Performance evaluation of coherent Ising machines against classical neural networks
Yoshitaka Haribara, Hitoshi Ishikawa, Shoko Utsunomiya, Kazuyuki Aihara, Yoshihisa Yamamoto · Quantum Science and Technology · 2017
Abstract 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 and a field programmable gate array circuit. The similar mathematical models were proposed three decades ago by Hopfield et al in the context of classical neural networks. In this article, we compare the computational performance of both models.