Jet Discrimination with Quantum Complete Graph Neural Network
This work addresses a domain-specific problem in high-energy physics with incremental improvements in speed for jet discrimination tasks.
The paper tackles jet discrimination in high-energy physics by proposing a Quantum Complete Graph Neural Network (QCGNN) that offers polynomial speedup over classical and quantum models, with performance benchmarks established through comparative analysis.
Machine learning, particularly deep neural networks, has been widely used in high-energy physics, demonstrating remarkable results in various applications. Furthermore, the extension of machine learning to quantum computers has given rise to the emerging field of quantum machine learning. In this paper, we propose the Quantum Complete Graph Neural Network (QCGNN), which is a variational quantum algorithm based model designed for learning on complete graphs. QCGNN with deep parametrized operators offers a polynomial speedup over its classical and quantum counterparts, leveraging the property of quantum parallelism. We investigate the application of QCGNN with the challenging task of jet discrimination, where the jets are represented as complete graphs. Additionally, we conduct a comparative analysis with classical models to establish a performance benchmark.