弱克尔非线性提升频分复用光子极限学习机性能：一种多维度研究方法
Weak Kerr Nonlinearity Boosts the Performance of Frequency-Multiplexed Photonic Extreme Learning Machines: A Multifaceted Approach
AI总结：
该研究通过理论、数值与实验结合的方式，揭示弱克尔非线性可通过四波混频效应高效混合信息，显著提升频分复用光子极限学习机的任务性能，且低功率下即可实现该增益。
摘要：
我们从理论、数值模拟和实验三个维度，研究了克尔非线性对频分复用极限学习机（Extreme Learning Machine，ELM）性能的影响。在这类ELM中，神经元信号被编码在频率梳的谱线中，克尔非线性可促成随机化的神经元连接，实现高效的信息混合，系统的输出权重由可编程光谱滤波器施加，且工作在连续波模式下。即便输入峰值功率较低，由此产生的弱克尔非线性也足以显著提升多项任务的性能。仅利用20米长掺铒光纤放大器中存在的极微弱克尔非线性，就能实现这种性能提升。相比之下，信号随后在540米单模光纤中传输仅能略微提升性能，而通过相位调制器进行额外信息混合则完全不会带来进一步的性能提升。我们提出的模型表明，在频分复用ELM中，克尔非线性是通过四波混频而非自相位调制或交叉相位调制来混合信息的；在低功率下，该效应与频率梳谱线振幅呈四次方关系。数值模拟结果验证了我们的实验结论与理论解释。
英文摘要：
We provide a theoretical, numerical, and experimental investigation of the Kerr nonlinearity impact on the performance of a frequency-multiplexed Extreme Learning Machine (ELM). In such ELM, the neuron signals are encoded in the lines of a frequency comb. The Kerr nonlinearity facilitates the randomized neuron connections allowing for efficient information mixing. A programmable spectral filter applies the output weights. The system operates in a continuous-wave regime. Even at low input peak powers, the resulting weak Kerr nonlinearity is sufficient to significantly boost the performance on several tasks. This boost already arises when one uses only the very small Kerr nonlinearity present in a 20-meter long erbium-doped fiber amplifier. In contrast, a subsequent propagation in 540 meters of a single-mode fiber improves the performance only slightly, whereas additional information mixing with a phase modulator does not result in a further improvement at all. We introduce a model to show that, in frequency-multiplexed ELMs, the Kerr nonlinearity mixes information via four-wave mixing, rather than via self- or cross-phase modulation. At low powers, this effect is quartic in the comb-line amplitudes. Numerical simulations validate our experimental results and interpretation.
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