Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree tensor network, we apply a quantum-inspired machine learning technique to a very important and challenging big data problem in high-energy physics: the analysis and classification of data produced by the Large Hadron Collider at CERN. In particular, we present how to effectively classify so-called b-jets, jets originating from b-quarks from proton–proton collisions in the LHCb experiment, and how to interpret the classification results. We exploit the Tensor Network approach to select important features and adapt the network geometry based on information acquired in the learning process. Finally, we show how to adapt the tree tensor network to achieve optimal precision or fast response in time without the need of repeating the learning process. These results pave the way to the implementation of high-frequency real-time applications, a key ingredient needed among others for current and future LHCb event classification able to trigger events at the tens of MHz scale.

张量网络是一种最初设计用于模拟量子多体系统的数值工具，最近已被应用于解决机器学习问题。利用树张量网络，我们将受量子启发的机器学习技术应用于高能物理学中一个非常重要且具有挑战性的大数据问题：欧洲核子研究中心的大型强子对撞机产生的数据的分析和分类。特别是，我们介绍了如何对 LHCb 实验中的质子-质子碰撞产生的 b-夸克产生的喷流进行有效分类，以及如何解释分类结果。我们利用张量网络方法来选择重要特征并根据学习过程中获得的信息调整网络几何形状。最后，我们展示了如何调整树张量网络以在不需要重复学习过程的情况下及时实现最佳精度或快速响应。这些结果为高频实时应用的实施铺平了道路，这是当前和未来 LHCb 事件分类所需的关键要素，能够触发数十 MHz 的事件。