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Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware
/ Wu, Sau Lan (Wisconsin U., Madison) ; Chan, Jay (Wisconsin U., Madison) ; Cheng, Alkaid (Wisconsin U., Madison) ; Guan, Wen (Wisconsin U., Madison) ; Sun, Shaojun (Wisconsin U., Madison) ; Wang, Alex (Wisconsin U., Madison) ; Zhang, Rui (Wisconsin U., Madison) ; Zhou, Chen (Wisconsin U., Madison) ; Livny, Miron (U. Wisconsin, Madison (main)) ; Di Meglio, Alberto (CERN) et al.
Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. [...]
FERMILAB-CONF-22-331-DI-QIS.-
2022 - 8 p.
- Published in : PoS EPS-HEP2021 (2022) 842
Fulltext: 9e81af30dcb178c482ac8c56f379040d - PDF; document - PDF; External link: Fermilab Library Server
In : European Physics Society conference on High Energy Physics 2021, Online, Online, 26 - 30 Jul 2021, pp.842
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2.
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Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware
/ Chan, Jay (Wisconsin U., Madison) ; Guan, Wen (Wisconsin U., Madison) ; Sun, Shaojun (Wisconsin U., Madison) ; Wang, Alex (Wisconsin U., Madison) ; Wu, Sau Lan (Wisconsin U., Madison) ; Zhou, Chen (Wisconsin U., Madison) ; Livny, Miron (U. Wisconsin, Madison (main)) ; Carminati, Federico (CERN) ; Meglio, Alberto Di (CERN) ; Li, Andy C Y (Fermilab) et al.
One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. [...]
SISSA, 2021 - 6 p.
- Published in : PoS ICHEP2020 (2021) 930
Fulltext: PDF;
In : 40th International Conference on High Energy Physics (ICHEP), Prague, Czech Republic, 28 Jul - 6 Aug 2020, pp.930
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3.
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Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware
/ Chan, Jay (Wisconsin U., Madison) ; Guan, Wen (Wisconsin U., Madison) ; Sun, Shaojun (Wisconsin U., Madison) ; Wang, Alex (Wisconsin U., Madison) ; Wu, Sau Lan (Wisconsin U., Madison) ; Zhou, Chen (Wisconsin U., Madison) ; Livny, Miron (Wisconsin U., Madison) ; Carminati, Federico (CERN) ; Di Meglio, Alberto (CERN)
The ambitious HL-LHC program will require enormous computing resources in the next two decades. A burning question is whether quantum computer can solve the ever growing demand of computing resources in High Energy Physics in general and physics at LHC in particular.Using IBM Quantum Computer Simulators and Quantum Computer Hardware, we have successfully employed the Quantum Support Vector Machine Method (QSVM) in applying quantum machine learning for a ttH (H to two photons), Higgs coupling to top quarks analysis at LHC..
SISSA, 2020 - 7 p.
- Published in : PoS EPS-HEP2019 (2020) 116
Fulltext from publisher.: PDF;
In : European Physical Society Conference on High Energy Physics (EPS-HEP) 2019, Ghent, Belgium, 10 - 17 Jul 2019, pp.116
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4.
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Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
/ Wu, Sau Lan (Wisconsin U., Madison) ; Sun, Shaojun (Wisconsin U., Madison) ; Guan, Wen (Wisconsin U., Madison) ; Zhou, Chen (Wisconsin U., Madison) ; Chan, Jay (Wisconsin U., Madison) ; Cheng, Chi Lung (Wisconsin U., Madison) ; Pham, Tuan (Wisconsin U., Madison) ; Qian, Yan (Wisconsin U., Madison) ; Wang, Alex Zeng (Wisconsin U., Madison) ; Zhang, Rui (Wisconsin U., Madison) et al.
Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in High Energy Physics by offering computational speed-ups. In this study, we employ a support vector machine with a quantum kernel estimator (QSVM-Kernel method) to a recent LHC flagship physics analysis: tˉtHt¯tH (Higgs boson production in association with a top quark pair). [...]
arXiv:2104.05059; FERMILAB-PUB-21-552-DI-QIS.-
2021-09-08 - 9 p.
- Published in : Phys. Rev. Res. 3 (2021) 033221
Fulltext: PDF; Fulltext from Publisher: PDF; Fulltext from publisher: PDF; External link: Fermilab Library Server
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5.
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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
/ Wu, Sau Lan (Wisconsin U., Madison) ; Chan, Jay (Wisconsin U., Madison) ; Guan, Wen (Wisconsin U., Madison) ; Sun, Shaojun (Wisconsin U., Madison) ; Wang, Alex (Wisconsin U., Madison) ; Zhou, Chen (Wisconsin U., Madison) ; Livny, Miron (Wisconsin U., Madison) ; Carminati, Federico (CERN) ; Di Meglio, Alberto (CERN) ; Li, Andy C.Y. (Fermilab) et al.
One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. [...]
arXiv:2012.11560; FERMILAB-PUB-20-675-DI-QIS.-
2021-10-26 - 12 p.
- Published in : J. Phys. G 48 (2021) 125003
Fulltext: 2012.11560 - PDF; fermilab-pub-20-675-di-qis - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server
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6.
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Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware
/ Chan, Jay (Wisconsin U., Madison) ; Guan, Wen (Wisconsin U., Madison) ; Sun, Shaojun (Wisconsin U., Madison) ; Wang, Alex Zeng (Wisconsin U., Madison) ; Wu, Sau Lan (Wisconsin U., Madison) ; Zhou, Chen (Wisconsin U., Madison) ; Livny, Miron (Prism Comp. Sci., Madison) ; Carminati, Federico (CERN) ; Meglio, Alberto Di (CERN)
The ambitious HL-LHC program will require enormous computing resources in the next two decades. A burning question is whether quantum computer can solve the ever growing demand of computing resources in High Energy Physics in general and physics at LHC in particular.Using IBM Quantum Computer Simulators and Quantum Computer Hardware, we have successfully employed the Quantum Support Vector Machine Method (QSVM) for a ttH (H to two photons), Higgs coupling to top quarks analysis at LHC..
SISSA, 2019 - 7 p.
- Published in : PoS LeptonPhoton2019 (2019) 049
Fulltext: PDF;
In : XXIX International Symposium on Lepton Photon Interactions at High Energies, Toronto, Canada, 5 - 10 Aug 2019, pp.049
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7.
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HEP Software Foundation Community White Paper Working Group -- Data Organization, Management and Access (DOMA)
/ Berzano, Dario (CERN) ; Bianchi, Riccardo Maria (Pittsburgh U.) ; Bird, Ian (CERN) ; Bockelman, Brian (Nebraska U.) ; Campana, Simone (CERN) ; De, Kaushik (Texas U., Arlington) ; Duellmann, Dirk (CERN) ; Elmer, Peter (Princeton U.) ; Gardner, Robert (Chicago U., EFI) ; Garonne, Vincent (Oslo U.) et al.
Without significant changes to data organization, management, and access (DOMA), HEP experiments will find scientific output limited by how fast data can be accessed and digested by computational resources. [...]
arXiv:1812.00761 ; HSF-CWP-2017-04 ; FERMILAB-PUB-18-671-CD.
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2018. - 18 p.
Fermilab Library Server (fulltext available) - Fulltext - Fulltext
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9.
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The Principals and Practice of Distributed High Throughput Computing
/ Dr. Livny, Miron (speaker) (University of Wisconsin-Madison)
The potential of Distributed Processing Systems to deliver computing capabilities with qualities ranging from high availability and reliability to easy expansion in functionality and capacity were recognized and formalized in the 1970’s. For more three decade these principals Distributed Computing guided the development of the HTCondor resource and job management system [...]
2016 - 4481.
CERN Computing Colloquium
External link: Event details
In : The Principals and Practice of Distributed High Throughput Computing
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10.
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The Open Science Grid
/ Pordes, Ruth (Fermilab) ; Kramer, Bill (LBL, Berkeley) ; Olson, Doug (LBL, Berkeley) ; Livny, Miron (Wisconsin U., Madison) ; Roy, Alain (Wisconsin U., Madison) ; Avery, Paul (Florida U.) ; Blackburn, Kent (Caltech) ; Wenaus, Torre (Brookhaven) ; Wuerthwein, Frank K. (UC, San Diego) ; Gardner, Rob (Chicago U.) et al.
The Open Science Grid (OSG) provides a distributed facility where the Consortium members provide guaranteed and opportunistic access to shared computing and storage resources. OSG provides support for and evolution of the infrastructure through activities that cover operations, security, software, troubleshooting, addition of new capabilities, and support for existing and engagement with new communities. [...]
FERMILAB-CONF-07-217-CD.-
2007 - 15 p.
- Published in : J. Phys.: Conf. Ser. 78 (2007) 012057
Fulltext: PDF;
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