English(EN) Parameterised graph theory for tensor networks: entanglement rerouting, structural simplification, and agnostic tomography

参数化图论应用于张量网络进行模拟和学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-04 04:00

研究人员将参数化图论应用于张量网络，探讨图参数如何影响模拟和学习这些网络的复杂性。研究表明，割宽和树割宽可以限制将张量网络状态表示为矩阵乘积状态或树张量网络的开销。此外，还推导出了张量网络状态断层扫描的样本和计算复杂性的新图相关界限，将现有的学习算法扩展到更复杂的网络结构。 AI

影响这项研究可能导致更有效的方法来模拟和学习复杂的量子态，并可能影响未来利用张量网络原理的AI架构。

排序理由该集群包含一篇详细介绍物理学和计算机科学专业领域新理论研究的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.LG TIER_1 English(EN) · Matthias C. Caro, Natalie McHugh, Sergii Strelchuk · 2026-09-04 04:00

参数化图论用于张量网络：纠缠重路由、结构简化和无偏断层扫描

arXiv:2609.04165v1 Announce Type: cross Abstract: Parameterised graph theory studies how the complexity of graph-theoretic problems depends on structural parameters of the input graph. This perspective has proved useful in analysing tensor-network simulation (Markov and Shi, 2008…