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A Tutorial on Quantum Graph Recurrent Neural Network (QGRNN)

机译：量子图经常性神经网络（QGRNN）的教程

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摘要

Over the past decades, various neural networks have been proposed with the rapid development of the machine learning field. In particular, graph neural networks using feature-vectors assigned to nodes and edges have been attracting attention in various fields. The usefulness of graph neural networks also affected the field of quantum computing, which led to the birth of quantum graph neural networks composed of parameterized quantum circuits. The quantum graph neural networks have many possibilities as applications from the simulation perspective of quantum dynamics. Among the application models of various quantum graph neural networks, the quantum graph recurrent neural network (QGRNN) is proven to be effective in training the Ising model Hamiltonian. Thus, this paper introduces the concepts of the Ising model, variational quantum eigensolver (VQE) for preparing quantum data, and QGRNN from a software engineer's point of view.
机译：在过去的几十年中，已经提出了各种神经网络，并通过机器学习领域的快速发展提出了各种神经网络。特别是，使用分配给节点和边缘的特征向量的图形神经网络已经引起各种领域的注意。图形神经网络的有用性也影响了量子计算领域，其导致了由参数化量子电路组成的量子图神经网络的诞生。量子图神经网络具有来自量子动态的模拟视角的应用程序。在各种量子图神经网络的应用模型中，经过证明量子图经常性神经网络（QGRNN）在培训汉密尔顿时期有效。因此，本文介绍了用于准备量子数据的Ising模型，变形量子Eigensolver（VQE）的概念，以及从软件工程师的角度来看QGRNN。

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