The minimal canonical form of a tensor network

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Tensor networks have a gauge degree of freedom on the virtual degrees of freedom that are contracted. A canonical form is a choice of fixing this degree of freedom. For matrix product states, choosing a canonical form is a powerful tool, both for theoretical and numerical purposes. On the other hand, for tensor networks in dimension two or greater there is only limited understanding of the gauge symmetry. Here we introduce a new canonical form, the minimal canonical form, which applies to projected entangled pair states (PEPS) in any dimension, and prove a corresponding fundamental theorem. Already for matrix product states this gives a new canonical form, while in higher dimensions it is the first rigorous definition of a canonical form valid for any choice of tensor. We show that two tensors have the same minimal canonical forms if and only if they are gauge equivalent up to taking limits; moreover, this is the case if and only if they give the same quantum state for any geometry. In particular, this implies that the latter problem is decidable - in contrast to the well-known undecidability for equality of PEPS on grids. We also provide rigorous algorithms for computing minimal canonical forms. To achieve this we draw on geometric invariant theory and recent progress in theoretical computer science in non-commutative group optimization.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 64th Annual Symposium on Foundations of Computer Science, FOCS 2023
PublisherIEEE
Publication date2023
Pages328-362
ISBN (Electronic)9798350318944
DOIs
Publication statusPublished - 2023
Event64th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2023 - Santa Cruz, United States
Duration: 6 Nov 20239 Nov 2023

Conference

Conference64th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2023
LandUnited States
BySanta Cruz
Periode06/11/202309/11/2023
SponsorIEEE, IEEE Computer Society, IEEE Computer Society Technical Committee on Mathematical Foundations of Computing, NSF

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

    Research areas

  • invariant theory, non-commutative optimization, tensor networks

ID: 380304696