GA Ansatz Optimization on a QAE for HEP Anomaly Detection

The Standard Model (SM) of High Energy Physics (HEP) has proven itself to be one of the most accurate scientific theories ever formulated. It is no surprise that current HEP research progresses slowly to find the correct Beyond Standard Model (BSM) extension. While experiments like the Large Hadron Collider (LHC) can directly probe exotic phenomena, such experiments produce an overwhelming amount of data. Therefore, it becomes imperative to develop techniques to easily sift through the background, SM processes and identify the new, BSM physics. Furthermore, because the BSM deviations could be caused by any number of theoretical BSM processes, current HEP anomaly detection should be as model-agnostic as possible, while remaining highly-sensitive. We propose to further develop anomaly detection algorithms using breakthrough quantum machine learning techniques. To go beyond fixed-ansatz quantum circuits models, we propose a meta-optimization of ansatz using a Genetic Algorithm (GA). The models and optimization are benchmarked on the LHC Olympics 2020 dataset, an already well adopted dataset when it comes to evaluating anomaly detection performance