Can Nonlinear Parametric Oscillators Solve Random Ising Models?

We study large networks of parametric oscillators as heuristic solvers of random Ising models. In these networks, known as coherent Bing machines, the model to he solved is encoded in the coupling between the oscillators, and a solution is offered by the steady state of the network. This approach relies on the assumption that mode competition steers the network to the ground-state solution of the Ising model. By considering a broad family of frustrated Ising models, we show that the most efficient mode does not correspond generically to the ground state of the Ising model. We infer that networks of parametric oscillators close to threshold are intrinsically not Ising solvers. Nevertheless, the network can find the correct solution if the oscillators are driven sufficiently above threshold, in a regime where nonlinearities play a predominant role. We find that for all probed instances of the model, the network converges to the ground state of the Ising model with a finite probability.

Automated summary

The study found that networks of parametric oscillators are not inherently Ising solvers, even near threshold. However, if the oscillators are sufficiently driven into a regime where nonlinearities play a predominant role, the network can find the correct solution.