I am interested in recognising graphs (or matrices, or subshifts of finite type) using topological pressure. Suppose that we play the following game:
${\bf Step 1:}$ I write down an irreducible n x n matrix A of zeroes and ones, which I ask you to try and work out. I tell you what n is.
Let $\sigma$ be the left shift on $\Sigma=\{1,2,..,n\}^{\mathbb N}$ and $\sigma_A$ be the left shift on $\Sigma_A$, which is the shift space corresponding to incidence matrix A.
${\bf Step 2:}$ You think of a function $f:\Sigma\to\mathbb R$ and ask me what the value of the topological pressure $P_{\sigma_A}(f|_{\Sigma_A})$ is. I tell you the answer. Repeat step 2 until you can tell me what A is.
${\bf Question:}$ How many times do you have to repeat step 2 before you know the matrix A?
It's easy to see that $n^2$ is enough. Define $f_{i,j}(\underline a) = 1$ if $a_1=i$, $a_2=j,$ 0 otherwise. Then $P_{\sigma_A}(f_{ij}|_{\Sigma_A})$ is different from $h_{top}(\sigma_A)$ if and only if $A_{ij}=1$. You don't know what $h_{top}(\sigma_A)$ is, but you can work this out from the topological pressure information.
Can we do any better? I'm particularly interested in the situation where you are only allowed to pick locally constant functions f, but the general case is also interesting. One could make minor improvements to the above idea to reduce $n^2$, but I'm really interested in whether there is a constant k independent of n such that we can always determine an n x n matrix by knowing k pressure functions.