[1] JORDAN M I, MITCHELL T M. Machine learning: trends, perspectives, and prospects[J]. Science, 349, 255-260(2015).

[2] ZIBAR D, WYMEERSCH H, LYUBOMIRSKY I. Machine learning under the spotlight[J]. Nature Photonics, 11, 749-751(2017).

[3] KIM M, CHEN C, WANG P et al. Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning[J]. Nature Biomedical Engineering, 6, 267-275(2022).

[4] GROJEAN C, PAUL A, QIAN Z et al. Lessons on interpretable machine learning from particle physics[J]. Nature Reviews Physics, 4, 284-286(2022).

[5] THIYAGALINGAM J, SHANKAR M, FOX G et al. Scientific machine learning benchmarks[J]. Nature Reviews Physics, 4, 413-420(2022).

[6] LUAN Haitao, CHEN Xi, ZHANG Qiming等. Artificial intelligence nanophotonics: optical neural networksand nanophotonics[J]. Acta Optica Sinica, 41, 0823005(2021).

[7] SIBBETT W, LAGATSKY A A, BROWN C T A. Te development and application of femtosecond laser systems[J]. Optics Express, 20, 6989-7001(2012).

[8] ZHOU J, HUANG B, YAN Z et al. Emerging role of machine learning in light-matter interaction[J]. Light: Science & Applications, 8, 84(2019).

[9] SUGIOKA K, CHENG Y. Ultrafast lasers reliable tools for advanced materials processing[J]. Light: Science & Applications, 3, e149(2014).

[10] FERMANN M E, HARTL I. Ultrafast fibre lasers[J]. Nature Photonics, 7, 868-874(2013).

[11] SU Yikai, YI Yasha, WANG Xingjun. Special focus on photonics in AI[J]. Scientia Sinica Informationis, 50, 935-936(2020).

[12] KUPRIKOV E, KOKHANOVSKIY A, SEREBRENNIKOV K et al. Deep reinforcement learning for self-tuning laser source of dissipative solitons[J]. Scientific Reports, 12, 7185(2022).

[13] SANCHEZ G A, MICAELLI P, OLIVIER C et al. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning[J]. Nature Communications, 8, 15461(2017).

[14] KEYES R W. Optical logic-in the light of computer technology[J]. Optica Acta: International Journal of Optics, 32, 525-535(1985).

[15] KUTZ J N, BRUNTON S L. Intelligent systems for stabilizing mode-locked lasers and frequency combs: machine learning and equation-free control paradigms for self-tuning optics[J]. Nanophotonics, 4, 459-471(2015).

[16] XU Shaofu, ZOU Xiuting, MA Bowen et al. Deep-learning-powered photonic analog-to-digital conversion[J]. Light: Science & Applications, 8, 66(2019).

[17] ZIBAR D, PIELS M, JONES R et al. Machine learning techniques in optical communication[J]. Journal of Lightwave Technology, 34, 1442-1452(2016).

[18] KNOX W H. Ultrafast technology in telecommunications[J]. IEEE Journal of Selected Topics in Quantum Electronics, 6, 1273-1278(2000).

[19] MEROLLA P A, JOHN V A, RODRIGO A I et al. A million spiking-neuron integrated circuit with a scalable communication network and interface[J]. Science, 345, 668-673(2014).

[20] FAN Qirui, ZHOU Gai, GUI Tao et al. Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning[J]. Nature Communications, 11, 3694(2020).

[21] THEAN D G L, CHU H Y, FONG J H C et al. Machine learning-coupled combinatorial mutagenesis enables resource-efficient engineering of CRISPR-Cas9 genome editor activities[J]. Nature Communications, 13, 2219(2022).

[22] NADELL C C, HUANG B, MALOF J M et al. Deep learning for accelerated all-dielectric metasurface design[J]. Optics Express, 27, 27523-27535(2019).

[23] MALKIEL I, MICHAEL M, ACHIYA N et al. Plasmonic nanostructure design and characterization via deep learning[J]. Light: Science & Applications, 7, 60(2018).

[24] HEGD R S. Deep learning: a new tool for photonic nanostructure design[J]. Nanoscale Advances, 2, 1007-1023(2020).

[25] CHEN C L, ATA M, LI C T et al. Deep learning in label-free cell classifcation[J]. Scientific Reports, 6, 21471(2016).

[26] PALMIERI A M, EGOR K, FEDERICO B et al. Experimental neural network enhanced quantum tomography[J]. Quantum Information, 6, 20(2020).

[27] BAMISILE O, CAI D, OLUWASANMI A et al. Comprehensive assessment, review, and comparison of AI models for solar irradiance prediction based on different time/estimation intervals[J]. Scientific Reports, 12, 9644(2022).

[28] OUYANG W, ARISTOV A, LELEK M et al. Deep learning massively accelerates super-resolution localization microscopy[J]. Nature Biotechnology, 36, 460-468(2018).

[29] DURAND A, WIESNER T, GARDNER M A et al. A machine learning approach for online automated optimization of super-resolution optical microscopy[J]. Nature Communications, 9, 5247(2018).

[30] LUGNAN A, KATUMBAL A, LAPOORTE F et al. Photonic neuromorphic information processing and reservoir computing[J]. APL Photonics, 5, 020901(2020).

[31] BLUVSTEIN D, LEVINE H, SEMEGHINI G et al. A quantum processor based on coherent transport of entangled atom arrays[J]. Nature, 604, 451-456(2022).

[32] ZHAO Yang, GUO Penglai, LI Xiaohui et al. Ultrafast photonics application of graphdiyne in optical communication region[J]. Carbon, 149, 336-341(2019).

[33] LIU Jishu, LI Xiaohui, GUO Yixuan et al. SnSe₂ nanosheets for femtosecond harmonic mode-locked pulse generation[J]. Small, 15, 1902811(2019).

[34] MATSAS V, NEWSON T, ZERVAS M. Self-starting passively mode-locked fiber ring soliton laser exploiting nonlinear polarization rotation[J]. Jelectronics Letters, 28, 1391-1393(1992).

[35] RYCZKOWSKI P, NARHI M, BILLET C et al. Real-time full-feld characterization of transient dissipative soliton dynamics in a mode-locked laser[J]. Nature Photonics, 12, 221-227(2018).

[36] WETZEL B, MICHAEL K, PIOTR R et al. Customizing supercontinuum generation via on-chip adaptive temporal pulse-splitting[J]. Nature Communications, 9, 4884(2018).

[37] CUI Chaohan, ZHANG Liang, FAN Linran. In situ control of effective Kerr nonlinearity with Pockels integrated photonics[J]. Nature Physics, 18, 497-501(2022).

[38] LIANG Yusheng, ZHU Zhimin, QIAO Shuqian et al. Migrating photon avalanche in different emitters at the nanoscale enables 46th-order optical nonlinearity[J]. Nature Nanotechnology, 17, 524-530(2022).

[39] TANG Yuxiang, ZHANG Yanbin, LIU Qirui et al. Interacting plexcitons for designed ultrafast optical nonlinearity in a monolayer semiconductor[J]. Light: Science & Applications, 11, 94(2022).

[40] HUANG C, FUJISAWA S, DE L T F et al. A silicon photonic-electronic neural network for fibre nonlinearity compensation[J]. Nature Electronics, 4, 837-844(2021).

[41] LI Guixin, CHEN Shumei, PHOLCHAI N et al. Continuous control of the nonlinearity phase for harmonic generations[J]. Nature Materials, 14, 607-612(2015).

[42] WANG K, SEIDEL M, NAGARAJAN K et al. Large optical nonlinearity enhancement under electronic strong coupling[J]. Nature Communications, 12, 1486(2021).

[43] ZUO Yonggang, YU Wentao, LIU Can et al. Optical fibres with embedded two-dimensional materials for ultrahigh nonlinearity[J]. Nature Nanotechnology, 15, 987-991(2020).

[44] KOKHANOVSKIY A, IVANENKO A, KOBTSEV S et al. Machine learning methods for control of fibre lasers with double gain nonlinear loop mirror[J]. Scientific Reports, 9, 2916(2019).

[45] EELTINK D, BRANGER H, LUNEAU C et al. Nonlinear wave evolution with data-driven breaking[J]. Nature Communications, 13, 2343(2022).

[46] ZAHAVY T, ALEX D, DANIEL M et al. Deep learning reconstruction of ultrashort pulses[J]. Optica, 5, 666-673(2018).

[47] OLIVIER M, GAGNON M D, PICHE M. Automated mode locking in nonlinear polarization rotation fiber lasers by detection of a discontinuous jump in the polarization state[J]. Optics Express, 23, 6738-6746(2015).

[48] GENTY G, SALMELA L, DUDLEY J M et al. Machine learning and applications in ultrafast photonics[J]. Nature Photonics, 15, 91-101(2021).

[49] YI Lilin, ZHANG Li, PU Guoqing. Research progress of automatic mode-locking fiber laser[J]. ZTE Communications, 2, 200240(2020).

[50] HELLWIG T, WALBAUM T, GROSS P et al. Automated characterization and alignment of passively mode-locked fiber lasers based on nonlinear polarization rotation[J]. Applied Physics B, 101, 565-570(2010).

[51] SHEN Xuling, LI Wenxue, YAN Minget al. Electronic control of nonlinear-polarization-rotation mode locking in Yb-doped fiber lasers[J]. Optics Letters, 37, 3426-3428(2012).

[52] LI Sha, XU Jun, CHEN Guoliang et al. An automatic mode-locked system for passively mode-locked fiber laser[C], 9043(2013).

[53] YI Bo, JIA Wen, XU Jun et al. Automatic mode lock circuit used in NPR passive mode locked fiber laser[J]. Optics and Precision Engineering, 21, 2994-3000(2014).

[54] ANDRAL U, FODIL R S, AMRANI F et al. Fiber laser mode locked through an evolutionary algorithm[J]. Optica, 2, 275-278(2015).

[55] SHEN Xuling, HAO Qiang, ZENG Heping. Self-tuning mode-locked fiber lasers based on prior collection of polarization settings[J]. IEEE Photonics Technology Letters, 29, 1719-1722(2017).

[56] PU Guoqing, YI Lilin, ZHANG Li et al. Programmable and fast-switchable passively harmonic mode-locking fiber laser[C], W2A.9(2018).

[57] WOODWARD R I, KELLEHER E J R. Towards 'smart lasers': self-optimisation of an ultrafast pulse source using a genetic algorithm[J]. Scientific Reports, 6, 37616(2016).

[58] WINTERS D G, KIRCHNER M S, BACKUS S J et al. Electronic initiation and optimization of nonlinear polarization evolution mode-locking in a fiber laser[J]. Optics Express, 25, 33216-33225(2017).

[59] RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial diferential equations[J]. Journal of Computational Physics, 378, 686-707(2019).

[60] MENG C, THRANE P C V, DING F et al. Full-range birefringence control with piezoelectric MEMS-based metasurfaces[J]. Nature Communications, 13, 2071(2022).

[61] FU X, BRUNTON S L, KUTZ J N. Classifcation of birefringence in mode-locked fiber lasers using machine learning and sparse representation[J]. Optics Express, 22, 8585-8597(2014).

[62] PU Guoqing, YI Lilin, ZHANG Li et al. Intelligent programmable mode-locked fiber laser with a human-like algorithm[J]. Optica, 6, 362-369(2019).

[63] PU Guoqing, YI Lilin, ZHANG Li et al. Genetic algorithm-based fast real-time automatic mode-locked fiber laser[J]. IEEE Photonics Technology Letters, 32, 7-10(2020).

[64] WU Xiuqi, PENG Junsong, BOSCOLO S et al. Intelligent breathing soliton generation in ultrafast fiber lasers[J]. Laser Photonics Reviews, 16, 2100191(2021).

[65] ZAHAVY T, DIKOPOLTSEV A, MOSS D et al. Deep learning reconstruction of ultrashort pulses[J]. Optica, 5, 666-673(2018).

[66] BRUNTON S L, PROCTOR J L, KUTZ J N. Discovering governing equations from data by sparse identifcation of nonlinear dynamical systems[J]. Proceedings of the national academy of sciences of the united states of america, 113, 3932-3937(2016).

[67] SALMELA L, TSIPINAKIS N, FOI A et al. Predicting ultrafast nonlinear dynamics in fbre optics with a recurrent neural network[J]. Nature Machine Intelligence, 3, 344-354(2021).

[68] GOODFELLOW I, BENGIO Y, COURVILLE A[M]. Deep Learning(2016).

[69] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 521, 436-444(2015).

[70] PRUCNAL P R, SHASTRI B J. Neuromorphic Photonics[M]. CRC(2017).

[71] FERREIRA D L T, PENG Hsuan Tung, TAIT A N et al. Machine learning with neuromorphic photonics[J]. Journal Of Lightwave Technology, 37, 1515-1534(2019).

[72] HUANG Chaoran, FUJISAWA S, LIMA T F et al. Demonstration of photonic neural network for fiber nonlinearity compensation in long-haul transmission systems[C](2020).

[73] BANGARI V, MARQUEZ B A, MILLER H et al. Digital electronics and analog photonics for convolutional neural networks (DEAP-CNNs)[J]. IEEE Journal of Selected Topics in Quantum Electronics, 26, 7701213(2020).

[74] MEHRABIAN A, MISCUGLIO M, AIKABANI Y et al. A Winograd-based integrated photonics accelerator for convolutional neural networks[J]. IEEE Journal of Selected Topics in Quantum Electronics, 26, 6100312(2020).

[75] YAN Qiuquan, DENG Qinghui, ZHANG Jun et al. Low-latency deep-reinforcement learning algorithm for ultrafast fiber lasers[J]. Photonics Research, 9, 8, 1493(2021).

[76] BAUMEISTER T, BRUNTON S L, KUTZ J N. Deep learning and model predictive control for self-tuning mode-locked lasers[J]. Journal of the Optical Society of America B, 35, 617-626(2018).

[77] WALKER N, TAM K M, JARRELL M. Deep learning on the 2-dimensional Ising model to extract the crossover region with a variational autoencoder[J]. Scientific Reports, 10, 13047(2020).

[78] TERNES L, DANE M, GROSS S et al. A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis[J]. Communications Biology, 5, 255(2022).

[79] LI H, GUAN Y. Asymmetric predictive relationships across histone modifications[J]. Nature Machine Intelligence, 4, 288-299(2022).

[80] GARCIA C E, PRETT D M, MORARI M. Model predictive control: theory and practice-a survey[J]. Automatica, 25, 335-348(1989).

[81] ALVARO P, MARCO H, OSWALDO M. Intelligent Swing-Up and Robust Stabilization via Tube-based Nonlinear Model Predictive Control for A Rotational Inverted-Pendulum System[J]. Revista Politécnica, 45, 49-64(2020).

[82] ROBERTSON J, HEJDA M, BUENO J et al. Ultrafast optical integration and pattern classifcation for neuromorphic photonics based on spiking VCSEL neurons[J]. Scientific Reports, 10, 6098(2020).

[83] LARGER L, SORIANO M C, BRUNNER D et al. Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing[J]. Optics Express, 20, 3241-3249(2012).

[84] PENG H, ANGELATOS G, LIMA T F et al. Temporal information processing with an integrated laser neuron[J]. IEEE Journal of Selected Topics in Quantum Electronics, 26, 5100209(2020).

[85] VANDOORNE K, MECHET P, VAERENBERGH T V et al. Experimental demonstration of reservoir computing on a silicon photonics chip[J]. Nature Communications, 5, 3541(2014).

[86] BERGGREN K, XIA Qiangfei, LIKHAREV K K et al. Roadmap on emerging hardware and technology for machine learning[J]. Nanotechnology, 32, 012002(2021).

[87] KIM H, BOSE R, SHEN T. A quantum logic gate between a solid-state quantum bit and a photon[J]. Nature Photonics, 7, 373-377(2013).

[88] CHOI J, LEE C, LEE C et al. Vertically stacked, low-voltage organic ternary logic circuits including nonvolatile floating-gate memory transistors[J]. Nature Communications, 13, 2305(2022).

[89] CHONG F, FRANKLIN D, MARTONOSI M. Programming languages and compiler design for realistic quantum hardware[J]. Nature, 549, 180-187(2017).

[90] MA Lixia, LEI Xing, YAN Jieli et al. High-performance cavity-enhanced quantum memory with warm atomic cell[J]. Nature Communications, 13, 2368(2022).

[91] ZHUO Shuyun, SONG Cheng, RONG Qinfeng et al. Shape and stiffness memory ionogels with programmable pressure-resistance response[J]. Nature Communications, 13, 1743(2022).

[92] AMBROGIO S, NARAYANAN P, TSAI H et al. Equivalent-accuracy accelerated neural-network training using analogue memory[J]. Nature, 558, 60-67(2018).

[93] HUNG J M, XUE C X, KAO H Y et al. A four-megabit compute-in-memory macro with eight-bit precision based on CMOS and resistive random-access memory for AI edge devices[J]. Nature Electronics, 4, 921-930(2021).

[94] HARRIS N C, JACQUES C, DARIUS B et al. Linear programmable nanophotonic processors[J]. Optica, 5, 1623-1631(2018).

[95] PEREZ D, GASULLA I, MAHAPATRA P D et al. Principles, fundamentals, and applications of programmable integrated photonics[J]. Advances in Optics and Photonics, 12, 709-786(2020).

[96] MENG J, MISCUGLIO M, GEORGE J K et al. Electronic bottleneck suppression in next-generation networks with integrated photonic digital-to-analog converters[J]. Advanced Photonics Research, 2, 2000033(2021).

[97] POWELL K, LI L, SHAMS-ANSARI A et al. Integrated silicon carbide electro-optic modulator[J]. Nature Communications, 13, 1851(2022).

[98] LIN Zhongjin, LIN Yanmei, LI Hao et al. High-performance polarization management devices based on thin-film lithium niobate[J]. Light: Science & Applications, 11, 93(2022).

[99] TURNER E H. High-frequency electro-optic coefcients of lithium niobate[J]. Applied Physics Letters, 8, 303-304(1966).

[100] WANG Cheng, ZHANG Mian, CHEN Xi et al. Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages[J]. Nature, 562, 101-104(2018).

[101] POP F, HERRERA B, RINALDI M. Lithium Niobate Piezoelectric Micromachined Ultrasonic Transducers for high data-rate intrabody communication[J]. Nature Communications, 13, 1782(2022).

[102] XU Mengyue, HE Mingbo, ZHANG Hongguang et al. High-performance coherent optical modulators based on thin-film lithium niobate platform[J]. Nature Communications, 11, 3911(2020).

[103] XIONG Zuoren, MA Xinyan, PEI Yanbo et al. Surface plasmon induced spot and line formation at interfaces of ITO coated LiNbO₃ slabs and gigantic nonlinearity[J]. Scientific Reports, 11, 19790(2021).

[104] KOKHANOVSKIY A, BEDNYAKOVA A, KUPRIKOV E et al. Machine learning-based pulse characterization in fgure-eight mode-locked lasers[J]. Optics Letters, 44, 3410-3413(2019).

[105] PU Guoqing, YI Lilin, ZHANG Li et al. Intelligent control of mode-locked femtosecond pulses by time-stretch-assisted real-time spectral analysis[J]. Light: Science & Applications, 9, 13(2020).

[106] BRILES T C, YOST D C, CINGOZ A et al. Simple piezoelectric-actuated mirror with 180 kHz servo bandwidth[J]. Optics Express, 18, 9739-9746(2010).

[107] LI Xiaohui, PENG Jiajun, LIU Ruisheng et al. Fe₃O₄ nanoparticle-enabled mode-locking in an erbium-doped fiber laser[J]. Frontiers of Optoelectronics, 13, 149-155(2020).

[108] SHASTRI B J, NAHMIAS M A, TAIT A N et al. Spike processing with a graphene excitable laser[J]. Scientific Reports, 6, 19126(2016).

[109] LI Xiaohui, GUO Yixuan, REN Yujie et al. Narrow-bandgap materials for optoelectronics applications[J]. Frontiers of Physics, 17, 13304(2022).

[110] HUDSON D D, HOLMAN K W, JONES R J et al. Mode-locked fiber laser frequency-controlled with an intracavity electro-optic modulator[J]. Optics Letters, 30, 2948-2950(2005).

[111] LARGER L, SORIANO M C, BRUNNER D et al. Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing[J]. Optics Express, 20, 3241-3249(2012).

[112] VANDOORNE K, MECHET P, VAERENBERGH T V et al. Experimental demonstration of reservoir computing on a silicon photonics chip[J]. Nature Communications, 5, 3541(2014).

[113] BRUNNER D, SORIANO M C, DER SANDE G. V. Photonic reservoir computing[M]. De Gruyter(2019).

[114] MENG J, MISCUGLIO M, GEORGE J K et al. Electronic bottleneck suppression in next-generation networks with integrated photonic digital-to-analog converters[J]. Advanced Photonics Research, 2, 2000033(2021).

[115] BUCKLEY S, CHILES J, MCCAUGHAN A N et al. All-silicon light-emitting diodes waveguide-integrated with superconducting single-photon detectors[J]. Applied Physics Letters, 111, 141101(2017).

[116] DUDLEY J M, GENTY G, MUSSOT A et al. Rogue waves and analogies in optics and oceanography[J]. Nature Reviews Physics, 1, 675-689(2019).

[117] KWON D, JEONG D, JEON I et al. Ultrastable microwave and soliton-pulse generation from fibre-photonic-stabilized microcombs[J]. Nature Communications, 13, 381(2022).

[118] YU Mengjie, JANG Ke, OKAWACHI Y et al. Breather soliton dynamics in microresonators[J]. Nature Communications, 8, 14569(2017).

[119] MENG Fanchao, LAPRE C, BILLET C et al. Intracavity incoherent supercontinuum dynamics and rogue waves in a broadband dissipative soliton laser[J]. Nature Communications, 12, 5567(2021).

[120] BRUNTON S L, FU X, KUTZ J N. Self-tuning fiber lasers[J]. IEEE Journal of Selected Topics in Quantum Electronics, 20, 464-471(2014).

[121] BRUNTON S L, FU X, KUTZ J N. Extremum-seeking control of a mode-locked laser[J]. IEEE Journal of Selected Topics in Quantum Electronics, 49, 852-861(2013).

[122] ANDRAL U, BUGUET J, FODIL R S et al. Toward an autosetting mode-locked fiber laser cavity[J]. Journal of The Optical Society of America B-optical Physics, 33, 825-833(2016).

[123] WOODWARD R, KELLEHER E. Genetic algorithm-based control of birefringent fltering for self-tuning, self-pulsing fiber lasers[J]. Optics Letters, 42, 2952-2955(2017).

[124] WINTERS D G, KIRCHNER M S, BACKUS S J et al. Electronic initiation and optimization of nonlinear polarization evolution mode-locking in a fiber laser[J]. Optics Express, 25, 33216-33225(2017).

[125] FU X, KUTZ N J. High-energy mode-locked fiber lasers using multiple transmission filters and a genetic algorithm[J]. Optics Express, 21, 6526-6537(2013).

[126] RYSER M, BACHER C, LATT C et al. Self-optimizing additive pulse mode-locked fiber laser: wavelength tuning and selective operation in continuous-wave or mode-locked regime[J]. Fiber Lasers XV: Technology Systems, 10512, 1(2018).

[127] WU Hsiaohua, HUANG Pinhan, TENG YuanHe et al. Automatic generation of noise-like or mode-locked pulses in an Ytterbiumdoped fiber laser by using two-photon-induced current for feedback[J]. IEEE Photonics Journal, 10, 1-8(2018).

[128] RADNATAROV D, KHRIPUNOV S, KOBTSEV S et al. Automatic electronic-controlled mode locking self-start in fiber lasers with non-linear polarization evolution[J]. Optics Express, 21, 20626-20631(2013).

[129] MA Wei, LIU Zhaocheng, KUDYSHEV Z A et al. Deep learning for the design of photonic structures[J]. Nature Photonics, 15, 77-90(2021).

[130] SHASTRI B J, TAIT A N, FERREIRA T et al. Photonics for artificial intelligence and neuromorphic computing[J]. Nature Photonics, 15, 102-114(2021).