论文标题
基于峰值神经网络的连贯的ISING机器的组合优化解决
Combinatorial optimization solving by coherent Ising machines based on spiking neural networks
论文作者
论文摘要
尖峰神经网络是一种神经形态计算，据信可以提高智能水平并为量子计算提供优势。在这项工作中，我们通过设计一个光学尖峰神经网络来解决此问题，并发现它可用于加速计算速度，尤其是在组合优化问题上。在这里，尖峰神经网络是由反对耦合的退化光学参数振荡脉冲和耗散脉冲构建的。选择非线性传递函数以减轻幅度不均匀性，并根据尖峰神经元的动态行为破坏所得的局部最小值。从数值上表明，尖峰神经网络协会机器在组合优化问题上具有出色的性能，这有望为神经计算和光学计算提供新的应用。
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.
