面向硬件的张量网络用于粒子对撞机中的实时量子启发异常检测
Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliders
AI总结：
本文提出一种面向硬件的张量网络，用于在粒子对撞机中实现实时量子启发异常检测，展示了空间矩阵积算符（SMPO）在检测超出标准模型物理中的有效性，并探讨了其在资源受限环境中的应用潜力。
摘要：
量子机器学习能够捕捉高维特征空间中的复杂相关性，这对于在对撞机事件中检测超出标准模型物理至关重要，同时未来量子处理器可能带来前所未有的计算效率。通过开发用于在经典硬件上部署的量子启发算法，可以实现当前科学实验的“边缘”应用。本文展示了张量网络在对撞机探测器中用于实时异常检测的应用。开发了一种空间矩阵积算符（SMPO），能够对多种超出标准模型的基准敏感，并可以在现场可编程门阵列硬件中实现，其资源和延迟与触发部署一致。引入了级联SMPO架构作为SMPO的一种变体，提供了在资源受限环境中更高的灵活性和效率。这些结果揭示了在高能对撞机中部署量子启发式机器学习的益处和短期可行性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
