Combinatorial optimization solving by coherent Ising machines based on spiking neural networks

Bo Lu1, Yong-Pan Gao2, Kai Wen3, and Chuan Wang1

1School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
2School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
3Beijing QBoson Quantum Technology Co., Ltd., Beijing 100015, China

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Spiking neural network is a kind of neuromorphic computing that is believed to improve the level of intelligence and provide advantages for quantum computing. In this work, we address this issue by designing an optical spiking neural network and find that it can be used to accelerate the speed of computation, especially on combinatorial optimization problems. Here the spiking neural network is constructed by the antisymmetrically coupled degenerate optical parametric oscillator pulses and dissipative pulses. A nonlinear transfer function is chosen to mitigate amplitude inhomogeneities and destabilize the resulting local minima according to the dynamical behavior of spiking neurons. It is numerically shown that the spiking neural network-coherent Ising machines have excellent performance on combinatorial optimization problems, which is expected to offer new applications for neural computing and optical computing.

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Cited by

[1] Lu Liu, Xing-Yu Wu, Chu-Yao Xu, Lu-Fan Zhang, and Chuan Wang, "The deterministic pattern matching based on the parameterized quantum circuit", EPJ Quantum Technology 11 1, 3 (2024).

[2] Bo Lu, Lu Liu, Jun-Yang Song, Kai Wen, and Chuan Wang, "Recent progress on coherent computation based on quantum squeezing", Association of Asia Pacific Physical Societies Bulletin 33 1, 7 (2023).

[3] Bo Lu, Chen-Rui Fan, Lu Liu, Kai Wen, and Chuan Wang, "Speed-up coherent Ising machine with a spiking neural network", Optics Express 31 3, 3676 (2023).

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