Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
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.
Keywords
Cite
@article{arxiv.2602.22248,
title = {Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)},
author = {Julia Gonski and Jenni Ott and Shiva Abbaszadeh and Sagar Addepalli and Matteo Cremonesi and Jennet Dickinson and Giuseppe Di Guglielmo and Erdem Yigit Ertorer and Lindsey Gray and Ryan Herbst and Christian Herwig and Tae Min Hong and Benedikt Maier and Maryam Bayat Makou and David Miller and Mark S. Neubauer and Cristián Peña and Dylan Rankin and Seon-Hee and Seo and Giordon Stark and Alexander Tapper and Audrey Corbeil Therrien and Ioannis Xiotidis and Keisuke Yoshihara and G Abarajithan and Sagar Addepalli and Nural Akchurin and Carlos Argüelles and Saptaparna Bhattacharya and Lorenzo Borella and Christian Boutan and Tom Braine and James Brau and Martin Breidenbach and Antonio Chahine and Talal Ahmed Chowdhury and Yuan-Tang Chou and Seokju Chung and Alberto Coppi and Mariarosaria D'Alfonso and Abhilasha Dave and Chance Desmet and Angela Di Fulvio and Karri DiPetrillo and Javier Duarte and Auralee Edelen and Jan Eysermans and Yongbin Feng and Emmett Forrestel and Dolores Garcia and Loredana Gastaldo and Julián García Pardiñas and Lino Gerlach and Loukas Gouskos and Katya Govorkova and Carl Grace and Christopher Grant and Philip Harris and Ciaran Hasnip and Timon Heim and Abraham Holtermann and Tae Min Hong and Gian Michele Innocenti and Koji Ishidoshiro and Miaochen Jin and Jyothisraj Johnson and Stephen Jones and Andreas Jung and Georgia Karagiorgi and Ryan Kastner and Nicholas Kamp and Doojin Kim and Kyoungchul Kong and Katie Kudela and Jelena Lalic and Bo-Cheng Lai and Yun-Tsung Lai and Tommy Lam and Jeffrey Lazar and Aobo Li and Zepeng Li and Haoyun Liu and Vladimir Lončar and Luca Macchiarulo and Christopher Madrid and Benedikt Maier and Zhenghua Ma and Prashansa Mukim and Mark S. Neubauer and Victoria Nguyen and Sungbin Oh and Isobel Ojalvo and Hideyoshi Ozaki and Simone Pagan Griso and Myeonghun Park and Christoph Paus and Santosh Parajuli and Benjamin Parpillon and Sara Pozzi and Ema Puljak and Benjamin Ramhorst and Amy Roberts and Larry Ruckman and Kate Scholberg and Sebastian Schmitt and Noah Singer and Eluned Anne Smith and Alexandre Sousa and Michael Spannowsky and Sioni Summers and Yanwen Sun and Daniel Tapia Takaki and Antonino Tumeo and Caterina Vernieri and Belina von Krosigk and Yash Vora and Linyan Wan and Michael H. L. S. Wang and Amanda Weinstein and Andy White and Simon Williams and Felix Yu},
journal= {arXiv preprint arXiv:2602.22248},
year = {2026}
}
Comments
125 pages, 51 figures