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CERN Document Server 660 Datensätze gefunden  1 - 10nächsteEnde  gehen zum Datensatz: Die Suche hat 0.64 Sekunden gedauert. 
1.
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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2.
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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3.
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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4.
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. [...]
arXiv:2410.21611 ; HEPHY-ML-24-05 ; FERMILAB-PUB-24-0728-CMS ; TTK-24-43. - 204.
Fermilab Library Server - Fulltext - Fulltext
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5.
Spooky Quantum Action: From Thought Experiments to Real World Quantum Technology Application / Grossi, Michele (CERN) ; Di Meglio, Alberto (CERN) ; Vallecorsa, Sofia (CERN)
This chapter explores quantum physics, tracing the journey from theoretical concepts to real-world applications. [...]
2024 - 16.
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6.
A Study on Quantum Graph Neural Networks Applied to Molecular Physics / Piperno, Simone (U. Rome La Sapienza (main)) ; Ceschini, Andrea (U. Rome La Sapienza (main)) ; Chang, Su Yeon (CERN ; LPHE, Lausanne) ; Grossi, Michele (CERN) ; Vallecorsa, Sofia (CERN) ; Panella, Massimo (U. Rome La Sapienza (main))
This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. [...]
arXiv:2408.03427. - 20.
Fulltext
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7.
Order Parameter Discovery for Quantum Many-Body Systems / Mariella, Nicola (Unlisted, IE) ; Murphy, Tara (Unlisted, IE ; Cambridge U.) ; Di Marcantonio, Francesco (Basque U., Bilbao) ; Najafi, Khadijeh (IBM Watson Res. Ctr.) ; Vallecorsa, Sofia (CERN) ; Zhuk, Sergiy (Unlisted, IE) ; Rico, Enrique (Basque U., Bilbao ; Donostia Intl. Phys. Ctr., San Sebastian ; IKERBASQUE, Bilbao)
Quantum phase transitions reveal deep insights into the behavior of many-body quantum systems, but identifying these transitions without well-defined order parameters remains a significant challenge. [...]
arXiv:2408.01400. - 15.
Fulltext
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8.
Guided quantum compression for high dimensional data classification / Belis, Vasilis (Zurich, ETH) ; Odagiu, Patrick (Zurich, ETH) ; Grossi, Michele (CERN) ; Reiter, Florentin (Zurich, ETH) ; Dissertori, Günther (Zurich, ETH) ; Vallecorsa, Sofia (CERN)
Quantum machine learning provides a fundamentally different approach to analyzing data. However, many interesting datasets are too complex for currently available quantum computers. [...]
arXiv:2402.09524.- 2024-07-16 - 11 p. - Published in : Mach. Learn. Sci. Tech. 5 (2024) 035010 Fulltext: document - PDF; 2402.09524 - PDF;
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9.
Symmetry Breaking in Geometric Quantum Machine Learning in the Presence of Noise / Tüysüz, Cenk (DESY ; Humboldt U., Berlin) ; Chang, Su Yeon (CERN ; Ecole Polytechnique, Lausanne) ; Demidik, Maria (DESY ; Cyprus Inst.) ; Jansen, Karl (DESY ; Cyprus Inst.) ; Vallecorsa, Sofia (CERN) ; Grossi, Michele (CERN)
Geometric quantum machine learning based on equivariant quantum neural networks (EQNN) recently appeared as a promising direction in quantum machine learning. Despite the encouraging progress, the studies are still limited to theory, and the role of hardware noise in EQNN training has never been explored. [...]
arXiv:2401.10293.- 2024-07-01 - 20 p. - Published in : PRX Quantum 5 (2024) 030314 Fulltext: 2401.10293 - PDF; Publication - PDF;
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10.
Not yet available
 Foundation models / Vallecorsa, Sofia (speaker) (CERN) ; Luise, Ilaria (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 [...]
2024 - 7111. CERN openlab summer student lecture programme External link: Event details In : Foundation models
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