From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks
It provides a foundational review for researchers in quantum computing and graph analysis, but it is incremental as it synthesizes existing work without presenting new results.
This paper reviews Quantum Graph Neural Networks (QGNNs), which combine quantum computing and Graph Neural Networks to address computational and scalability challenges in classical GNNs, highlighting their potential for quantum advantage across fields like high-energy physics and molecular chemistry.
Quantum Graph Neural Networks (QGNNs) represent a novel fusion of quantum computing and Graph Neural Networks (GNNs), aimed at overcoming the computational and scalability challenges inherent in classical GNNs that are powerful tools for analyzing data with complex relational structures but suffer from limitations such as high computational complexity and over-smoothing in large-scale applications. Quantum computing, leveraging principles like superposition and entanglement, offers a pathway to enhanced computational capabilities. This paper critically reviews the state-of-the-art in QGNNs, exploring various architectures. We discuss their applications across diverse fields such as high-energy physics, molecular chemistry, finance and earth sciences, highlighting the potential for quantum advantage. Additionally, we address the significant challenges faced by QGNNs, including noise, decoherence, and scalability issues, proposing potential strategies to mitigate these problems. This comprehensive review aims to provide a foundational understanding of QGNNs, fostering further research and development in this promising interdisciplinary field.