The coherent Ising machine is expected to find a near-optimal solution in various combinatorial optimization problems, which has been experimentally confirmed with optical parametric oscillators (OPOs) and a field programmable gate array (FPGA) circuit. The similar mathematical models were proposed three decades ago by J. J. Hopfield, et al. in the context of classical neural networks. In this article, we compare the computational performance of both models.
@article{arxiv.1706.01283,
title = {Performance evaluation of coherent Ising machines against classical neural networks},
author = {Yoshitaka Haribara and Hitoshi Ishikawa and Shoko Utsunomiya and Kazuyuki Aihara and Yoshihisa Yamamoto},
journal= {arXiv preprint arXiv:1706.01283},
year = {2017}
}