  Machine learning, particularly deep neural networks, has been widely utilized
in high energy physics and has shown remarkable results in various
applications. Moreover, the concept of machine learning has been extended to
quantum computers, giving rise to a new research area known as quantum machine
learning. In this paper, we propose a novel variational quantum circuit model,
Quantum Complete Graph Neural Network (QCGNN), designed for learning complete
graphs. We argue that QCGNN has a polynomial speedup against its classical
counterpart, due to the property of quantum parallelism. In this paper, we
study the application of QCGNN through the challenging jet discrimination,
where the jets are represented with complete graphs. Subsequently, we conduct a
comparative analysis with classical graph neural networks to establish a
benchmark.
