Network of time-multiplexed optical parametric oscillators as a coherent Ising machine

By Alireza Marandi, Zhe Wang, Kenta Takata, Robert L. Byer, Yoshihisa Yamamoto. Nature Photonics, 2014

517 citations recorded by the source corpus.

A network of four degenerate optical parametric oscillators (OPOs) is employed to find the ground state of the Ising Hamiltonian. The good performance of the network reveals the potential of OPOs for many similar problems. Finding the ground states of the Ising Hamiltonian1 maps to various combinatorial optimization problems in biology, medicine, wireless communications, artificial intelligence and social network. So far, no efficient classical and quantum algorithm is known for these problems and intensive research is focused on creating physical systems—Ising machines—capable of finding the absolute or approximate ground states of the Ising Hamiltonian2,3,4,5,6. Here, we report an Ising machine using a network of degenerate optical parametric oscillators (OPOs). Spins are represented with above-threshold binary phases of the OPOs and the Ising couplings are realized by mutual injections7. The network is implemented in a single OPO ring cavity with multiple trains of femtosecond pulses and configurable mutual couplings, and operates at room temperature. We programmed a small non-deterministic polynomial time-hard problem on a 4-OPO Ising machine and in 1,000 runs no computational error was detected.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/932ca8f0d978f00aca739c96d245394dcc28de79

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Citations in the corpus

Large-scale Ising spin network based on degenerate optical parametric oscillators (2016, methodology): “jp demonstrated a CIM using DOPOs [13].”
Realizing the classical XY Hamiltonian in polariton simulators. (2016, unknown): “Another scalable platform that beneﬁts from high temperature operation is the coupled degenerate OPOs Ising Machine, which solves the MAX-CUT [34, 35].”
Large-Scale Optical Neural Networks based on Photoelectric Multiplication (2018, methodology): “This encoding, reminiscent of the Coherent Ising Machine [28, 29, 30], contrasts with other approaches used for neural networks, which encode inputs in separate spatial channels [23, 24, 22].”
The physics of optical computing (2023, background): “For example, lasers and parametric oscillators in optics have a threshold when gain is equal to loss, and the fact that they first oscillate in the mode with lowest loss can be used to design optical Ising machines 106,107 (Fig.”
Topological dissipation in a time-multiplexed photonic resonator network (2021, unknown): “The lengths of the ( N − 1) delay lines are chosen so that each pulse couples to the other ( N − 1) pulses in the network, and the electro-optic modulators (EOMs) in the delay lines determine the strengths and phases of the couplings 13 .”
A fully programmable 100-spin coherent Ising machine with all-to-all connections (2016, background): “Several numerical models have been developed to understand the operation of networks of coupled OPOs (17, 20, 43, 44).”
Exciton-polaritons in lattices: A non-linear photonic simulator (2016, background): “Celebrated examples of simulators include lattices of microwave cavities [16–18], coupled optical parametric oscillators [19] and microlasers [20], and arrays of coupled waveguides arranged both in one and two dimensions [21].”
Ising machines as hardware solvers of combinatorial optimization problems (2022, background): “The classical description of a CIM (5) is sufﬁcient to explain the results obtained in the experimental demonstrations 35,115,116,119,124–126 to date, since these experiments have used DOPOs with fairly large roundtrip (photon) loss.”