Performance evaluation of coherent Ising machines against classical neural networks

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 (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.

arXiv comment11 pages, 4 figures, submitted to IOP-QST

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Cited by in corpus (11)

• Nanophotonic spin-glass for realization of a coherent Ising machine
• Quantum Computation Based on Quantum Adiabatic Bifurcations of Kerr-Nonlinear Parametric Oscillators
• Annealing by simulating the coherent Ising machine
• Rapid Phase Retrieval by Lasing
• All-optical scalable spatial coherent Ising machine
• Analog Errors in Ising Machines
• Equation Planting: A Tool for Benchmarking Ising Machines
• Quantum Correlations in the Kerr Ising Model
• Entanglement-assisted variational algorithm for discrete optimization problems
• Direct comparison of stochastic driven nonlinear dynamical systems for combinatorial optimization
• Steady state distributions of network of degenerate optical parametric oscillators in solving combinatorial optimization problems