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Performance of Particle Tracking Using a Quantum Graph Neural Network
/ Tüysüz, Cenk (Middle East Tech. U., Ankara) ; Novotny, Kristiane (Unlisted, CH) ; Rieger, Carla (Zurich, ETH) ; Carminati, Federico (CERN) ; Demirköz, Bilge (Middle East Tech. U., Ankara) ; Dobos, Daniel (Unlisted, CH ; Lancaster U.) ; Fracas, Fabio (CERN ; Padua U.) ; Potamianos, Karolos (Unlisted, CH ; Oxford U.) ; Vallecorsa, Sofia (CERN) ; Vlimant, Jean-Roch (Caltech)
The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC. [...]
arXiv:2012.01379.
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6 p.
Fulltext
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Data-Parallel Training of Generative Adversarial Networks on HPC Systems for HEP Simulations
/ Vallecorsa, Sofia (CERN) ; Moise, Diana (Unlisted, CH) ; Carminati, Federico (CERN) ; Khattak, Gul Rukh (CERN)
In the field of High Energy Physics (HEP), simulating the interaction of particle detector materials is a compute-intensive task, that currently uses 50% of the computing resources globally available as part of the Worldwide LCH Computing Grid (WLCG). Since some level of approximation is acceptable, it is possible to implement fast simulation simplified models that have the advantage of being less computationally intensive. [...]
2018 - 10 p.
- Published in : 10.1109/hipc.2018.00026
In : 25th IEEE International Conference on High Performance Computing (HiPC 2018), Bengaluru, India, 17 - 20 Dec 2018, pp.162
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Artificial Intelligence for Science, Industry and Society (AISIS 2019) - AISIS2019
21 - 25 Oct 2019
- Mexico City, Mexico
/ Escalant, Boris (ed.); Carminati, Federico (ed.); Barnafoldi, Gergely (ed.); Paic, Guy (ed.); Nellen, Lukas (ed.); Mayo, Rafael (ed.); Schramm, Steven (ed.); Ivezic, Zeljko (ed.)
2020
- Published in : PoS: AISIS2019 (2020)
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6.
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Evaluating POWER Architecture for Distributed Training of Generative Adversarial Networks
/ Hesam, Ahmad (CERN ; Delft Tech. U.) ; Vallecorsa, Sofia (CERN) ; Khattak, Gulrukh (CERN) ; Carminati, Federico (CERN)
The increased availability of High-Performance Computing resources can enable data scientists to deploy and evaluate data-driven approaches, notably in the field of deep learning, at a rapid pace. As deep neural networks become more complex and are ingesting increasingly larger datasets, it becomes unpractical to perform the training phase on single machine instances due to memory constraints, and extremely long training time. [...]
2019 - 9 p.
- Published in : 10.1007/978-3-030-34356-9_32
In : High Performance Computing: ISC High Performance 2019 International Workshops, Frankfurt, Germany, June 16-20, 2019, Revised Selected Papers, Frankfurt, Germany, 16 - 20 Jun 2019, pp.432-440
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7.
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Quantum Track Reconstruction Algorithms for non-HEP applications
/ Novotny, Kristiane Sylvia (gluoNNet) ; Tüysüz, Cenk (Middle East Tech. U., Ankara) ; Rieger, Carla (Zurich, ETH) ; Dobos, Daniel (gluoNNet ; Lancaster U.) ; Potamianos, Karolos Jozef (gluoNNet ; Oxford U.) ; Vallecorsa, Sofia (CERN) ; Carminati, Federico (CERN) ; Demirköz, Bilge (Middle East Tech. U., Ankara) ; Vlimant, Jean-Roch (Caltech) ; Fracas, Fabio (Padua U.)
The expected increase in simultaneous collisions creates a challenge for accurate particle track reconstruction in High Luminosity LHC experiments. Similar challenges can be seen in non-HEP trajectory reconstruction use-cases, where tracking and track evaluation algorithms are used. [...]
SISSA, 2021 - 6 p.
- Published in : PoS ICHEP2020 (2021) 983
Fulltext: PDF;
In : 40th International Conference on High Energy Physics (ICHEP), Prague, Czech Republic, 28 Jul - 6 Aug 2020, pp.983
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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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10.
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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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