A Study on Quantum Graph Neural Networks Applied to Molecular Physics

• URL: http://arxiv.org/abs/2408.03427v1
• Date: Tue, 6 Aug 2024 20:06:48 GMT
• Title: A Study on Quantum Graph Neural Networks Applied to Molecular Physics
• Authors: Simone Piperno, Andrea Ceschini, Su Yeon Chang, Michele Grossi, Sofia Vallecorsa, Massimo Panella,
• Abstract summary: This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer parameters, resulting in an extremely interpretable architecture rooted in the underlying physics of the problem.
• Score: 0.5277756703318045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer parameters, resulting in an extremely interpretable architecture rooted in the underlying physics of the problem. The architectural novelties arise from three pivotal aspects. Firstly, we employ an embedding updating method that is analogous to classical Graph Neural Networks, therefore bridging the classical-quantum gap. Secondly, each layer is devoted to capturing interactions of distinct orders, aligning with the physical properties of the system. Lastly, we harness SWAP gates to emulate the problem's inherent symmetry, a novel strategy not found currently in the literature. The obtained results in the considered experiments are encouraging to lay the foundation for continued research in this field.

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