Lidar, and M. Spiropulu, Charged particle tracking with quantum annealing-inspired optimization, Quantum Machine Intelligence 3

Original

1908

Earlier work this paper cites.

M. T. Musavi, W. Ahmed, K. H. Chan, K. B. Faris, and D. M. Hummels, On the training of radial basis function classifiers, Neural Networks 5

1992

Earlier work this paper cites.

R. K. Sundaram, A First Course in Optimization Theory (Cambridge University Press, 1996)

1996

Earlier work this paper cites.

N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods (Cambridge University Press, 2000)

2000

Earlier work this paper cites.

K. Terashi, M. Kaneda, T. Kishimoto, M. Saito, R. Sawada, and J. Tanaka, Event Classification with Quantum Machine Learning in High-Energy Physics, Comput. Softw. Big Sci. 5

Original

2002

Earlier work this paper cites.

C. Tüysüz, F. Carminati, B. Demirköz, D. Dobos, F. Fracas, K. Novotny, K. Potamianos, S. Vallecorsa, and J.-R. Vlimant, Particle Track Reconstruction with Quantum Algorithms, EPJ Web Conf. 245

Original

2003

Earlier work this paper cites.

C. Tüysüz, F. Carminati, B. Demirköz, D. Dobos, F. Fracas, K. Novotny, K. Potamianos, S. Vallecorsa, and J.-R. Vlimant, A Quantum Graph Neural Network Approach to Particle Track Reconstruction, in Connecting The Dots (2020) arXiv:2007.06868 [quant-ph]

Original

2007

Earlier work this paper cites.

L. Bottou, O. Chapelle, D. DeCoste, and J. Weston, Large-Scale Kernel Machines (Neural Information Processing) (The MIT Press, 2007)

2007

Earlier work this paper cites.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research 12

2011

Earlier work this paper cites.

D. Contardo, M. Klute, J. Mans, L. Silvestris, and J. Butler, Technical Proposal for the Phase-II Upgrade of the CMS Detector 10.17181/CERN.VU8I.D59J (2015)

2015

Cited alongside, same era.

S. Farrell et al. , Novel deep learning methods for track reconstruction, in 4th International Workshop Connecting The Dots 2018 (2018) arXiv:1810.06111 [hep-ex]

Original

2018

Cited alongside, same era.

S. Aaronson and A. Ambainis, Forrelation: A problem that optimally separates quantum from classical computing, SIAM Journal on Computing 47

2018

Cited alongside, same era.

ATLAS and CMS Collaborations, Report on the physics at the HL-LHC and perspectives for the HE-LHC (2019)

2019

Cited alongside, same era.

V. Havlíček, A. D. Córcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, and J. M. Gambetta, Supervised learning with quantum-enhanced feature spaces, Nature 567

M. Schuld and F. Petruccione, Quantum models as kernel methods, in Machine Learning with Quantum Computers (Springer International Publishing, Cham, 2021) pp. 217–245

2021

Later among the works it cites.

S. Bravyi, D. Gosset, D. Grier, and L. Schaeffer, Classical algorithms for Forrelation, (2021), arXiv:2102.06963 [quant-ph]

Original

2021

Later among the works it cites.

T. Haug, C. N. Self, and M. S. Kim, Quantum machine learning of large datasets using randomized measurements, (2021), arXiv:2108.01039 [quant-ph]

Original

2021

Later among the works it cites.

ATLAS Collaboration, Technical Design Report for the Phase-II Upgrade of the ATLAS Trigger and Data Acquisition System - Event Filter Tracking Amendment 10.17181/CERN.ZK85.5TDL (2022)

2022

Closest in time.