Right now David Poulin is speaking about a quantum version of the Hammersley–Clifford theorem, which is a theorem about Markov networks. Let me quickly say a bit about what he proved! This will be a bit rough, since I’m doing it live…

It’s the average amount of information about learned by measuring when you already knew

All this works for both classical (Shannon) and quantum (von Neumann) entropy. So, when we say ‘random variable’ above, we
could mean it in the traditional classical sense or in the quantum sense.

If then has the following Markov property: if you know learning tells you nothing new about In condensed matter physics, say a spin system, we get (quantum) random variables from measuring what’s going on in regions, and we have short range entanglement if when corresponds to some sufficiently thick region separating the regions and We’ll get this in any Gibbs state of a spin chain with a local Hamiltonian.

A Markov network is a graph with random variables at vertices (and thus subsets of vertices) such that whenever is a subset of vertices that completely ‘shields’ the subset from the subset : any path from to goes through a vertex in a

The Hammersley–Clifford theorem says that in the classical case we can get any Markov network from the Gibbs state

of a local Hamiltonian and vice versa. Here a Hamiltonian is local if it is a sum of terms, one depending on the degrees of freedom in each clique in the graph:

Hayden, Jozsa, Petz and Winter gave a quantum generalization of one direction of this result to graphs that are just ‘chains’, like this:

o—o—o—o—o—o—o—o—o—o—o—o

Namely: for such graphs, any quantum Markov network is the Gibbs state of some local Hamiltonian. Now Poulin has shown the same for all graphs. But the converse is, in general, false. If the different terms in a local Hamiltonian all commute, its Gibbs state will have the Markov property. But otherwise, it may not.

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Ah, I grew up just up the hill. :-)