发表机构
SLAC National Accelerator Laboratory（SLAC国家加速器实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究用于对撞机实验实时异常检测的量子自动编码器，通过经典模拟编译和FPGA合成实现其模型，性能可媲美经典方法，满足资源和时序约束，是首批QML模型的FPGA实现，推动对撞机实验基础设施的量子就绪。
AI 中文摘要
高能物理（HEP）中的量子机器学习（QML）算法可以有效地表示和利用高维对撞机数据中的长程、高阶相关性，相对于经典模型可能具有更少的参数和良好的扩展性。在诸如触发系统等实时对撞机应用中部署QML需要能够经典地模拟和编译量子电路，然后将生成的量子门合成到低延迟硬件加速器，即现场可编程门阵列（FPGA）上。我们对用于现代对撞机实验实时异常检测触发的变分量子自动编码器模型进行了研究。这些模型实现了与最先进的经典方法相当的性能，并且在FPGA合成后，满足了与未来对撞机触发应用一致的资源使用和时序约束。这项工作提供了首批用于HEP触发的QML模型的FPGA实现之一，在当今的经典数据采集管道中实现了更高能力的模型，同时推进了对撞机实验基础设施的量子就绪状态。
英文摘要
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.
Comments15 pages, 7 figures, 2 tables
