Multi-entropy in random tensor networks
- URL: http://arxiv.org/abs/2606.04470v1
- Date: Wed, 03 Jun 2026 05:30:41 GMT
- Title: Multi-entropy in random tensor networks
- Abstract summary: We study the evaluation of Rényi multi-entropies $S(q)_n$ in Random Network (RTN) states in the large bond-dimension limit.<n>For the case of Rényi index $n=2$ and arbitrary number of parties $q$, we prove that multi-entropies are determined by minimal multiway cuts through the network.
- Score: 2.7233225545211375
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We study the evaluation of Rényi multi-entropies $S^{(q)}_n$ in Random Tensor Network (RTN) states in the large bond-dimension limit. For the case of Rényi index $n=2$ and arbitrary number of parties $q$, we prove that that multi-entropies are determined by minimal multiway cuts through the network. When the minimal multiway cut is degenerate, we characterize the full minimizer set via compatible families of minimal cuts and give a criterion for all minimizers to come from ordinary cut partitions. For $n=2$, this gives a natural generalization of the minimal cut description of bipartite entanglement to multipartite systems with arbitrarily many parties. For the case of integer $n>2$, we show that the minimal multiway cut conjecture is in general \emph{not true} by providing explicit counter examples for both the single random tensor and for the network built from isometric tilings. We discuss the implication for our results on the multipartite entanglement structures in RTN and holography.
Related papers
- Local large deviations for linear-region growth in random piecewise-linear networks [0.0]
We study a random compositional model for the growth of affine regions in deep piecewise-linear networks.<n>We prove the existence of a submultiplicative pressure for (N_n), yielding exponential upper bounds for both tails of (n-1log N_n)
arXiv Detail & Related papers (2026-07-08T05:14:19Z) - All you need is log [0.42671348654734564]
We show that the Rényi divergences of order $in[0,infty]$ are the unique family monotone under data processing and additive on independent products.<n>Many problems compare more than two distributions at once -- multi-population fairness, multi-prior PAC-Bayes bounds, multi-hypothesis testing.<n>The right multi-distribution generalization of the Rényi family has been an open question.
arXiv Detail & Related papers (2026-06-25T17:54:49Z) - Canonical partial ordering from min-cuts and quantum entanglement in random tensor networks [5.882601249202242]
We show that the emphfinite correction to entanglement R'enyi entropy arising from the degeneracy of the min-cuts is given by the number of emphorder morphisms<n>We also show that the number of order morphisms corresponds to moments of a graph-dependent measure which generalizes the free Bessel law in some special cases in free probability theory.
arXiv Detail & Related papers (2025-06-30T14:26:40Z) - Reflected entropy in random tensor networks III: triway cuts [0.0]
We prove that the integer R'enyi reflected entropies are determined by minimal triway cuts through the network.
A natural extrapolation away from integer R'enyi parameters, suggested by the triway cut problem, implies the holographic conjecture $S_R=2EW$.
This sheds light on the gap between the existence of tripartite entanglement in holographic states and the bipartite entanglement structure motivated by bit-threads.
arXiv Detail & Related papers (2024-09-25T18:00:00Z) - Constructive Universal Approximation and Finite Sample Memorization by Narrow Deep ReLU Networks [0.0]
We show that any dataset with $N$ distinct points in $mathbbRd$ and $M$ output classes can be exactly classified.<n>We also prove a universal approximation theorem in $Lp(Omega; mathbbRm)$ for any bounded domain.<n>Our results offer a unified and interpretable framework connecting controllability, expressivity, and training dynamics in deep neural networks.
arXiv Detail & Related papers (2024-09-10T14:31:21Z) - Distributed Extra-gradient with Optimal Complexity and Communication
Guarantees [60.571030754252824]
We consider monotone variational inequality (VI) problems in multi-GPU settings where multiple processors/workers/clients have access to local dual vectors.
Extra-gradient, which is a de facto algorithm for monotone VI problems, has not been designed to be communication-efficient.
We propose a quantized generalized extra-gradient (Q-GenX), which is an unbiased and adaptive compression method tailored to solve VIs.
arXiv Detail & Related papers (2023-08-17T21:15:04Z) - The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich
Regimes [75.59720049837459]
We study the transition from infinite-width behavior to this variance limited regime as a function of sample size $P$ and network width $N$.
We find that finite-size effects can become relevant for very small datasets on the order of $P* sim sqrtN$ for regression with ReLU networks.
arXiv Detail & Related papers (2022-12-23T04:48:04Z) - Shallow neural network representation of polynomials [91.3755431537592]
We show that $d$-variables of degreeR$ can be represented on $[0,1]d$ as shallow neural networks of width $d+1+sum_r=2Rbinomr+d-1d-1d-1[binomr+d-1d-1d-1[binomr+d-1d-1d-1[binomr+d-1d-1d-1d-1[binomr+d-1d-1d-1d-1
arXiv Detail & Related papers (2022-08-17T08:14:52Z) - Geometry of the Loss Landscape in Overparameterized Neural Networks:
Symmetries and Invariances [9.390008801320024]
We show that adding one extra neuron to each is sufficient to connect all previously discrete minima into a single manifold.
We show that the number of symmetry-induced critical subspaces dominates the number of affine subspaces forming the global minima manifold.
arXiv Detail & Related papers (2021-05-25T21:19:07Z) - A simple geometric proof for the benefit of depth in ReLU networks [57.815699322370826]
We present a simple proof for the benefit of depth in multi-layer feedforward network with rectified activation ("depth separation")
We present a concrete neural network with linear depth (in $m$) and small constant width ($leq 4$) that classifies the problem with zero error.
arXiv Detail & Related papers (2021-01-18T15:40:27Z) - Average-case Complexity of Teaching Convex Polytopes via Halfspace
Queries [55.28642461328172]
We show that the average-case teaching complexity is $Theta(d)$, which is in sharp contrast to the worst-case teaching complexity of $Theta(n)$.
Our insights allow us to establish a tight bound on the average-case complexity for $phi$-separable dichotomies.
arXiv Detail & Related papers (2020-06-25T19:59:24Z) - Neural Networks are Convex Regularizers: Exact Polynomial-time Convex
Optimization Formulations for Two-layer Networks [70.15611146583068]
We develop exact representations of training two-layer neural networks with rectified linear units (ReLUs)
Our theory utilizes semi-infinite duality and minimum norm regularization.
arXiv Detail & Related papers (2020-02-24T21:32:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.