Physics Simulation Via Quantum Graph Neural Network

• URL: http://arxiv.org/abs/2301.04702v1
• Date: Wed, 11 Jan 2023 20:21:10 GMT
• Title: Physics Simulation Via Quantum Graph Neural Network
• Authors: Benjamin Collis, Saahil Patel, Daniel Koch, Massimiliano Cutugno, Laura Wessing, and Paul M. Alsing
• Abstract summary: We develop and implement two realizations of quantum graph neural networks (QGNN) The first QGNN is a speculative quantum-classical hybrid learning model that relies on the ability to directly implement superposition states as classical information. The second is a feasible quantum-classical hybrid learning model that propagates particle information directly through the parameters of $RX$ rotation gates.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We develop and implement two realizations of quantum graph neural networks (QGNN), applied to the task of particle interaction simulation. The first QGNN is a speculative quantum-classical hybrid learning model that relies on the ability to directly implement superposition states as classical information to propagate information between particles, while the second is a feasible quantum-classical hybrid learning model that propagates particle information directly through the parameters of $RX$ rotation gates. A classical graph neural network (CGNN) is also trained in the same task. Both the speculative QGNN and CGNN act as controls against the feasible QGNN. Comparison between classical and quantum models is based on the loss value and the accuracy of each model throughout training. Overall, the performance of each model is highly similar. Each of the three models has a high learning efficiency, in which the loss value rapidly approaches zero during training. Contrarily, the accuracy of each model is poor. In relative terms, the learning efficiency of the feasible QGNN is highest, and it has a greater accuracy than the CGNN during training; however, their measured accuracies become identical when tested on a validation data set. These outcomes suggests that the feasible QGNN has a potential advantage over the CGNN. Additionally, we show that a slight alteration in hyperparameters notably improves accuracy, suggesting that further fine tuning these could mitigate the issue of high inaccuracy.

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