用光纤非线性传播实现极限学习机，准确率超91%。
Limits of nonlinear and dispersive fiber propagation for an optical fiber-based extreme learning machine
- 构建基于光纤传播的非线性薛定谔方程模型模拟ELM
- 异常和正常色散下准确率分别达91%和93%
- 量子噪声会内在降低性能，适合光计算研究者
我们提出了一种基于光学纤维传播的极限学习机（ELM）的广义非线性薛定谔方程模拟模型。以手写数字数据集MNIST为基准，研究了精度如何依赖于传播动力学，以及谱编码、读出和噪声参数的影响。在量子噪声受限输入条件下，异常色散和正常色散区域的测试准确率分别达到91%和93%。结果还表明，输入脉冲上的量子噪声会对ELM性能引入固有损耗。
原文摘要 · Abstract (English)
We report a generalized nonlinear Schrödinger equation simulation model of an extreme learning machine (ELM) based on optical fiber propagation. Using the MNIST handwritten digit dataset as a benchmark, we study how accuracy depends on propagation dynamics, as well as parameters governing spectral encoding, readout, and noise. For this dataset and with quantum noise limited input, test accuracies of : over 91% and 93% are found for propagation in the anomalous and normal dispersion regimes respectively. Our results also suggest that quantum noise on the input pulses introduces an intrinsic penalty to ELM performance.
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