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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Abstract
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.
Featured image: Optical implementation of Spiking Neural Network-Coherent Ising Machine
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Cited by
[1] 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).
[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).
The above citations are from SAO/NASA ADS (last updated successfully 2023-10-26 15:42:55). The list may be incomplete as not all publishers provide suitable and complete citation data.
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This Paper is published in Quantum under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Copyright remains with the original copyright holders such as the authors or their institutions.
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