Jet Discrimination with Quantum Complete Graph Neural Network (2403.04990v3)
Abstract: 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.
- M. Feickert and B. Nachman, A living review of machine learning for particle physics (2021), arXiv:2102.02770 [hep-ph] .
- K.-F. Chen and Y.-T. Chien, Deep learning jet substructure from two-particle correlations, Phys. Rev. D 101, 114025 (2020).
- P. T. Komiske, E. M. Metodiev, and M. D. Schwartz, Deep learning in color: towards automated quark/gluon jet discrimination, Journal of High Energy Physics 2017, 110 (2017).
- H. Qu and L. Gouskos, Jet tagging via particle clouds, Phys. Rev. D 101, 056019 (2020).
- J. Shlomi, P. Battaglia, and J.-R. Vlimant, Graph neural networks in particle physics, Machine Learning: Science and Technology 2, 021001 (2020).
- A. Zeguendry, Z. Jarir, and M. Quafafou, Quantum machine learning: A review and case studies, Entropy 25, 10.3390/e25020287 (2023).
- D. P. García, J. Cruz-Benito, and F. J. García-Peñalvo, Systematic literature review: Quantum machine learning and its applications (2022), arXiv:2201.04093 [quant-ph] .
- K. A. Tychola, T. Kalampokas, and G. A. Papakostas, Quantum machine learning mdash;an overview, Electronics 12, 10.3390/electronics12112379 (2023).
- M. Schuld and F. Petruccione, Machine Learning with Quantum Computers (2021).
- E. W. Weisstein, ”complete graph.” from mathworld–a wolfram web resource, last visited on 2/11/2023.
- J. Preskill, Quantum Computing in the NISQ era and beyond, Quantum 2, 79 (2018).
- G. E. Crooks, Gradients of parameterized quantum gates using the parameter-shift rule and gate decomposition (2019), arXiv:1905.13311 [quant-ph] .
- M. Cacciari, G. P. Salam, and G. Soyez, The anti-kt jet clustering algorithm, Journal of High Energy Physics 2008, 063 (2008).
- M. Cacciari, G. P. Salam, and G. Soyez, Fastjet user manual, The European Physical Journal C 72, 1896 (2012).
- M. Fey and J. E. Lenssen, Fast graph representation learning with PyTorch Geometric, in ICLR Workshop on Representation Learning on Graphs and Manifolds (2019).
- P. Team, Pytorch.
- M. A. Nielsen and I. L. Chuang, Controlled operations, in Quantum Computation and Quantum Information (Cambridge University Press, 2007) Chap. 4.3 Fig(4.10).