Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliders

2026-03-27

AI Summary

This work addresses the challenge of real-time detection of physics beyond the Standard Model in high-energy particle colliders, where complex correlations in high-dimensional data and stringent hardware constraints on edge devices limit conventional approaches. The authors propose a quantum-inspired tensor network method featuring a novel Skip Matrix Product Operator (SMPO) and its cascaded architecture, enabling efficient anomaly detection on classical hardware. The approach maintains high sensitivity to diverse benchmark signals of new physics while substantially improving computational efficiency and deployment flexibility. Notably, it meets the stringent latency and resource requirements of trigger systems when implemented on FPGA hardware, demonstrating the near-term viability of quantum-inspired machine learning for real-world applications in high-energy physics experiments.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Quantum machine learning offers the ability to capture complex correlations in high-dimensional feature spaces, crucial for the challenge of detecting beyond the Standard Model physics in collider events, along with the potential for unprecedented computational efficiency in future quantum processors. Near-term utilization of these benefits can be achieved by developing quantum-inspired algorithms for deployment in classical hardware to enable applications at the "edge" of current scientific experiments. This work demonstrates the use of tensor networks for real-time anomaly detection in collider detectors. A spaced matrix product operator (SMPO) is developed that provides sensitivity to a variety beyond the Standard Model benchmarks, and can be implemented in field programmable gate array hardware with resources and latency consistent with trigger deployment. The cascaded SMPO architecture is introduced as an SMPO variation that affords greater flexibility and efficiency in ways that are key to edge applications in resource-constrained environments. These results reveal the benefit and near-term feasibility of deploying quantum-inspired ML in high energy colliders.

Problem

• anomaly detection
• particle colliders
• beyond the Standard Model
• real-time
• edge computing

Innovation

• tensor networks
• quantum-inspired machine learning
• anomaly detection
• FPGA implementation
• matrix product operator