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1.
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Data Placement Optimization for ATLAS in a Multi-Tiered Storage System within a Data Center
/ Huang, Qiulan (Brookhaven National Laboratory (US)) ; Garonne, Vincent (Brookhaven National Laboratory (US)) ; Yoo, Shinjae (Brookhaven National Laboratory (US))
/ATLAS Collaboration
Scientific experiments and computations, especially in High Energy Physics, are generating and accumulating data at an unprecedented rate. Effectively managing this vast volume of data while ensuring efficient data analysis poses a significant challenge for data centers, which must integrate various storage technologies. [...]
ATL-SOFT-SLIDE-2024-534.-
Geneva : CERN, 2024 - 15 p.
Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 27th International Conference on Computing in High Energy & Nuclear Physics, Kraków, Pl, 19 - 25 Oct 2024
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2.
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Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
/ Alexeev, Yuri (Argonne, PHY) ; Amsler, Maximilian (Unlisted, DE) ; Barroca, Marco Antonio (Rio de Janeiro, IMPA ; Rio de Janeiro, CBPF) ; Bassini, Sanzio (CINECA) ; Battelle, Torey (Arizona State U.) ; Camps, Daan (LBL, Berkeley) ; Casanova, David (Donostia Intl. Phys. Ctr., San Sebastian ; IKERBASQUE, Bilbao ; Basque U., Bilbao) ; Choi, Young Jai (Yonsei U.) ; Chong, Frederic T. (Chicago U.) ; Chung, Charles (IBM Watson Res. Ctr.) et al.
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. [...]
arXiv:2312.09733; FERMILAB-PUB-24-0001-SQMS.-
2024-05-31 - 45 p.
- Published in : Future Gener. Comput. Syst. 160 (2024) 666-710
Fulltext: FERMILAB-PUB-24-0001-SQMS - PDF; 990a7c5cfb7293c88d2918a117658c8c - PDF; 2312.09733 - PDF; External link: Fermilab Accepted Manuscript
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3.
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Quantum Computing for High-Energy Physics : State of the Art and Challenges
/ Di Meglio, Alberto (CERN) ; Jansen, Karl (DESY, Zeuthen ; Cyprus Inst.) ; Tavernelli, Ivano (IBM, Zurich) ; Alexandrou, Constantia (Cyprus U. ; Cyprus Inst.) ; Arunachalam, Srinivasan (IBM Watson Res. Ctr.) ; Bauer, Christian W. (LBNL, Berkeley) ; Borras, Kerstin (DESY ; Aachen, Tech. Hochsch.) ; Carrazza, Stefano (Milan U. ; CERN) ; Crippa, Arianna (DESY, Zeuthen ; Humboldt U., Berlin) ; Croft, Vincent (U. Leiden (main)) et al.
Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage, namely a significant (in some cases exponential) speed-up of numerical simulations. The rapid development of hardware devices with various realizations of qubits enables the execution of small scale but representative applications on quantum computers. [...]
arXiv:2307.03236; FERMILAB-PUB-23-468-ETD.-
2024-08-01 - 49 p.
- Published in : PRX Quantum 5 (2024) 037001
Fulltext: 2307.03236 - PDF; FERMILAB-PUB-23-468-ETD - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server
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4.
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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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5.
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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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6.
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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: $t\bar{t}H$ (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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7.
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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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