Mode-dependent dynamic gain of all-fiber FM-EDFA under various pump manipulation

The dynamic gain of a few-mode erbium-doped fiber amplifier(FM-EDFA)is vital for the long-haul mode division multiplexing(MDM)transmission.Here,we investigate the mode-dependent dynamic gain of an FM-EDFA under various manipulations of the pump mode.First,we numerically calculate the gain variation with respect to the input signal power,where a modedependent saturation input power occurs under different pump modes.Even under the fixed intensity profile of the pump laser,the saturation input power of each spatial mode is different.Moreover,high-order mode pumping leads to a compression of the linear amplification region,even though it is beneficial for the mitigation of the differential modal gain(DMG)arising in all guided modes.Then,we develop an all-fiber 3-mode EDFA,where the fundamental mode of the pump laser can be efficiently converted to the LP_(11)mode using the all-fiber mode-selective coupler(MSC).In comparison with the traditional LP_(01)pumping scheme,the DMG at 1550 nm can be mitigated from 1.61 dB to 0.97 dB under the LP_(11)mode pumping,while both an average gain of 19.93 dB and a DMG of less than 1 dB can be achieved from 1530 nm to 1560 nm.However,the corresponding signal input saturation powers are reduced by 0.3 dB for the LP_(01)mode and 1.6 dB for the LP_(11)mode,respectively.Both theoretical and experimental results indicate that a trade-off occurs between the DMG mitigation and the extension of the linear amplification range when the intensity profile of pump laser is manipulated.

Scalable parallel ultrafast optical random bit generation based on a single chaotic microcomb

Random bit generators are critical for information security,cryptography,stochastic modeling,and simulations.Speed and scalability are key challenges faced by current physical random bit generation.Herein,we propose a massively parallel scheme for ultrafast random bit generation towards rates of order 100 terabit per second based on a single micro-ring resonator.A modulation-instability-driven chaotic comb in a micro-ring resonator enables the simultaneous generation of hundreds of independent and unbiased random bit streams.A proof-of-concept experiment demonstrates that using our method,random bit streams beyond 2 terabit per second can be successfully generated with only 7 comb lines.This bit rate can be easily enhanced by further increasing the number of comb lines used.Our approach provides a chip-scale solution to random bit generation for secure communication and high-performance computation,and offers superhigh speed and large scalability.

Highly robust spatiotemporal wavefront prediction with a mixed graph neural network in adaptive optics

The time-delay problem,which is introduced by the response time of hardware for correction,is a critical and nonignorable problem of adaptive optics(AO)systems.It will result in significant wavefront correction errors while turbulence changes severely or system responses slowly.Predictive AO is proposed to alleviate the time-delay problem for more accurate and stable corrections in the real time-varying atmosphere.However,the existing prediction approaches either lack the ability to extract non-linear temporal features,or overlook the authenticity of spatial features during prediction,leading to poor robustness in generalization.Here,we propose a mixed graph neural network(MGNN)for spatiotemporal wavefront prediction.The MGNN introduces the Zernike polynomial and takes its inherent covariance matrix as physical constraints.It takes advantage of conventional convolutional layers and graph convolutional layers for temporal feature catch and spatial feature analysis,respectively.In particular,the graph constraints from the covariance matrix and the weight learning of the transformation matrix promote the establishment of a realistic internal spatial pattern from limited data.Furthermore,its prediction accuracy and robustness to varying unknown turbulences,including the generalization from simulation to experiment,are all discussed and verified.In experimental verification,the MGNN trained with simulated data can achieve an approximate effect of that trained with real turbulence.By comparing it with two conventional methods,the demonstrated performance of the proposed method is superior to the conventional AO in terms of root mean square error(RMS).With the prediction of the MGNN,the mean and standard deviation of RMS in the conventional AO are reduced by 54.2%and 58.6%at most,respectively.The stable prediction performance makes it suitable for wavefront predictive correction in astronomical observation,laser communication,and microscopic imaging.