1.
|
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
|
|
2.
|
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
|
|
3.
|
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
|
|
4.
|
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
|
|
5.
|
High-Energy and Ultra-High-Energy Neutrinos: A Snowmass White Paper
/ Ackermann, Markus (DESY, Zeuthen) ; Bustamante, Mauricio (Bohr Inst.) ; Lu, Lu (Wisconsin U., Madison) ; Otte, Nepomuk (Georgia Tech., Atlanta) ; Reno, Mary Hall (Iowa U.) ; Wissel, Stephanie (Penn State U., University Park, IGC) ; Agarwalla, Sanjib K. (Bhubaneswar, Inst. Phys. ; HBNI, Mumbai ; ICTP, Trieste) ; Alvarez-Muñiz, Jaime (Santiago de Compostela U., IGFAE) ; Alves Batista, Rafael (U. Autonoma, Madrid (main)) ; Argüelles, Carlos A. (Harvard U.) et al.
Astrophysical neutrinos are excellent probes of astroparticle physics and high-energy physics. With energies far beyond solar, supernovae, atmospheric, and accelerator neutrinos, high-energy and ultra-high-energy neutrinos probe fundamental physics from the TeV scale to the EeV scale and beyond. [...]
arXiv:2203.08096.-
2022-11 - 56 p.
- Published in : JHEAp: 36 (2022) , pp. 55-110
Fulltext: PDF; External link: eConf
In : 2021 Snowmass Summer Study, Seattle, WA, United States, 11 - 20 July 2021, pp.55-110
|
|
6.
|
lenstronomy II: A gravitational lensing software ecosystem
/ Birrer, Simon (KIPAC, Menlo Park ; SLAC) ; Shajib, Anowar J. (Chicago U., Astron. Astrophys. Ctr. ; UCLA) ; Gilman, Daniel (Toronto U., Astron. Dept.) ; Galan, Aymeric (CERN ; Ecole Polytechnique, Lausanne) ; Aalbers, Jelle (KIPAC, Menlo Park ; SLAC) ; Millon, Martin (CERN ; Ecole Polytechnique, Lausanne) ; Morgan, Robert (Wisconsin U., Madison) ; Pagano, Giulia ; Park, Ji Won (KIPAC, Menlo Park ; SLAC) ; Teodori, Luca et al.
lenstronomy is an Astropy-affiliated Python package for gravitational lensing simulations and analyses. lenstronomy was introduced by Birrer and Amara (2018) and is based on the linear basis set approach by Birrer et a. [...]
arXiv:2106.05976.-
2021-06-08
- Published in : J. Open Source Softw. 6 (2021) 3283
Fulltext: 2106.05976 - PDF; document - PDF;
|
|
7.
|
The AWAKE Run 2 programme and beyond
/ AWAKE Collaboration
Plasma wakefield acceleration is a promising technology to reduce the size of particle accelerators. Use of high energy protons to drive wakefields in plasma has been demonstrated during Run 1 of the AWAKE programme at CERN. [...]
arXiv:2206.06040.-
2022-08-12 - 21 p.
- Published in : Symmetry 14 (2022) 1680
Fulltext: document - PDF; 2206.06040 - PDF;
|
|
8.
|
Development of the Self-Modulation Instability of a Relativistic Proton Bunch in Plasma
/ AWAKE Collaboration
Self-modulation is a beam-plasma instability that is useful to drive large-amplitude wakefields with bunches much longer than the plasma skin depth. We present experimental results showing that, when increasing the ratio between the initial transverse size of the bunch and the plasma skin depth, the instability occurs later along the bunch, or not at all, over a fixed plasma length, because the amplitude of the initial wakefields decreases. [...]
arXiv:2305.05478.-
2023-08-01 - 10 p.
- Published in : Phys. Plasmas 30 (2023) 083104
Fulltext: 2305.05478 - PDF; Publication - PDF;
|
|
9.
|
|
10.
|
|