Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

2026-02-24

AI Summary

This work addresses the challenges of real-time inference and efficient processing posed by extreme environments and massive data volumes in next-generation particle physics experiments. It proposes a novel hardware-algorithm co-design paradigm that integrates edge computing, heterogeneous accelerators, reconfigurable hardware, analog computing, and quantum algorithms to construct a low-power, low-latency machine learning system. Specifically optimized for harsh conditions such as high radiation and cryogenic temperatures, the system enables intelligent data compression and real-time analysis. By doing so, it catalyzes a paradigm shift in scientific data processing architectures and establishes a community-driven roadmap that delineates key research directions and application pathways for hardware-driven artificial intelligence at the frontiers of fundamental science.

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Human-in-the-loop Machine Learning
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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) and 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.

Problem

• particle physics
• real-time inference
• data reduction
• edge computing
• heterogeneous hardware

Innovation

• heterogeneous hardware
• edge computing
• quantum algorithms
• hardware-software co-design
• low-latency inference