Approximating or calculating the mutual information of certain binary random vectors

In my studies of applied probability I have recently met the following problem on which I need help:

We consider two binary random (column) vectors $X,Y \in \{0,1\}^d$ where the mutual distribution satisfies $P(X,Y) = \frac{e^{-X^TWY-b^TX-c^TY}}{Z(W,b,c)}$ where W is a symmetric positive semidefinite matrix, b and c are constant column vectors of length $d$ and $Z$ is the partition function to normalize the probability distribution, this setting might be familiar to some from machine learning and neural networks as a "Restricted Boltzmann Machine" (RBM), I was looking at the mutual information $I(X;Y) = \sum_{X} \sum_{Y} P(X,Y) \ln{(\frac{P(X,Y)}{P(X)P(Y)})}$

I was wondering if we can approximate this quantity (the mutual information) or even calculate it analytically? We may assume the vectors are over {-1,1} if it makes things easier. Maybe an asymptotic approximation would help? I was thinking maybe a probabilistic approximation, maybe approximating the discrete distributions as Gaussian distributions or other distributions easier to deal with? I thank all helpers on this.