Quantum Anomaly Detection for Collider Physics

Abstract

Quantum Machine Learning (QML) is an exciting tool that has received significant recent attention due in part to advances in quantum computing hardware. While there is currently no formal guarantee that QML is superior to classical ML for relevant problems, there have been many claims of an empirical advantage with high energy physics datasets. These studies typically do not claim an exponential speedup in training, but instead usually focus on an improved performance with limited training data. We explore an analysis that is characterized by a low statistics dataset. In particular, we study an anomaly detection task in the four-lepton final state at the Large Hadron Collider that is limited by a small dataset. We explore the application of QML in a semi-supervised mode to look for new physics without specifying a particular signal model hypothesis. We find no evidence that QML provides any advantage over classical ML. It could be that a case where QML is superior to classical ML for collider physics will be established in the future, but for now, classical ML is a powerful tool that will continue to expand the science of the LHC and beyond.

Long-Lived Particles Anomaly Detection with Parametrized Quantum Circuits

“…We then apply the technique to a more complex and interesting use-case, the identification of anomalous signatures inside a particle detector due to the decay of long-lived particles with macroscopic lifetimes. The application of quantum machine learning to high-energy physics is an interesting field that has been studied using QML simulators in some recent works [11][12][13][14][15][16][17][18][19][20][21]. In this paper, we present the first application of QML to the task of anomaly detection for long-lived particle identification and also prove that the proposed variational quantum circuits could be used on actual quantum hardware.…”

Medium induced jet broadening in a quantum computer

“…Beside field theory based simulations, they have also been applied to specific topics such as nuclear structure [32][33][34][35][36][37], neutrino oscillation [38] and string theory [39]. Concerning collider oriented physics, these technologies have, for example, been used to simulate hard probes like heavy flavors [40] and jets [41,42], optimize parton showers [43][44][45] and jet clustering algorithms [46][47][48] as well as in the detection of quantum anomalies [49] and the study of spin correlations at high energies [50]. Although such applications are still highly constrained by the performance of current quantum computers [51], even the (re)formulation of problems in a language accessible to these machines turns out to be highly non-trivial.…”