Coherent Ising Machines with Error Correction Feedback

A nonequilibrium open‐dissipative neural network, such as a coherent Ising machine based on mutually coupled optical parametric oscillators, has been proposed and demonstrated as a novel computing machine for hard combinatorial optimization problems. However, there is a challenge in the previously proposed approach: The machine can be trapped by local minima which increases exponentially with a problem size. This leads to erroneous solutions rather than correct answers. In this paper, it is shown that it is possible to overcome this problem partially by introducing error detection and correction feedback mechanism. The proposed machine achieves efficient sampling of degenerate ground states and low‐energy excited states via its inherent exploration property during a solution search process. Coherent Ising machines (CIMs) with error detection and correction feedback can search for ground states during their exploration without getting trapped in local minima. The success probability of such closed‐loop CIMs is much higher than that of open‐loop CIMs without error correction. The proposed machine also realizes efficient and fair sampling of the degenerate ground and low‐energy excited states.