Information Graph Flow: A Geometric Approximation of Quantum and Statistical Systems

Vitaly Vanchurin

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Given a quantum (or statistical) system with a very large number of degrees of freedom and a preferred tensor product factorization of the Hilbert space (or of a space of distributions) we describe how it can be approximated with a very low-dimensional field theory with geometric degrees of freedom. The geometric approximation procedure consists of three steps. The first step is to construct weighted graphs (we call information graphs) with vertices representing subsystems (e.g., qubits or random variables) and edges representing mutual information (or the flow of information) between subsystems. The second step is to deform the adjacency matrices of the information graphs to that of a (locally) low-dimensional lattice using the graph flow equations introduced in the paper. (Note that the graph flow produces very sparse adjacency matrices and thus might also be used, for example, in machine learning or network science where the task of graph sparsification is of a central importance.) The third step is to define an emergent metric and to derive an effective description of the metric and possibly other degrees of freedom. To illustrate the procedure we analyze (numerically and analytically) two information graph flows with geometric attractors (towards locally one- and two-dimensional lattices) and metric perturbations obeying a geometric flow equation. Our analysis also suggests a possible approach to (a non-perturbative) quantum gravity in which the geometry (a secondary object) emerges directly from a quantum state (a primary object) due to the flow of the information graphs.

Original languageEnglish (US)
Pages (from-to)636-653
Number of pages18
JournalFoundations of Physics
Issue number6
StatePublished - Jun 1 2018

Bibliographical note

Funding Information:
The author is grateful to Daniel Harlow, Mudit Jain, Mahdiyar Noorbala and Evan Severson for useful discussions and comments on the manuscript. The work was supported in part by Templeton Foundation and Foundational Questions Institute (FQXi).

Publisher Copyright:
© 2018, Springer Science+Business Media, LLC, part of Springer Nature.


  • Foundations of quantum gravity
  • Graph theory
  • Information theory


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