Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jennifer Ott, Shiva Abbaszadeh, Sagar Addepalli, Matteo Cremonesi, Jennet Dickinson, G. D. Guglielmo, E. Y. Ertorer, Lindsey Gray, Ryan Herbst, C. Herwig, Tae Min Hong, B. Maier, M. Makou, D.A.B. Miller, Mark S. Neubauer, Cristián Peña, D. Rankin, S. Seo, Giordon Stark, Alexander D. Tapper, A. Therrien, I. Xiotidis, K. Yoshihara, G. Abarajithan, N. Akchurin, Carlos A. Arguelles, S. Bhattacharya, Lorenzo Borella, C. Boutan, T. Braine, J. Brau, M. Breidenbach, A. Chahine, Talal Ahmed Chowdhury, Yuan-Tang Chou, Seokju Chung, A. Coppi, M. D'Alfonso, Abhilasha Dave, C. DeSmet, A. D. Fulvio, K. DiPetrillo, Javier Duarte, A. Edelen, J. Eysermans, Yongbing Feng, Emmet P. Forrestel, Dolores Garcia, L. Gastaldo, J. G. Pardinas, Lino Gerlach, L. Gouskos, Katya Govorkova, Carl Grace, C. Grant, Philip Harris, C. Hasnip, T. Heim, Abraham Holtermann, G. Innocenti, K. Ishidoshiro, Miaochen Jin, Jyothisraj Johnson, S. Jones, Andreas Jung, G. Karagiorgi, R. Kastner, N. Kamp, Doojin Kim, Kyoungchul Kong, K. Kudela, J. Lalic, Bo-Cheng Lai, Yunliang Lai, Tommy Lam, J. Lazar, Ao Li, Zepeng Li, Haoyu Liu, Vladimir Lonvcar, L. Macchiarulo, Christopher Madrid, Zheng Ma, P. Mukim, Victoria V Nguyen, Sung-Mook Oh, I. Ojalvo, H. Ozaki, S. P. Griso, Myeonghun Park, C. Paus, Santoshi Parajuli, Benjamin Parpillon, S. Pozzi, E. Puljak, Benjamin Ramhorst, Amy Roberts, L. Ruckman, K. Scholberg, Sebastian Schmitt, N. Singer, E. Smith, Alexandre Sousa, M. Spannowsky, Sioni Summers, Yanwen Sun, D. Takaki, Antonino Tumeo, C. Vernieri, B. Krosigk, Yashil Vora, Linyan Wan, Michael Wang, A. Weinstein, Andy White, Simon R.P. Williams, F. Yu·February 24, 2026·DOI: 10.48550/arXiv.2602.22248
Computer SciencePhysicsEngineering
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Abstract
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML), silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.