Livine/Terno Geometry from Information

In summary: The paper proposes that the geometry of a quantum system can be understood by looking at the correlations between its parts. It suggests that this geometry is related to the distance between two regions. This is similar to ideas in quantum field theory and quantum information theory.
  • #1
f-h
272
0
There's no discussion of this recent paper yet as far as I can see:

Pasting from Marcus' Intuitive content thread:

http://arxiv.org/abs/gr-qc/0603008
Reconstructing Quantum Geometry from Quantum Information: Area Renormalisation, Coarse-Graining and Entanglement on Spin Networks
Etera R. Livine, Daniel R. Terno
27 pages, 12 figures

"After a brief review of spin networks and their interpretation as wave functions for the (space) geometry, we discuss the renormalisation of the area operator in loop quantum gravity. In such a background independent framework, we propose to probe the structure of a surface through the analysis of the coarse-graining and renormalisation flow(s) of its area. We further introduce a procedure to coarse-grain spin network states and we quantitatively study the decrease in the number of degrees of freedom during this process. Finally, we use these coarse-graining tools to define the correlation and entanglement between parts of a spin network and discuss their potential interpretation as a natural measure of distance in such a state of quantum geometry."


This touches on many issues we discussed in the Baez thread. Careful, Kea, anyone else, any comments?
 
Physics news on Phys.org
  • #2
Why do I like this paper. First of all I find the often implicit assumption in LQG that the elementary excitations labeled by spin-networks carry a completely unambiguous geometrical interpretation questionable.

By proposing a way how the geometric concepts arise from the pregeometric information (I hope this word isn't overloaded to much?) in the spin networks this point is clarified, and the precise additional information/assumptions needed to get geometry becomes visible.

In doing so it might become clear what additional structures next to geometric ones, might arise.

Finally a notion of coarse graining will be imperative to find potential (semi-)classical behaviours in Spin Networks.
 
  • #3
f-h said:
There's no discussion of this recent paper yet as far as I can see:

Pasting from Marcus' Intuitive content thread:

http://arxiv.org/abs/gr-qc/0603008
Reconstructing Quantum Geometry from Quantum Information: Area Renormalisation, Coarse-Graining and Entanglement on Spin Networks
Etera R. Livine, Daniel R. Terno
27 pages, 12 figures
...

Good initiative F-H
BTW the second author Daniel Terno was a student of the late Asher Peres. Terno has co-authored several papers IIRC with Peres. Yesterday and today I was citing the "Einstein Podolsky Rosen and SHANNON" paper of Peres. Rosen was Peres' PhD thesis advisor at princeton. Shannon founded Information Theory, Peres helped to establish Quantum information theory. Terno just recently moved from Haifa Technion to Perimeter, where he has collaborated on several things with Livine including this. This sketch of connections is meant to help indicate where Terno is coming from.
 
Last edited:
  • #4
f-h said:
...By proposing a way how the geometric concepts arise from the pregeometric information (I hope this word isn't overloaded to much?) in the spin networks this point is clarified,..

To make this a little bit more specific, here is a sample exerpt from page 23 of the Livine Terno paper

----quote Livine Terno---
Our goal is to understand the (quantum) metric defined by a spin network state, without referring to any assumed embedding of the spin network in a (background) manifold.

We support the basic proposal that a natural notion of distance between two vertices (or more generally two regions) of that spin network is provided by the correlations between the two vertices induced by the algebraic structure of the spin network state.

Two parts of the spin network would be close if they are strongly correlated and would get far from each other as the correlations weaken. Our set-up is as follows. We consider two (small) regions, A and B, of the spin network. The distance between them should be given by the (quantum) metric outside these two regions. Thus we define the correlations (and entanglement) between A and B induced by the rest of the spin network. This should be naturally related to the (geodesic) distance between A and B.

A first inspiration is quantum field theory on a fixed background. Considering a (scalar) field phi for example, the correlation phi(x)phi(y) between two points x and y in the vacuum state depends (only) on the distance d(x, y) and actually decreases as 1/d(x, y)^2 in the flat four-dimensional Minkowski space-time. Reversing the logic, one could measure the correlation phi(x)phi(y) between the value of a certain field phi at two different space-time points and define the distance in term of that correlation.

Indeed just as the correlations in QFT contain all the information about the theory and describes the dynamics of the matter degrees of freedom, we expect in a quantum gravity theory that the correlations contained in a quantum state to fully describe the geometry of the quantum space-time defined by that state. Another inspiration is the study of spin systems, in condensed matter physics and quantum information [14, 15, 21]. Such spin systems are very close mathematically and physically to the spin networks of LQG...
---endquote---

I have bolded some words and broken their text into bite-size paragraphs so I can understand it easier
 
Last edited:
  • #5
this is a long shot.

do the ideas in this paper have any connection with Garrett's (audaciously speculative:smile: )new paper

this Livine Terno, which F-H flagged, wrestles with the majorly undefined notion of "coarse-graining"

Garrett's paper confronts the idea of "action"

the most necessary ideas that nobody can do work without are always the most undefinable, it seems---and so, in exasperation, one ventures out on limbs

I'm game, and will venture slightly: "action" is what washes away when you coarse-grain. Naaaah!

anyway, read Garrett's new paper. it is far-out, but I think interesting

https://www.physicsforums.com/showthread.php?t=119029
 
  • #6
marcus said:
I'm game, and will venture slightly: "action" is what washes away when you coarse-grain. Naaaah!
In the ongoing search to deduce physics from logical principles, we have that the action integral is minimized along the geodesic. This hints at a geometrical reason for physical properties. But why a geodesic? Now it may be that the least information of a system accurs along the geodesic. Other paths would be less probable and provide too much information.

How's that for going out on a limb?
 
  • #7
Mike2 said:
... Now it may be that the least information of a system accurs along the geodesic. Other paths would be less probable and provide too much information.

How's that for going out on a limb?

It is good for going out on a limb. Personally I like what you are saying.

I am not sure one can deduce fundamental descriptions of nature from logical principles but I don't want to say anything about that general question---it is offtopic. If it HEURISTICALLY works for you to pursue that line then that is fine.
 
  • #8
Mike2 said:
In the ongoing search to deduce physics from logical principles, we have that the action integral is minimized along the geodesic. This hints at a geometrical reason for physical properties. But why a geodesic? Now it may be that the least information of a system accurs along the geodesic. Other paths would be less probable and provide too much information.

How's that for going out on a limb?

Could you do a Feynman (or a Huygens:smile: ) from this and have the too-much info paths cancel out, leaving only the geodesic? Might require complex information, or anyway some kind of phase so you could have out-of-phase cancellation. So the real info would also be the minimum.
 

1. What is Livine/Terno Geometry from Information?

Livine/Terno Geometry from Information is a mathematical framework that combines the principles of Livine/Terno geometry and information theory to study networks and information flow in complex systems. It involves using Livine/Terno geometry to represent the structure of a network and applying information theory to analyze the flow of information within the network.

2. How does Livine/Terno Geometry from Information differ from traditional geometry?

Livine/Terno Geometry from Information differs from traditional geometry in that it focuses on the relationships and interactions between elements in a network rather than just the physical properties of those elements. It also incorporates the concept of information and how it flows through the network.

3. What are the practical applications of Livine/Terno Geometry from Information?

Livine/Terno Geometry from Information has a wide range of practical applications, including analyzing communication networks, studying social networks, understanding the dynamics of ecosystems, and designing efficient transportation networks. It also has applications in fields such as computer science, physics, and biology.

4. How does Livine/Terno Geometry from Information contribute to our understanding of complex systems?

Livine/Terno Geometry from Information provides a powerful tool for studying and understanding complex systems. It allows us to analyze the structure and dynamics of networks, identify critical nodes and pathways, and predict how changes in the network can affect the flow of information. This can help us better understand the behavior and functioning of complex systems such as the human brain, social networks, and ecosystems.

5. Are there any limitations to using Livine/Terno Geometry from Information?

While Livine/Terno Geometry from Information is a valuable framework for studying complex systems, it does have some limitations. It may not be suitable for all types of networks, and the accuracy of the analysis depends on the quality of the data being used. Additionally, the complexity of the mathematical models involved may make it challenging to apply in certain situations.

Similar threads

  • Beyond the Standard Models
Replies
7
Views
3K
  • Beyond the Standard Models
Replies
1
Views
2K
  • Beyond the Standard Models
Replies
1
Views
2K
  • Beyond the Standard Models
3
Replies
96
Views
17K
  • Beyond the Standard Models
Replies
9
Views
3K
  • Beyond the Standard Models
Replies
12
Views
4K
  • Beyond the Standard Models
Replies
8
Views
4K
  • Poll
  • Beyond the Standard Models
Replies
14
Views
9K
Replies
41
Views
2K
  • Beyond the Standard Models
Replies
5
Views
2K
Back
Top