Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC). However, current experiments do not indicate clear signs of new physics that could guide the development of additional Beyond Standard Model (BSM) theories. Identifying signatures of new physics out of the enormous amount of data produced at the LHC falls into the class of anomaly detection and constitutes one of the greatest computational challenges. In this article, we propose a novel strategy to perform anomaly detection in a supervised learning setting, based on the artificial creation of anomalies through a random process. For the resulting supervised learning problem, we successfully apply classical and quantum Support Vector Classifiers (CSVC and QSVC respectively) to identify the artificial anomalies among the SM events. Even more promising, we find that employing an SVC trained to identify the artificial anomalies, it is possible to identify realistic BSM events with high accuracy. In parallel, we also explore the potential of quantum algorithms for improving the classification accuracy and provide plausible conditions for the best exploitation of this novel computational paradigm.

翻译：在微观尺度发现新物理现象的希望，很大程度上依赖于高能物理实验（如大型强子对撞机LHC）获得的观测数据。然而，当前实验并未显示出能指导发展更多超越标准模型（BSM）理论的新物理明确迹象。从LHC产生的海量数据中识别出新物理的迹象属于异常检测范畴，构成了最大的计算挑战之一。本文提出了一种基于随机过程人工生成异常数据的监督学习框架下的新型异常检测策略。针对由此产生的监督学习问题，我们成功应用经典支持向量分类器（CSVC）和量子支持向量分类器（QSVC）来识别标准模型事件中的人工异常。更令人振奋的是，我们发现使用针对人工异常训练的支持向量分类器，能够高精度地识别真实的BSM事件。与此同时，我们还探索了量子算法在提升分类精度方面的潜力，并为最优利用这一新型计算范式提供了可行的条件。