  Quantum simulators and processors are rapidly improving nowadays, but they
are still not able to solve complex and multidimensional tasks of practical
value. However, certain numerical algorithms inspired by the physics of real
quantum devices prove to be efficient in application to specific problems,
related, for example, to combinatorial optimization. Here we implement a
numerical annealer based on simulating the coherent Ising machine as a tool to
sample from a high-dimensional Boltzmann probability distribution with the
energy functional defined by the classical Ising Hamiltonian. Samples provided
by such a generator are then utilized for the partition function estimation of
this distribution and for the training of a general Boltzmann machine. Our
study opens up a door to practical application of numerical quantum-inspired
annealers.
