  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.
