A tensor network formulation of Lattice Gauge Theories based only on symmetric tensors
- URL: http://arxiv.org/abs/2412.16961v1
- Date: Sun, 22 Dec 2024 10:42:05 GMT
- Title: A tensor network formulation of Lattice Gauge Theories based only on symmetric tensors
- Authors: Manu Canals, Natalia Chepiga, Luca Tagliacozzo,
- Abstract summary: We provide a new PEPS tensor network formulation of gauge-invariant theories based on symmetric elementary tensors.
The new formulation can be implemented in numerical simulation using available state-of-the-art tensor network libraries.
We show that such a new formulation provides a novel duality transformation between lattice gauge theories and specific sectors of globally invariant systems.
- Score: 0.0
- License:
- Abstract: The Lattice Gauge Theory Hilbert space is divided into gauge-invariant sectors selected by the background charges. Such a projector can be directly embedded in a tensor network ansatz for gauge-invariant states as originally discussed in [Phys. Rev. B 83, 115127 (2011)] and in [Phys. Rev. X 4, 041024 (2014)] in the context of PEPS. The original ansatz is based on sparse tensors, though parts of them are not explicitly symmetric, and thus their actual implementation in numerical simulations has been hindered by the complexity of developing ad hoc libraries. Here we provide a new PEPS tensor network formulation of gauge-invariant theories purely based on symmetric elementary tensors. The new formulation can be implemented in numerical simulation using available state-of-the-art tensor network libraries but also holds interest from a purely theoretical perspective since it requires embedding the original gauge theory with gauge symmetry G into an enlarged globally symmetric theory with symmetry GxG. By revisiting the original ansatz in the modern landscape of i) duality transformations between gauge and spin systems, ii) finite depth quantum circuits followed by measurements that allow generating topologically ordered states, and iii) Clifford enhanced tensor networks, we show that such a new formulation provides a novel duality transformation between lattice gauge theories and specific sectors of globally invariant systems.
Related papers
- Twisted gauging and topological sectors in (2+1)d abelian lattice gauge theories [0.0]
We investigate a duality operation that amounts to gauging its invertible 1-form symmetry, followed by gauging the resulting 0-form symmetry in a twisted way.
We compute the non-trivial interplay between symmetry-twisted boundary conditions and charge sectors under the duality operation.
We argue that this results in a symmetry structure that encodes the 2-representations of a 2-group.
arXiv Detail & Related papers (2025-01-27T18:39:10Z) - 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) - Holographic Codes from Hyperinvariant Tensor Networks [70.31754291849292]
We show that a new class of exact holographic codes, extending the previously proposed hyperinvariant tensor networks into quantum codes, produce the correct boundary correlation functions.
This approach yields a dictionary between logical states in the bulk and the critical renormalization group flow of boundary states.
arXiv Detail & Related papers (2023-04-05T20:28:04Z) - Quantum and classical spin network algorithms for $q$-deformed
Kogut-Susskind gauge theories [0.0]
We introduce $q$-deformed Kogut-Susskind lattice gauge theories, obtained by deforming the defining symmetry algebra to a quantum group.
Our proposal simultaneously provides a controlled regularization of the infinite-dimensional local Hilbert space while preserving essential symmetry-related properties.
Our work gives a new perspective for the application of tensor network methods to high-energy physics.
arXiv Detail & Related papers (2023-04-05T15:49:20Z) - Geometrical aspects of lattice gauge equivariant convolutional neural
networks [0.0]
Lattice gauge equivariant convolutional neural networks (L-CNNs) are a framework for convolutional neural networks that can be applied to non-Abelian lattice gauge theories.
arXiv Detail & Related papers (2023-03-20T20:49:08Z) - Toward random tensor networks and holographic codes in CFT [0.0]
In spherically symmetric states in any dimension and more general states in 2d CFT, this leads to a holographic error-correcting code.
The code is shown to be isometric for light operators outside the horizon, and non-isometric inside.
The transition at the horizon occurs due to a subtle breakdown of the Virasoro identity block approximation in states with a complex interior.
arXiv Detail & Related papers (2023-02-05T18:16:02Z) - Boundary theories of critical matchgate tensor networks [59.433172590351234]
Key aspects of the AdS/CFT correspondence can be captured in terms of tensor network models on hyperbolic lattices.
For tensors fulfilling the matchgate constraint, these have previously been shown to produce disordered boundary states.
We show that these Hamiltonians exhibit multi-scale quasiperiodic symmetries captured by an analytical toy model.
arXiv Detail & Related papers (2021-10-06T18:00:03Z) - Conformal Properties of Hyperinvariant Tensor Networks [0.0]
We analyze the challenges related to optimizing tensors in a hyMERA with respect to some quasiperiodic critical spin chain.
We show two new sets of tensor decompositions which exhibit different properties from the original construction.
We find that the constraints imposed on the spectra of local descending superoperators in hyMERA are compatible with the operator spectra of several minimial model CFTs.
arXiv Detail & Related papers (2020-12-17T14:06:15Z) - 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) - Lorentz Group Equivariant Neural Network for Particle Physics [58.56031187968692]
We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group.
For classification tasks in particle physics, we demonstrate that such an equivariant architecture leads to drastically simpler models that have relatively few learnable parameters.
arXiv Detail & Related papers (2020-06-08T17:54:43Z) - Tensor network models of AdS/qCFT [69.6561021616688]
We introduce the notion of a quasiperiodic conformal field theory (qCFT)
We show that qCFT can be best understood as belonging to a paradigm of discrete holography.
arXiv Detail & Related papers (2020-04-08T18:00:05Z)
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.