The ongoing quest to discover new phenomena at the LHC necessitates the continuous development of algorithms and technologies. Established approaches like machine learning, along with emerging technologies such as quantum computing show promise in the enhancement of experimental capabilities. In this work, we propose a strategy for anomaly detection tasks at the LHC based on unsupervised quantum machine learning, and demonstrate its effectiveness in identifying new phenomena. The designed quantum models, an unsupervised kernel machine and two clustering algorithms, are trained to detect new-physics events using a latent representation of LHC data, generated by an autoencoder designed to accommodate current quantum hardware limitations on problem size. For kernel-based anomaly detection, we implement an instance of the model on a quantum computer, and we identify a regime where it significantly outperforms its classical counterparts. We show that the observed performance enhancement is related to the quantum resources utilised by the model.

翻译：在大型强子对撞机（LHC）上持续探索新物理现象的需求，推动着算法与技术的不断发展。成熟的机器学习方法以及量子计算等新兴技术，在提升实验能力方面展现出巨大潜力。本研究提出一种基于无监督量子机器学习的LHC异常检测策略，并验证了其在识别新物理现象方面的有效性。所设计的量子模型——一种无监督核机器与两种聚类算法——通过训练，能够利用LHC数据的隐空间表示来检测新物理事件；该隐表示由专门设计的自编码器生成，以适应当前量子硬件在问题规模上的限制。对于基于核的异常检测，我们在量子计算机上实现了该模型的一个实例，并发现其在特定参数区间内显著优于经典对应模型。我们证明，观测到的性能提升与模型所利用的量子资源密切相关。