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Quantum inspired algorithm: Tensor Networks
/ Di Marcantonio, Francesco (speaker) (UPV/EHU University of the Basque Country (SP)) ; Puljak, Ema (speaker) (Universitat Autonoma de Barcelona (ES))
Abstract
This talk with cover a foundational understanding of tensor networks, starting with the basics of tensors and their contractions. Students will learn simple forms of tensor networks and their connection to quantum circuits. [...]
2024 - 2823.
CERN openlab summer student lecture programme; Quantum Computing Applications and Use-cases
External links: Talk details; Event details
In : Quantum Computing Applications and Use-cases
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Basics of quantum computing (theory)
/ Puljak, Ema (speaker) (Universitat Autonoma de Barcelona (ES))
Abstract
In this basic introduction to quantum computing, the underlying mathematical concepts will be presented together with quantum mechanical phenoma of interest such as superposition and entanglement in order to enable the understanding of basic quantum circuits.
Bio
Ema is a computer scientist pursuing PhD in physics at CERN and Universitat Autonoma de Barcelona, specializing in quantum and quantum-inspired algorithms for anomaly detection with applications in high-energy physics and medical imaging..
2024 - 2315.
CERN openlab summer student lecture programme; Basics of Quantum Computing
External links: Talk details; Event details
In : Basics of Quantum Computing
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3.
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Unravelling physics beyond the standard model with classical and quantum anomaly detection
/ Schuhmacher, Julian (IBM, Zurich) ; Boggia, Laura (IBM, Zurich ; Zurich, ETH) ; Belis, Vasilis (Zurich, ETH) ; Puljak, Ema (Barcelona, Autonoma U. ; CERN) ; Grossi, Michele (CERN) ; Pierini, Maurizio (CERN) ; Vallecorsa, Sofia (CERN) ; Tacchino, Francesco (IBM, Zurich) ; Barkoutsos, Panagiotis (IBM, Zurich) ; Tavernelli, Ivano (IBM, Zurich)
Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC). However, current experiments do not indicate clear signs of new physics that could guide the development of additional Beyond Standard Model (BSM) theories. [...]
arXiv:2301.10787.-
2023-11-16 - 15 p.
- Published in : Mach. Learn. Sci. Tech. 4 (2023) 045031
Fulltext: document - PDF; 2301.10787 - PDF;
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4.
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Quantum anomaly detection in the latent space of proton collision events at the LHC
/ Belis, Vasilis (Zurich, ETH) ; Woźniak, Kinga Anna (CERN ; Vienna U.) ; Puljak, Ema (CERN ; Barcelona U.) ; Barkoutsos, Panagiotis (IBM, Zurich) ; Dissertori, Günther (Zurich, ETH) ; Grossi, Michele (CERN) ; Pierini, Maurizio (CERN) ; Reiter, Florentin (Zurich, ETH) ; Tavernelli, Ivano (IBM, Zurich) ; Vallecorsa, Sofia (CERN)
The ongoing quest to discover new phenomena at the LHC necessitates the continuous development of algorithms and technologies. Established approaches like machine learning, along with emerging technologies such as quantum computing show promise in the enhancement of experimental capabilities. [...]
arXiv:2301.10780.-
2024-10-14 - 11 p.
- Published in : Commun. Phys. 7 (2024) 334
Fulltext: 2301.10780 - PDF; document - PDF;
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5.
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CMS Phase-2 DAQ and Timing Hub -- Prototyping results and perspectives
/ Amoiridis, Vasileios (CERN) ; Behrens, Ulf (Rice U.) ; Bocci, Andrea (CERN) ; Branson, James (UC, San Diego) ; Brummer, Philipp Maximilian (CERN) ; Cittolin, Sergio (UC, San Diego) ; Da Silva Gomes, Diego (Rio de Janeiro State U.) ; Darlea, Georgiana Lavinia (MIT) ; Deldicque, Christian (CERN) ; Dobson, Marc (CERN) et al.
This paper describes recent progress on the design of the DAQ and Timing Hub, or DTH, an ATCA hub board intended for the Phase-2 upgrade of the CMS experiment. Prototyping was originally divided into multiple feature lines, spanning all different aspects of the DTH functionality. [...]
CMS-CR-2021-213.-
Geneva : CERN, 2022 - 7 p.
- Published in : JINST 17 (2022) C05003
Fulltext: PDF;
In : TWEPP 2021 Topical Workshop on Electronics for Particle Physics, Online, Online, 20 - 24 Sep 2021, pp.C05003
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6.
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The Phase-2 Upgrade of the CMS Data Acquisition
/ CMS Collaboration
The High Luminosity LHC (HL-LHC) will start operating in 2027 after the third Long Shutdown (LS3), and is designed to provide an ultimate instantaneous luminosity of $7.5\times10^{34}$ cm$^{-2}$ s$^{-1}$, at the price of extreme pileup of up to 200 interactions percrossing. The number of overlapping interactions in HL-LHC collisions, their density, and the resulting intense radiation environment, warrant an almost complete upgrade of the CMS detector.The upgraded CMS detector will be read out by approximately 50 thousand high-speed front-end optical links at an unprecedented data rate of up to 80~Tb/s,for an average expected total event size of approximately 7-10 MB.Following the present established design, the CMS trigger and data acquisition system will continue to feature two trigger levels, with only one synchronous hardware-based Level-1 Trigger (L1),consisting of custom electronic boards and operating on dedicated data streams, and a second level, the High Level Trigger (HLT), using software algorithms running asynchronously on standard processors and making use of the full detector data to select events for offline storage and analysis.The upgraded CMS data acquisition system will collect data fragments for Level-1 accepted events from the detector back-end modules at a rate up to 750 kHz, aggregate fragments corresponding to individual Level-1 accepts into events, and distribute them to the HLT processors where they will be further selected. [...]
CMS-CR-2021-076.-
Geneva : CERN, 2021 - 12 p.
- Published in : EPJ Web Conf. 251 (2021) 04023
Fulltext: PDF;
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021, pp.04023
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7.
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40 MHz Scouting with Deep Learning in CMS
/ Golubovic, Dejan (CERN) ; James, Thomas Owen (CERN) ; Meschi, Emilio (CERN) ; Puljak, Ema (Boskovic Inst., Zagreb) ; Rabady, Dinyar Sebastian (CERN) ; Zahid Rasheed, Awais (CERN-based) ; Sakulin, Hannes (CERN) ; Vourliotis, Emmanouil (Athens U.) ; Zejdl, Petr (CERN)
/CMS Collaboration
A 40 MHz scouting system at CMS would provide fast and virtually unlimited statistics for detector diagnostics, alternative luminosity measurements and, in some cases, calibrations, and it has the potential to enable the study of otherwise inaccessible signatures, either too common to fit in the L1 accept budget, or with requirements which are orthogonal to ``mainstream'' physics, such as long-lived particles.
Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw inputs. A series of studies on different aspects of LHC data processing have demonstrated the potential of deep learning for CERN applications. [...]
CMS-CR-2020-109.-
Geneva : CERN, 2020 - 10 p.
Fulltext: PDF;
In : Connecting The Dots 2020, Princeton, United States Of America, 20 - 30 Apr 2020
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8.
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Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
/ Govorkova, Ekaterina (CERN) ; Puljak, Ema (CERN) ; Aarrestad, Thea (CERN) ; James, Thomas (CERN) ; Loncar, Vladimir (CERN) ; Pierini, Maurizio (CERN) ; Pol, Adrian Alan (CERN) ; Ghielmetti, Nicolò (CERN) ; Graczyk, Maksymilian (CERN) ; Summers, Sioni (CERN) et al.
In this paper, we show how to adapt and deploy anomaly detection algorithms based on deep autoencoders, for the unsupervised detection of new physics signatures in the extremely challenging environment of a real-time event selection system at the Large Hadron Collider (LHC). We demonstrate that new physics signatures can be enhanced by three orders of magnitude, while staying within the strict latency and resource constraints of a typical LHC event filtering system. [...]
arXiv:2108.03986; FERMILAB-PUB-21-487-CMS; FERMILAB-PUB-21-487-CMS.-
2022-02-23 - 12 p.
- Published in : Nature Mach. Intell. 4 (2022) 154-161
Fulltext: 2108.03986 - PDF; fermilab-pub-21-487-cms - PDF; External link: Fermilab Library Server
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