  Optical computing often employs tailor-made hardware to implement specific
algorithms, trading generality for improved performance in key aspects like
speed and power efficiency. An important computing approach that is still
missing its corresponding optical hardware is probabilistic computing, used
e.g. for solving difficult combinatorial optimization problems. In this study,
we propose an experimentally viable photonic approach to solve arbitrary
probabilistic computing problems. Our method relies on the insight that
coherent Ising machines composed of coupled and biased optical parametric
oscillators can emulate stochastic logic. We demonstrate the feasibility of our
approach by using numerical simulations equivalent to the full density matrix
formulation of coupled optical parametric oscillators.
