Tensor network manifolds and Riemannian fundamental theorem for tensor networks
- URL: http://arxiv.org/abs/2606.14613v1
- Date: Fri, 12 Jun 2026 16:39:45 GMT
- Title: Tensor network manifolds and Riemannian fundamental theorem for tensor networks
- Abstract summary: tensor networks provide a powerful framework for representing high-dimensional data and many-body quantum states.<n>A central feature of tensor networks is their gauge freedom, whose characterisation underlies both their intrinsic structure and the design of numerical algorithms.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Tensor networks provide a powerful framework for efficiently representing high-dimensional data and many-body quantum states. Endowing tensor networks with a Riemannian manifold structure provides a natural setting for numerical optimization and analysis. A central feature of tensor networks is their gauge freedom, whose characterisation (captured by so-called fundamental theorems) underlies both their intrinsic structure and the design of numerical algorithms. In this work, we study the interaction between the Riemannian manifold structure and the gauge freedom for several families of tensor networks. Using group actions and Riemannian submersions, we establish a Riemannian fundamental theorem for the tensor network families studied.
Related papers
- Approximation Theory for Neural Networks: Old and New [1.6300219183960216]
Universal approximations provide a mathematical explanation for the expressive power of neural networks.<n>We review classical density results for single-hidden-layer networks, as well as quantitative bounds that relate approximation error to network size.<n>Recent developments on Kolmogorov--Arnold Networks (KANs) offer an alternative architectural paradigm.
arXiv Detail & Related papers (2026-05-20T17:42:34Z) - Pointwise Generalization in Deep Neural Networks [24.565019622696664]
We identify the structural properties and mathematical principles that explain the tractability of deep networks.<n>We show that deep networks are mathematically tractable in practical regimes and that their generalization is sharply explained by pointwise, feature-spectrum-aware complexity.
arXiv Detail & Related papers (2026-05-18T16:09:06Z) - Enhancing lattice kinetic schemes for fluid dynamics with Lattice-Equivariant Neural Networks [79.16635054977068]
We present a new class of equivariant neural networks, dubbed Lattice-Equivariant Neural Networks (LENNs)
Our approach develops within a recently introduced framework aimed at learning neural network-based surrogate models Lattice Boltzmann collision operators.
Our work opens towards practical utilization of machine learning-augmented Lattice Boltzmann CFD in real-world simulations.
arXiv Detail & Related papers (2024-05-22T17:23:15Z) - A singular Riemannian Geometry Approach to Deep Neural Networks III. Piecewise Differentiable Layers and Random Walks on $n$-dimensional Classes [49.32130498861987]
We study the case of non-differentiable activation functions, such as ReLU.
Two recent works introduced a geometric framework to study neural networks.
We illustrate our findings with some numerical experiments on classification of images and thermodynamic problems.
arXiv Detail & Related papers (2024-04-09T08:11:46Z) - Riemannian Residual Neural Networks [58.925132597945634]
We show how to extend the residual neural network (ResNet)
ResNets have become ubiquitous in machine learning due to their beneficial learning properties, excellent empirical results, and easy-to-incorporate nature when building varied neural networks.
arXiv Detail & Related papers (2023-10-16T02:12:32Z) - TensorKrowch: Smooth integration of tensor networks in machine learning [46.0920431279359]
We introduceKrowch, an open source Python library built on top of PyTorch.
Krowch allows users to construct any tensor network, train it, and integrate it as a layer in more intricate deep learning models.
arXiv Detail & Related papers (2023-06-14T15:55:19Z) - Adaptive Log-Euclidean Metrics for SPD Matrix Learning [73.12655932115881]
We propose Adaptive Log-Euclidean Metrics (ALEMs), which extend the widely used Log-Euclidean Metric (LEM)
The experimental and theoretical results demonstrate the merit of the proposed metrics in improving the performance of SPD neural networks.
arXiv Detail & Related papers (2023-03-26T18:31:52Z) - A Practical Guide to the Numerical Implementation of Tensor Networks I:
Contractions, Decompositions and Gauge Freedom [0.0]
We present an overview of the key ideas and skills necessary to begin implementing tensor network methods numerically.
The topics presented are of key importance to many common tensor network algorithms such as DMRG, TEBD, TRG, PEPS and MERA.
arXiv Detail & Related papers (2022-02-04T14:10:09Z) - A singular Riemannian geometry approach to Deep Neural Networks I.
Theoretical foundations [77.86290991564829]
Deep Neural Networks are widely used for solving complex problems in several scientific areas, such as speech recognition, machine translation, image analysis.
We study a particular sequence of maps between manifold, with the last manifold of the sequence equipped with a Riemannian metric.
We investigate the theoretical properties of the maps of such sequence, eventually we focus on the case of maps between implementing neural networks of practical interest.
arXiv Detail & Related papers (2021-12-17T11:43:30Z) - T-Basis: a Compact Representation for Neural Networks [89.86997385827055]
We introduce T-Basis, a concept for a compact representation of a set of tensors, each of an arbitrary shape, which is often seen in Neural Networks.
We evaluate the proposed approach on the task of neural network compression and demonstrate that it reaches high compression rates at acceptable performance drops.
arXiv Detail & Related papers (2020-07-13T19:03:22Z)
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.