研究人员开发了一种新颖的图神经网络（GNN）方法，用于优化具有可重构智能表面的多基站系统的频谱和能源效率。另外，一种受AlphaGo启发并利用GNN的新型魔术信息量子架构搜索技术，旨在控制量子资源以改进电路设计。 AI
影响 这些论文探讨了GNN在专业领域的创新应用，可能影响未来通信效率和量子计算的研究。
排序理由 arXiv上发表的两篇不同的研究论文，一篇关于用于通信系统的GNN，另一篇关于用于量子架构搜索的GNN。
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arXiv:2605.03932v1 Announce Type: cross Abstract: Nonstabilizerness, commonly referred to as magic, is a fundamental resource underpinning quantum advantage. In this paper, we propose a magic-informed quantum architecture search (QAS) technique that enables control over a quantum…
arXiv:2605.01307v1 Announce Type: cross Abstract: This paper investigates coordinated downlink transmission in a multi-base station (multi-BS) multi-reconfigurable intelligent surface (multi-RIS)-assisted pinching-antenna (PA) system, where each user equipment (UE) is associated …
Nonstabilizerness, commonly referred to as magic, is a fundamental resource underpinning quantum advantage. In this paper, we propose a magic-informed quantum architecture search (QAS) technique that enables control over a quantum resource within the general framework of circuit …