mlsanityData to 7 Oct 2026

Jet Discrimination with Quantum Complete Graph Neural Network

arXiv 2403.04990 ↗First posted 8 Mar 2024hep-ph cs.LG quant-phcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 37 of its 116 references that are in the map. First posted on arXiv 8 Mar 2024.

Builds on

37 of 116 references.

References · 37

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25 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2025QKAN-LSTM: Quantum-inspired Kolmogorov-Arnold Long Short-term Memorycited by 8
  2. 2024From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networkscited by 6
  3. 2026Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Eracited by 0