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The coherent Ising machine is expected to find a near-optimal solution invarious combinatorial optimization problems, which has been experimentallyconfirmed with optical parametric oscillators (OPOs) and a field programmablegate array (FPGA) circuit. The similar mathematical models were proposed threedecades ago by J. J. Hopfield, et al. in the context of classical neuralnetworks. In this article, we compare the computational performance of bothmodels.

Performance evaluation of coherent Ising machines against classical neural networks