Unravelling physics beyond the standard model with classical and quantum anomaly detection
This work addresses the computational problem of anomaly detection in high-energy physics for researchers, but it is incremental as it builds on existing methods with a novel training approach.
The authors tackled the challenge of detecting new physics anomalies in Large Hadron Collider data by proposing a supervised learning strategy that artificially creates anomalies and uses classical and quantum Support Vector Classifiers, achieving high accuracy in identifying realistic beyond-standard-model events.
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.