Modeling performance of data collection systems for high-energy physics by Wilkie Olin-Ammentorp, Xingfu Wu, Andrew A. Chien

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Quantum Computing for High-Energy Physics: State of the Art and Challenges by Alberto Di Meglio, Karl Jansen, Ivano Tavernelli, Constantia Alexandrou, Srinivasan Arunachalam, Christian W. Bauer, Kerstin Borras, Stefano Carrazza, Arianna Crippa, Vincent Croft, Roland de Putter, Andrea Delgado, Vedran Dunjko, Daniel J. Egger, Elias Fernández-Combarro, Elina Fuchs, Lena Funcke, Daniel González-Cuadra, Michele Grossi, Jad C. Halimeh, Zoë Holmes, Stefan Kühn, Denis Lacroix, Randy Lewis, Donatella Lucchesi, Miriam Lucio Martinez, Federico Meloni, Antonio Mezzacapo, Simone Montangero, Lento Nagano, Vincent R. Pascuzzi, Voica Radescu, Enrique Rico Ortega, Alessandro Roggero, Julian Schuhmacher, Joao Seixas, Pietro Silvi, Panagiotis Spentzouris, Francesco Tacchino, Kristan Temme, Koji Terashi, Jordi Tura, Cenk Tüysüz, Sofia Vallecorsa, Uwe-Jens Wiese, Shinjae Yoo, Jinglei Zhang

“…In particular, the high-energy physics community plays a pivotal role in accessing the power of quantum computing, since the field is a driving source for challenging computational problems. …”
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Adiabatic quantum algorithm for multijet clustering in high energy physics by Diogo Pires, Yasser Omar, João Seixas

Subjects: “…High-energy physics…”
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A dimensions aware evaluator for High Energy Physics applications by Couturier Ben, Clemencic Marco

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Quantum data learning for quantum simulations in high-energy physics by Lento Nagano, Alexander Miessen, Tamiya Onodera, Ivano Tavernelli, Francesco Tacchino, Koji Terashi

“…In this article, we explore the applicability of quantum data learning to practical problems in high-energy physics, aiming to identify domain specific use-cases where quantum models can be employed. …”
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Uncertainties associated with GAN-generated datasets in high energy physics by Konstantin T. Matchev, Alexander Roman, Prasanth Shyamsundar

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Evaluating summarization and inference techniques for high energy physics applications by Diehl, Hannah R.

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Machine Learning in High Energy Physics Community White Paper by Albertsson, Kim, Altoe, Piero, Anderson, Dustin, Andrews, Michael, Araque Espinosa, Juan Pedro, Aurisano, Adam, Basara, Laurent, Bevan, Adrian, Bhimji, Wahid, Bonacorsi, Daniele, Calafiura, Paolo, Campanelli, Mario, Capps, Louis, Carminati, Federico, Carrazza, Stefano, Childers, Taylor, Coniavitis, Elias, Cranmer, Kyle, David, Claire, Davis, Douglas, Duarte, Javier, Erdmann, Martin, Eschle, Jonas, Farbin, Amir, Feickert, Matthew, Castro, Nuno Filipe, Fitzpatrick, Conor, Floris, Michele, Forti, Alessandra, Garra-Tico, Jordi, Gemmler, Jochen, Girone, Maria, Glaysher, Paul, Gleyzer, Sergei, Gligorov, Vladimir, Golling, Tobias, Graw, Jonas, Gray, Lindsey, Greenwood, Dick, Hacker, Thomas, Harvey, John, Hegner, Benedikt, Heinrich, Lukas, Hooberman, Ben, Junggeburth, Johannes, Kagan, Michael, Kane, Meghan, Kanishchev, Konstantin, Karpiński, Przemysław, Kassabov, Zahari, Kaul, Gaurav, Kcira, Dorian, Keck, Thomas, Klimentov, Alexei, Kowalkowski, Jim, Kreczko, Luke, Kurepin, Alexander, Kutschke, Rob, Kuznetsov, Valentin, Köhler, Nicolas, Lakomov, Igor, Lannon, Kevin, Lassnig, Mario, Limosani, Antonio, Louppe, Gilles, Mangu, Aashrita, Mato, Pere, Meinhard, Helge, Menasce, Dario, Moneta, Lorenzo, Moortgat, Seth, Narain, Meenakshi, Neubauer, Mark, Newman, Harvey, Pabst, Hans, Paganini, Michela, Paulini, Manfred, Perdue, Gabriel, Perez, Uzziel, Picazio, Attilio, Pivarski, Jim, Prosper, Harrison, Psihas, Fernanda, Radovic, Alexander, Reece, Ryan, Rinkevicius, Aurelius, Rodrigues, Eduardo, Rorie, Jamal, Rousseau, David, Sauers, Aaron, Schramm, Steven, Schwartzman, Ariel, Severini, Horst, Seyfert, Paul, Siroky, Filip, Skazytkin, Konstantin, Sokoloff, Mike, Stewart, Graeme, Stienen, Bob, Stockdale, Ian, Strong, Giles, Thais, Savannah, Tomko, Karen, Upfal, Eli, Usai, Emanuele, Ustyuzhanin, Andrey, Vala, Martin, Vallecorsa, Sofia, Vasel, Justin, Verzetti, Mauro, Vilasís-Cardona, Xavier, Vlimant, Jean-Roch, Vukotic, Ilija, Wang, Sean-Jiun, Watts, Gordon, Williams, Michael, Wu, Wenjing, Wunsch, Stefan, Zapata, Omar

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Retracted: Extracting the QCD Cutoff Parameter Using the Bernstein Polynomials and the Truncated Moments by Advances in High Energy Physics

“…Advances in High Energy Physics…”
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Retracted: Performance of Water-Based Liquid Scintillator: An Independent Analysis by Advances in High Energy Physics

“…Advances in High Energy Physics…”
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Expression of Concern on “Antigravity, an Answer to Nature’s Phenomena including the Expansion of the Universe” by Advances in High Energy Physics

“…Advances in High Energy Physics…”
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Contacts between high energy physics and other fields of physics : proceedings by International Universitatswochen fur Kernphysik der Karl-Franzens (1977 : Universitat Graz), Urban, Paul

Relativistic kinematics : a guide to the kinematic problems of high-energy physics/ by 320402 Hagedorn, Rolf

Co-Design of Quantum Hardware and Algorithms in Nuclear and High Energy Physics by Franz Maja, Zurita Pía, Diefenthaler Markus, Mauerer Wolfgang

“…Quantum computing (QC) has emerged as a promising technology, and is believed to have the potential to advance nuclear and high energy physics (NHEP) by harnessing quantum mechanical phenomena to accelerate computations. …”
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Quantum convolutional neural networks for high energy physics data analysis by Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang, Haiwang Yu, Shinjae Yoo

“…This paper presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment. …”
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Distilling particle knowledge for fast reconstruction at high-energy physics experiments by A Bal, T Brandes, F Iemmi, M Klute, B Maier, V Mikuni, T K Årrestad

“…Our study proves that knowledge transfer between networks of different complexity can be used for fast artificial intelligence (AI) in high-energy physics that improves the expressiveness of observables over non-AI-based reconstruction algorithms. …”
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Scientometric indicators for Brazilian research on High Energy Physics, 1983-2013 by GONZALO R. ALVAREZ, SAMILE A.S. VANZ, MARCIA C. BARBOSA

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Order out of Chaos? A Case Study in High Energy Physics by Rafaela Hillerbrand

“…Taking experiments in high energy physics as a study case, this paper contends that using these types of entangled material and numerical experiments as a source of new phenomena or for theory testing must presuppose a certain understanding of causality and thus binds us at least to a weak form of realism.…”
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New Inogranic Scintillators’ Application in the Electromagnetic Calorimetry in High-Energy Physics by Dmitry Averyanov, Dmitry Blau

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Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics by Eyup B. Unlu, Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva

“…In this work, we construct several variations of a quantum hybrid vision transformer for a classification problem in high-energy physics (distinguishing photons and electrons in the electromagnetic calorimeter). …”
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