用FPGA实现量子自编码器，实现实时对撞机异常检测。
Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments
- 将量子自编码器编译为经典硬件可运行的门电路。
- 在FPGA上部署后满足未来对撞机触发系统的资源与延迟要求。
- 首次在对撞机触发中实现量子机器学习硬件加速，推动量子就绪。
高能物理中的量子机器学习算法能高效表示高维对撞机数据中的长程、高阶关联，可能以更少参数和更优缩放性优于经典模型。实时对撞机应用（如触发系统）需具备经典模拟与编译量子电路的能力，并将生成的量子门映射到低延迟硬件加速器——现场可编程门阵列（FPGAs）。本文研究了用于现代对撞机实验实时异常检测触发的变分量子自编码器模型。该模型性能接近当前最优经典方法，经FPGA综合后满足未来对撞机触发应用的资源占用与时序约束。本工作提供了对撞机触发中首个量子机器学习模型的FPGA实现，使今日的数据采集流程支持更高能力的量子模型，同时推进对撞机实验基础设施的量子准备度。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。