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Parameterised graph theory for tensor networks: entanglement rerouting, structural simplification, and agnostic tomography

Paper recorded by Signals 4 on 2026-09-03 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-03 on arXiv · recorded by Signals 4 on 2026-09-04

Category: cs.LG · 机器学习 · first seen 2026-09-04

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). Its implications for tensor-network representations and tomography are less well understood. In particular, which graph parameters determine whether a tensor-network s

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#129 most recent of 215 cs.LG papers we have recorded · ↑ newer: Para-Pipe: Exploiting Hierarchical Operator Parallelism of ML Computat · ↓ older: Prospective Coding Improves Learning in Deep Continuous-Time Recurrent
Cite this page: Parameterised graph theory for tensor networks: entanglement rerouting, structural simplification, and agnostic tomography: the #129 most recent of 215 cs.LG papers we have recorded (as of 2026-09-03). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/parameterised-graph-theory-for-tensor-networks-entanglement-rerouting-structural.html
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