用张量网络实现实时粒子对撞异常检测，适合边缘部署。
Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliders
- 设计空间矩阵乘积算子，适配硬件资源受限场景。
- 在真实对撞机数据中实现对新物理的敏感检测。
- 架构可部署于现场可编程门阵列，满足触发延迟要求。
量子机器学习能捕捉高维特征空间中的复杂关联，对探测标准模型之外的新物理至关重要，且未来有望在量子处理器上实现前所未有的计算效率。近期可通过开发适用于经典硬件的量子启发算法，在科学实验的'边缘'实现应用。本文展示了利用张量网络进行对撞机探测器中的实时异常检测。提出一种空间矩阵乘积算子（SMPO），对超出标准模型的物理信号具有灵敏度，且可在现场可编程门阵列（FPGA）上实现，资源占用与延迟均符合触发系统部署要求。引入级联式SMPO架构，提升了灵活性与效率，特别适合资源受限环境下的边缘应用。结果表明，量子启发机器学习在高能对撞机中的近中期可行性与优势。
原文摘要 · Abstract (English)
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.
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