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CERN Document Server 732 records found  1 - 10nextend  jump to record: Search took 0.79 seconds. 
1.
Guided Graph Compression for Quantum Graph Neural Networks / Casals, Mikel (Barcelona, Polytechnic U.) ; Belis, Vasilis (Zurich, ETH) ; Combarro, Elias F. (U. Oviedo (main)) ; Alarcón, Eduard (Barcelona, Polytechnic U.) ; Vallecorsa, Sofia (CERN) ; Grossi, Michele (CERN)
Graph Neural Networks (GNNs) are effective for processing graph-structured data but face challenges with large graphs due to high memory requirements and inefficient sparse matrix operations on GPUs. [...]
arXiv:2506.09862. - 2025 - 11.
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Foundation models / Luise, Ilaria (speaker) (CERN) ; Vallecorsa, Sofia (speaker) (CERN)
Description Foundation models, also known as large-scale self-supervised models, have revolutionized the field of artificial intelligence. These models, such as ChatGPT and AlphaFold, are pre-trained on massive amounts of data and can be fine-tuned for a wide range of downstream tasks [...]
2025 - 6930. CERN openlab summer student lecture programme External link: Event details In : Foundation models
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Roughening and dynamics of an electric flux string in a (2+1)D lattice gauge theory / Di Marcantonio, Francesco (Basque U., Bilbao) ; Pradhan, Sunny (Basque U., Bilbao) ; Vallecorsa, Sofia (CERN) ; Bañuls, Mari Carmen (Hannover, Max Planck Inst. Quantenopt. ; MCQST, Munich) ; Ortega, Enrique Rico (Basque U., Bilbao ; CERN ; Donostia Intl. Phys. Ctr., San Sebastian ; IKERBASQUE, Bilbao)
We investigate the roughening transition in the pure $\mathbb{Z}_2$ lattice gauge theory in (2+1) dimensions. [...]
arXiv:2505.23853 ; CERN-TH-2025-105. - 2025-05-29 - 21.
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Quantum Information meets High-Energy Physics: Input to the update of the European Strategy for Particle Physics / Afik, Yoav (Chicago U., EFI) ; Fabbri, Federica (Bologna U. ; INFN, Bologna) ; Low, Matthew (Pittsburgh U.) ; Marzola, Luca (NICPB, Tallinn ; Tartu U.) ; Aguilar-Saavedra, Juan Antonio (Madrid, IFT) ; Altakach, Mohammad Mahdi (LPSC, Grenoble) ; Asbah, Nedaa Alexandra (CERN) ; Bai, Yang (Wisconsin U., Madison ; Argonne) ; Banks, Hannah (Cambridge U., DAMTP) ; Barr, Alan J. (Merton Coll., Oxford) et al.
Some of the most astonishing and prominent properties of Quantum Mechanics, such as entanglement and Bell nonlocality, have only been studied extensively in dedicated low-energy laboratory setups. The feasibility of these studies in the high-energy regime explored by particle colliders was only recently shown, and has gathered the attention of the scientific community. [...]
arXiv:2504.00086.- 2025 - 14 p. - Published in : Eur. Phys. J. Plus 140 (2025) 855 Fulltext: 2504.00086 - PDF; document - PDF;
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Validating the advantage of using ensembles over a single GAN model for calorimeter simulations / Jaruskova, Kristina (CERN ; Prague, Tech. U.) ; Vallecorsa, Sofia (CERN)
The use of generative deep learning models has been of interest in the high-energy physics community intending to develop a faster alternative to the compute-intensive Monte Carlo simulations. This work focuses on evaluating an ensemble of GANs on the task of electromagnetic calorimeter simulations. [...]
2024 - 3 p. - Published in : PoS ICHEP2024 (2025) 1045 Fulltext: PDF;
In : 42nd International Conference on High Energy Physics (ICHEP 2024), Prague, Czech Republic, 18 - 24 Jul 2024, pp.1045
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Not yet available
 CERN QTI2 / Vallecorsa, Sofia (speaker) (CERN)
2025 - 890. QTI other events or meetings; International Conference on Quantum Technologies for High-Energy Physics External links: Talk details; Event details In : International Conference on Quantum Technologies for High-Energy Physics
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7.
Transformers for Generalized Fast Shower Simulation / Raikwar, Piyush (CERN) ; Cardoso, Renato (CERN) ; Chernyavskaya, Nadezda (CERN) ; Jaruskova, Kristina (CERN) ; Pokorski, Witold (CERN) ; Salamani, Dalila (CERN) ; Srivatsa, Mudhakar (IBM Watson Res. Ctr.) ; Tsolaki, Kalliopi (CERN) ; Vallecorsa, Sofia (CERN) ; Zaborowska, Anna (CERN)
Recently, transformer-based foundation models have proven to be a generalized architecture applicable to various data modalities, ranging from text to audio and even a combination of multiple modalities. Transformers by design should accurately model the non-trivial structure of particle showers thanks to the absence of strong inductive bias, better modeling of long-range dependencies, and interpolation and extrapolation capabilities. [...]
2024 - 8 p. - Published in : EPJ Web Conf. 295 (2024) 09039 Fulltext: PDF;
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.09039
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Precise Quantum Angle Generator Designed for Noisy Quantum Devices / Rehm, Florian (CERN ; DESY) ; Vallecorsa, Sofia (RWTH Aachen U.) ; Borras, Kerstin (DESY) ; Krücker, Dirk (RWTH Aachen U.) ; Grossi, Michele (RWTH Aachen U.) ; Varo, Valle (RWTH Aachen U.)
The Quantum Angle Generator (QAG) is a cutting-edge quantum machine learning model designed to generate precise images on current Noise Intermediate Scale Quantum devices. It utilizes variational quantum circuits and incorporates the MERA-upsampling architecture, achieving exceptional accuracy. [...]
2024 - 8 p. - Published in : EPJ Web Conf. 295 (2024) 12006 Fulltext: PDF;
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.12006
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Measurements With A Quantum Vision Transformer: A Naive Approach / Pasquali, Dominic (UC, Santa Cruz ; CERN) ; Grossi, Michele (CERN) ; Vallecorsa, Sofia (CERN)
In mainstream machine learning, transformers are gaining widespread usage. As Vision Transformers rise in popularity in computer vision, they now aim to tackle a wide variety of machine learning applications. [...]
2024 - 8 p. - Published in : EPJ Web Conf. 295 (2024) 12003 Fulltext: PDF;
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.12003
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CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation / Krause, Claudius (ed.) (Vienna, OAW ; Heidelberg U.) ; Faucci Giannelli, Michele (ed.) (INFN, Rome2 ; Chalmers U. Tech.) ; Kasieczka, Gregor (ed.) (Hamburg U.) ; Nachman, Benjamin (ed.) (LBNL, Berkeley) ; Salamani, Dalila (ed.) (CERN) ; Shih, David (ed.) (Rutgers U., Piscataway) ; Zaborowska, Anna (ed.) (CERN) ; Amram, Oz (Fermilab) ; Borras, Kerstin (DESY ; Aachen, Tech. Hochsch.) ; Buckley, Matthew R. (Rutgers U., Piscataway) et al.
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. [...]
HEPHY-ML-24-05 ; FERMILAB-PUB-24-0728-CMS ; TTK-24-43 ; arXiv:2410.21611. - 204.
Fermilab Library Server - Fulltext - Fulltext
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