Can Nonlinear Parametric Oscillators Solve Random Ising Models?

Marcello Calvanese Strinati, Leon Bello, Emanuele G. Dalla Torre, Avi Pe'er

Research output: Contribution to journalArticlepeer-review


We study large networks of parametric oscillators as heuristic solvers of random Ising models. In these networks, known as coherent Ising machines, the model to be 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.

Original languageEnglish
Article number143901
Number of pages9
JournalPhysical Review Letters
Issue number14
StatePublished - 9 Apr 2021

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)


Dive into the research topics of 'Can Nonlinear Parametric Oscillators Solve Random Ising Models?'. Together they form a unique fingerprint.

Cite this