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.

翻译：量子机器学习能够捕捉高维特征空间中复杂的相关性，这对探测对撞机事件中超越标准模型物理的挑战至关重要，同时具备在未来量子处理器中实现前所未有的计算效率的潜力。通过在经典硬件中部署量子启发式算法，可在当前科学实验的"边缘"实现这些优势的近期应用。本研究展示了张量网络在对撞机探测器实时异常检测中的应用。开发了一种间隔矩阵乘积算子（SMPO），该算子能够对多种超越标准模型基准保持敏感性，并可在资源与延迟均符合触发部署要求的现场可编程门阵列硬件中实现。此外，引入级联式SMPO架构作为SMPO的一种变体，其以关键方式提供了更高的灵活性与效率，特别适用于资源受限环境下的边缘应用。这些结果揭示了量子启发式机器学习在高能对撞机中的应用优势及其近期可行性。