Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments

2026-07-22

AI Summary

This work proposes a variational quantum autoencoder (VQA)-based anomaly detection method tailored to the complex correlations in high-dimensional data and stringent real-time triggering requirements of high-energy collider experiments. By leveraging classical simulation and compilation workflows, the quantum machine learning model is, for the first time, deployed on FPGA hardware while satisfying low-latency and resource constraints. Experimental results demonstrate that the proposed approach achieves detection performance comparable to state-of-the-art classical methods, yet meets the demanding latency and hardware footprint specifications of future collider trigger systems. This advancement significantly enhances the capability of existing data acquisition pipelines and paves the way for collider infrastructure to evolve toward quantum readiness.

Abstract

Quantum machine learning (QML) algorithms in high energy physics (HEP) can efficiently represent and leverage long-range, high-order correlations in high-dimensional collider data, potentially with fewer parameters and favorable scaling relative to classical models. Deployment of QML in real-time collider applications such as trigger systems requires the ability to emulate and compile quantum circuits classically, then synthesize the resulting quantum gates onto low-latency hardware accelerators, namely field-programmable gate arrays (FPGAs). We present a study of variational quantum autoencoder models for real-time anomaly detection triggers in modern collider experiments. The models achieve performance comparable to state-of-the-art classical approaches and, after FPGA synthesis, satisfy resource usage and timing constraints consistent with trigger applications in future colliders. This work provides one of the first FPGA implementations of QML models for HEP triggers, enabling higher-capability models in today's classical data acquisition pipelines while advancing quantum readiness of collider experiment infrastructure.

Problem

• Quantum Machine Learning
• Real-Time Anomaly Detection
• Hardware Acceleration
• Collider Experiments
• FPGA

Innovation

• quantum autoencoder
• FPGA acceleration
• real-time anomaly detection
• high energy physics
• quantum machine learning