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

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.

💡 Research Summary

The white paper “Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML‑HEQUPP)” presents a comprehensive vision for addressing the unprecedented data‑acquisition challenges of next‑generation particle‑physics experiments. These experiments will generate data at rates and volumes far beyond current capabilities while operating in harsh environments that impose strict limits on power, latency, and reliability. The authors argue that only a tightly integrated stack of artificial‑intelligence/machine‑learning (AI/ML) algorithms, cutting‑edge silicon micro‑electronics, and emerging quantum technologies can meet these demands.

The document is organized into six major sections.

1. Introduction frames the scientific motivations—precision measurements, discovery‑driven searches across a wide energy range—and links them to the need for new detector instrumentation and computing paradigms. It highlights recent strategic planning efforts (Snowmass, P5, DOE BRN, AI‑native HEP) and introduces the concept of “ML‑HEQUPP” as a holistic, co‑design approach that treats algorithms, hardware, firmware, and system architecture as a single entity.

2. Technology Landscape surveys the hardware families that will form the backbone of ML‑HEQUPP:

   • ASICs – Custom application‑specific integrated circuits designed for real‑time feature extraction (e.g., the AIML65P1 neural‑network ASIC), in‑pixel signal processing, and analog neural‑network front‑ends. The emphasis is on ultra‑low power, sub‑microsecond latency, and radiation‑hard designs.

   • FPGAs – Reconfigurable logic platforms leveraged through high‑level synthesis tools such as hls4ml, CGRA4ML, and the SLA‑C neural‑network language. These enable rapid prototyping, flexible pipeline construction, and deployment of lightweight decision‑tree models for edge inference.

   • Quantum Processors and Sensors – An overview of superconducting and trapped‑ion quantum processors, their performance envelopes, and quantum‑machine‑learning algorithms (variational circuits, quantum graph neural networks). The paper also discusses quantum‑enhanced sensing (photonic quantum extreme learning machines, superconducting microwave kinetic inductance detectors) and distributed quantum networking for fast data reduction.

   • Novel Paradigms – Embedded FPGAs, analog compute (neuromorphic, compute‑in‑memory), open‑source hardware‑software co‑design toolchains, and heterogeneous pipelines that combine digital, analog, and quantum blocks. Special attention is given to operation in cryogenic or high‑radiation environments, where reconfigurable System‑on‑Chip (SoC) solutions are required.

   • Analysis Facilities – Infrastructure for training and deploying edge models, model‑optimization workflows, data‑access services, and the integration of front‑end inference with downstream analysis.

3. Physics Applications maps the hardware capabilities onto concrete scientific use cases:

   • Collider Physics – Real‑time vertex, tracking, and calorimetry reconstruction; AI‑driven trigger decisions; low‑latency inference for HL‑LHC and future circular colliders.

   • Dark‑Matter Searches – Low‑power AI/ML for axion haloscopes, quantum‑amplifier auto‑tuning, and quantum‑enhanced signal‑to‑noise extraction.

   • Neutrino Experiments – ML‑based supernova burst pointing in DUNE, online anomaly detection, smart time‑projection chamber (TPC) calibration, and self‑triggering for COHERENT.

   • Quantum Sensors & Experiments – Continuous‑variable photonic quantum extreme learning machines for fast collider‑data selection, quantum‑enhanced graph neural networks for particle tracking, and quantum sensing of radiative decays.

   • Accelerators – Physics‑informed AI for ultrafast X‑ray diffraction at SLAC’s LCLS, AI/ML for beam optimization at FEL facilities, and hybrid model‑based control loops for next‑generation accelerator tuning.

4. Community and Education outlines existing collaborations (A3D3, Next‑Generation Triggers, FastML, HFCC), workforce development strategies, and interdisciplinary impacts. It stresses the importance of open‑source toolchains, shared test‑beds, and cross‑sector partnerships to sustain a pipeline of talent at the intersection of HEP, AI, and hardware engineering.

5. Key R&D Topics enumerates a prioritized list of research thrusts, including: ASIC co‑design for AI inference, FPGA‑based heterogeneous pipelines, quantum‑hardware integration, analog compute architectures, robust data‑flow orchestration across classical‑accelerated‑quantum resources, and scalable analysis infrastructures. The roadmap is divided into short‑term edge‑ML deployments, mid‑term heterogeneous platform design for future experiments, and long‑term exploratory work on quantum‑hybrid workflows.

6. Conclusions reiterates that ML‑HEQUPP aligns with U.S. national priorities in AI, micro‑electronics, and quantum computing, offering a risk‑balanced portfolio that spans immediate experimental upgrades to transformative, long‑term capabilities. The authors call for a shift away from the traditional CPU/GPU‑centric model toward a truly heterogeneous, edge‑centric, quantum‑enabled architecture that maximizes physics reach while minimizing power and cost.

Overall, the paper provides a detailed, community‑driven blueprint for integrating AI/ML directly into the detector front‑end, leveraging ASICs, FPGAs, analog compute, and quantum technologies in a co‑designed ecosystem. By doing so, it aims to enable real‑time inference, intelligent data reduction, and new physics discovery potential across the full spectrum of particle‑physics research.