Abstract
We explore the use of Quantum Machine Learning (QML) for anomaly detection at the Large Hadron Collider (LHC). In particular, we explore a semi-supervised approach in the four-lepton final state where simulations are reliable enough for a direct background prediction. This is a representative task where classification needs to be performed using small training datasets — a regime that has been suggested for a quantum advantage. We find that Classical Machine Learning (CML) benchmarks outperform standard QML algorithms and are able to automatically identify the presence of anomalous events injected into otherwise background-only datasets.
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References
S.P. Jordan, K.S.M. Lee and J. Preskill, Quantum Algorithms for Quantum Field Theories, Science 336 (2012) 1130 [arXiv:1111.3633] [INSPIRE].
C.W. Bauer et al., Quantum Simulation for High Energy Physics, UMD-PP-022-04 (2022) [arXiv:2204.03381] [INSPIRE].
A. Mott, J. Job, J.R. Vlimant, D. Lidar and M. Spiropulu, Solving a Higgs optimization problem with quantum annealing for machine learning, Nature 550 (2017) 375 [INSPIRE].
A. Zlokapa, A. Mott, J. Job, J.-R. Vlimant, D. Lidar and M. Spiropulu, Quantum adiabatic machine learning by zooming into a region of the energy surface, Phys. Rev. A 102 (2020) 062405 [arXiv:1908.04480] [INSPIRE].
A. Blance and M. Spannowsky, Quantum Machine Learning for Particle Physics using a Variational Quantum Classifier, JHEP 02 (2021) 212 [arXiv:2010.07335] [INSPIRE].
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 (2021) 2 [arXiv:2002.09935] [INSPIRE].
S.Y.-C. Chen, T.-C. Wei, C. Zhang, H. Yu and S. Yoo, Quantum convolutional neural networks for high energy physics data analysis, Phys. Rev. Res. 4 (2022) 013231 [arXiv:2012.12177] [INSPIRE].
S.L. Wu et al., Application of quantum machine learning using the quantum variational classifier method to high energy physics analysis at the LHC on IBM quantum computer simulator and hardware with 10 qubits, J. Phys. G 48 (2021) 125003 [arXiv:2012.11560] [INSPIRE].
S.Y.-C. Chen, T.-C. Wei, C. Zhang, H. Yu and S. Yoo, Hybrid Quantum-Classical Graph Convolutional Network, arXiv:2101.06189 [INSPIRE].
J. Heredge, C. Hill, L. Hollenberg and M. Sevior, Quantum Support Vector Machines for Continuum Suppression in B Meson Decays, Comput. Softw. Big Sci. 5 (2021) 27 [arXiv:2103.12257] [INSPIRE].
S.L. Wu et al., Application of quantum machine learning using the quantum kernel algorithm on high energy physics analysis at the LHC, Phys. Rev. Res. 3 (2021) 033221 [arXiv:2104.05059] [INSPIRE].
V. Belis et al., Higgs analysis with quantum classifiers, EPJ Web Conf. 251 (2021) 03070 [arXiv:2104.07692] [INSPIRE].
J.Y. Araz and M. Spannowsky, Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States, JHEP 08 (2021) 112 [arXiv:2106.08334] [INSPIRE].
C. Bravo-Prieto, J. Baglio, M. Cè, A. Francis, D.M. Grabowska and S. Carrazza, Style-based quantum generative adversarial networks for Monte Carlo events, Quantum 6 (2022) 777 [arXiv:2110.06933] [INSPIRE].
A. Blance and M. Spannowsky, Unsupervised event classification with graphs on classical and photonic quantum computers, JHEP 21 (2020) 170 [arXiv:2103.03897] [INSPIRE].
V.S. Ngairangbam, M. Spannowsky and M. Takeuchi, Anomaly detection in high-energy physics using a quantum autoencoder, Phys. Rev. D 105 (2022) 095004 [arXiv:2112.04958] [INSPIRE].
J.Y. Araz and M. Spannowsky, Classical versus quantum: Comparing tensor-network-based quantum circuits on Large Hadron Collider data, Phys. Rev. A 106 (2022) 062423 [arXiv:2202.10471] [INSPIRE].
A. Gianelle et al., Quantum Machine Learning for b-jet charge identification, JHEP 08 (2022) 014 [arXiv:2202.13943] [INSPIRE].
T.S. Humble et al., Snowmass White Paper: Quantum Computing Systems and Software for High-energy Physics Research, in 2022 Snowmass Summer Study, Seattle U.S.A., July 17–26 2022 [arXiv:2203.07091] [INSPIRE].
W. Guan et al., Quantum Machine Learning in High Energy Physics, Mach. Learn. Sci. Tech. 2 (2021) 011003 [arXiv:2005.08582] [INSPIRE].
M. Schuld and N. Killoran, Is Quantum Advantage the Right Goal for Quantum Machine Learning?, PRX Quantum 3 (2022) 030101 [arXiv:2203.01340] [INSPIRE].
G. Karagiorgi, G. Kasieczka, S. Kravitz, B. Nachman and D. Shih, Machine Learning in the Search for New Fundamental Physics, arXiv:2112.03769 [INSPIRE].
G. Kasieczka et al., The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics, Rept. Prog. Phys. 84 (2021) 124201 [arXiv:2101.08320] [INSPIRE].
T. Aarrestad et al., The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider, SciPost Phys. 12 (2022) 043 [arXiv:2105.14027] [INSPIRE].
ATLAS collaboration, Search for heavy resonances decaying into a pair of Z bosons in the ℓ+ℓ−ℓ′+ℓ′− and \( {\ell}^{+}{\ell}^{-}\nu \overline{\nu} \) final states using 139 fb−1 of proton-proton collisions at \( \sqrt{s} \) = 13 TeV with the ATLAS detector, Eur. Phys. J. C 81 (2021) 332 [arXiv:2009.14791] [INSPIRE].
ATLAS collaboration, Search for Higgs boson decays to beyond-the-Standard-Model light bosons in four-lepton events with the ATLAS detector at \( \sqrt{s} \) = 13 TeV, JHEP 06 (2018) 166 [arXiv:1802.03388] [INSPIRE].
ATLAS collaboration, Measurements of the Higgs boson inclusive and differential fiducial cross sections in the 4ℓ decay channel at \( \sqrt{s} \) = 13 TeV, Eur. Phys. J. C 80 (2020) 942 [arXiv:2004.03969] [INSPIRE].
CMS collaboration, Measurements of the Higgs boson width and anomalous HVV couplings from on-shell and off-shell production in the four-lepton final state, Phys. Rev. D 99 (2019) 112003 [arXiv:1901.00174] [INSPIRE].
CMS collaboration, Search for low-mass dilepton resonances in Higgs boson decays to four-lepton final states in proton-proton collisions at \( \sqrt{s} \) = 13 TeV, Eur. Phys. J. C 82 (2022) 290 [arXiv:2111.01299] [INSPIRE].
CMS collaboration, Constraints on anomalous Higgs boson couplings to vector bosons and fermions in its production and decay using the four-lepton final state, Phys. Rev. D 104 (2021) 052004 [arXiv:2104.12152] [INSPIRE].
K. Krzyżańska and B. Nachman, Simulation-based anomaly detection for multileptons at the LHC, JHEP 01 (2023) 061 [arXiv:2203.09601] [INSPIRE].
J.H. Collins, P. Martín-Ramiro, B. Nachman and D. Shih, Comparing weak- and unsupervised methods for resonant anomaly detection, Eur. Phys. J. C 81 (2021) 617 [arXiv:2104.02092] [INSPIRE].
K. Fraser, S. Homiller, R.K. Mishra, B. Ostdiek and M.D. Schwartz, Challenges for unsupervised anomaly detection in particle physics, JHEP 03 (2022) 066 [arXiv:2110.06948] [INSPIRE].
R.T. d’Agnolo, G. Grosso, M. Pierini, A. Wulzer and M. Zanetti, Learning new physics from an imperfect machine, Eur. Phys. J. C 82 (2022) 275 [arXiv:2111.13633] [INSPIRE].
P. Rebentrost, M. Mohseni and S. Lloyd, Quantum support vector machine for big data classification, Physical Review Letters 113 (2014) .
A.W. Harrow, A. Hassidim and S. Lloyd, Quantum algorithm for linear systems of equations, Physical Review Letters 103 (2009).
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe and S. Lloyd, Quantum machine learning, Nature 549 (2017) 195.
P.-A. McRae and M. Hilke, Quantum-Enhanced Machine Learning for Covid-19 and Anderson Insulator Predictions, [arXiv:2012.03472].
V. Bergholm et al., PennyLane: Automatic differentiation of hybrid quantum-classical computations, arXiv:1811.04968 [INSPIRE].
J. Alwall et al., The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations, JHEP 07 (2014) 079 [arXiv:1405.0301] [INSPIRE].
Higgs effective couplings to gluons (and photons), (2013) [https://cp3.irmp.ucl.ac.be/projects/madgraph/wiki/Models/HiggsEffective].
T. Sjöstrand, S. Mrenna and P.Z. Skands, PYTHIA 6.4 Physics and Manual, JHEP 05 (2006) 026 [hep-ph/0603175] [INSPIRE].
T. Sjöstrand, S. Mrenna and P.Z. Skands, A Brief Introduction to PYTHIA 8.1, Comput. Phys. Commun. 178 (2008) 852 [arXiv:0710.3820] [INSPIRE].
T. Sjöstrand et al., An introduction to PYTHIA 8.2, Comput. Phys. Commun. 191 (2015) 159 [arXiv:1410.3012] [INSPIRE].
Particle Data Group collaboration, Review of Particle Physics, PTEP 2020 (2020) 083C01 [INSPIRE].
J. Alwall et al., A Standard format for Les Houches event files, Comput. Phys. Commun. 176 (2007) 300 [hep-ph/0609017] [INSPIRE].
DELPHES 3 collaboration, DELPHES 3, A modular framework for fast simulation of a generic collider experiment, JHEP 02 (2014) 057 [arXiv:1307.6346] [INSPIRE].
A. Mertens, New features in Delphes 3, J. Phys. Conf. Ser. 608 (2015) 012045 [INSPIRE].
M. Selvaggi, DELPHES 3: A modular framework for fast-simulation of generic collider experiments, J. Phys. Conf. Ser. 523 (2014) 012033 [INSPIRE].
T. Robens, T. Stefaniak and J. Wittbrodt, Two-real-scalar-singlet extension of the SM: LHC phenomenology and benchmark scenarios, Eur. Phys. J. C 80 (2020) 151 [arXiv:1908.08554] [INSPIRE].
M.J.D. Powell, A direct search optimization method that models the objective and constraint functions by linear interpolation, in Advances in Optimization and Numerical Analysis, S. Gomez and J.-P. Hennart eds., Springer Netherlands, Dordrecht (1994), pp. 51–67 [DOI].
M. Abadi et al., TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, [arXiv:1603.04467].
D.P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, arXiv:1412.6980 [INSPIRE].
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Alvi, S., Bauer, C.W. & Nachman, B. Quantum anomaly detection for collider physics. J. High Energ. Phys. 2023, 220 (2023). https://doi.org/10.1007/JHEP02(2023)220
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DOI: https://doi.org/10.1007/JHEP02(2023)220