为粒子物理采用异构、边缘和量子硬件的机器学习（ML-HEQUPP）
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
AI总结：
本文提出利用异构、边缘和量子硬件进行机器学习，以应对粒子物理实验中前所未有的数据挑战。
摘要：
下一代粒子物理实验将面临数据获取的新挑战，由于前所未有的数据速率和体积以及极端环境和操作限制。利用这些数据进行科学发现需要实时推理和决策、智能数据缩减以及超越当前能力的高效处理架构。该实验范式成功的关键技术包括人工智能和机器学习（AI/ML）、硅微电子学以及量子算法和处理的出现。它们的交叉领域包括边缘计算的低功耗和低延迟设备、异构加速器系统、可重构硬件、新颖的代码设计和合成策略、低温或高辐射环境下的读出技术以及模拟计算。本文白皮书提出了一种由社区主导的愿景，以识别和优先考虑基于硬件的机器学习系统及其相应物理应用的研究和开发机会，从而推动向基础科学新数据前沿的顺利过渡。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
