  The standard model (SM) of particle physics represents a theoretical paradigm
for the description of the fundamental forces of nature. Despite its broad
applicability, the SM does not enable the description of all physically
possible events. The detection of events that cannot be described by the SM,
which are typically referred to as anomalous, and the related potential
discovery of exotic physical phenomena is a non-trivial task. The challenge
becomes even greater with next-generation colliders that will produce even more
events with additional levels of complexity. The additional data complexity
motivates the search for unsupervised anomaly detection methods that do not
require prior knowledge about the underlying models. In this work, we develop
such a technique. More explicitly, we employ a quantum generative adversarial
network to identify anomalous events. The method learns the background
distribution from SM data and, then, determines whether a given event is
characteristic for the learned background distribution. The proposed
quantum-powered anomaly detection strategy is tested on proof-of-principle
examples using numerical simulations and IBM Quantum processors. We find that
the quantum generative techniques using ten times fewer training data samples
can yield comparable accuracy to the classical counterpart for the detection of
the Graviton and Higgs particles. Additionally, we empirically compute the
capacity of the quantum model and observe an improved expressivity compared to
its classical counterpart.
