  Machine learning algorithms are heavily relied on to understand the vast
amounts of data from high-energy particle collisions at the CERN Large Hadron
Collider (LHC). The data from such collision events can naturally be
represented with graph structures. Therefore, deep geometric methods, such as
graph neural networks (GNNs), have been leveraged for various data analysis
tasks in high-energy physics. One typical task is jet tagging, where jets are
viewed as point clouds with distinct features and edge connections between
their constituent particles. The increasing size and complexity of the LHC
particle datasets, as well as the computational models used for their analysis,
greatly motivate the development of alternative fast and efficient
computational paradigms such as quantum computation. In addition, to enhance
the validity and robustness of deep networks, one can leverage the fundamental
symmetries present in the data through the use of invariant inputs and
equivariant layers. In this paper, we perform a fair and comprehensive
comparison between classical graph neural networks (GNNs) and equivariant graph
neural networks (EGNNs) and their quantum counterparts: quantum graph neural
networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The
four architectures were benchmarked on a binary classification task to classify
the parton-level particle initiating the jet. Based on their AUC scores, the
quantum networks were shown to outperform the classical networks. However,
seeing the computational advantage of the quantum networks in practice may have
to wait for the further development of quantum technology and its associated
APIs.
