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English(EN) Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy

新的量子图神经网络框架有望实现可扩展性和表达能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的消息传递量子图神经网络 (QGNN) 框架，该框架旨在实现可扩展性和表达能力。这种新的 QGNN 具有排列等变性，并且可以精确地定位在 Weisfeiler-Leman 层级中，这是图区分的标准度量。该框架通过引入预训练策略解决了变分量子电路中常见的可训练性问题，并通过多达 56 个量子比特的大规模模拟进行了验证。 AI

影响 这项研究可能为在分子性质预测和优化问题等领域开发更有效的量子算法铺平道路。

排序理由 该集群包含一篇研究论文，详细介绍了量子图神经网络的新理论框架和模拟结果。

在 arXiv cs.LG 阅读 →

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新的量子图神经网络框架有望实现可扩展性和表达能力

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该集群包含一篇研究论文，详细介绍了量子图神经网络的新理论框架和模拟结果。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Snehal Raj, Brian Coyle, L\'eo Monbroussou, Andr\'e J. Ferreira-Martins, Renato M. S. Farias, Elham Kashefi ·

    可扩展的消息传递量子图神经网络在 Weisfeiler-Leman 层次结构中的应用

    arXiv:2606.26873v1 Announce Type: cross Abstract: Graphs provide a natural language for relational data in chemistry, biology and optimisation. Graph neural networks (GNNs) have driven much of the recent progress in learning from such data through message passing, a single primit…

  2. arXiv cs.LG TIER_1 English(EN) · Elham Kashefi ·

    可扩展的消息传递量子图神经网络在 Weisfeiler-Leman 层级中

    Graphs provide a natural language for relational data in chemistry, biology and optimisation. Graph neural networks (GNNs) have driven much of the recent progress in learning from such data through message passing, a single primitive that generalises convolution and attention. Qu…

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