Critical points of the random cluster model with Newman-Ziff sampling

We present a method for computing transition points of the random cluster model using generalization Newman-Ziff algorithm, celebrated technique in numerical percolation, to model. The new is straightforward implement and works real weight $q>0$. Furthermore, results an arbitrary number values $q$ can be found at once within single simulation. Because algorithm used sweep through bond configurations identical that Newman Ziff, which was conceived loses accuracy large lattices when $q>1$. However, by sampling critical polynomial, accurate estimates two dimensions relatively small lattice sizes, we demonstrate here non-integer on square lattice, compare with exact solution, unsolved non-planar matching lattice. latter would much more difficult obtain other techniques.