OSA Publishing > OSA Continuum > Volume 2 > Issue 12 > Page 3438
Takashige Omatsu, Editor-in-Chief
Dispersion characterization and pulse prediction with machine learning
Sanjaya Lohani, Erin M. Knutson, Wenlei Zhang, and Ryan T. Glasser
Sanjaya Lohani, Erin M. Knutson, Wenlei Zhang, and Ryan T. Glasser*
Tulane University, New Orleans, LA 70118, USA
*Corresponding author: rglasser@tulane.edu
Sanjaya Lohani https://orcid.org/0000-0003-0699-0669
Erin M. Knutson https://orcid.org/0000-0002-2637-7688
Wenlei Zhang https://orcid.org/0000-0001-5126-4135
S Lohani
E Knutson
W Zhang
R Glasser
•https://doi.org/10.1364/OSAC.2.003438
Sanjaya Lohani, Erin M. Knutson, Wenlei Zhang, and Ryan T. Glasser, "Dispersion characterization and pulse prediction with machine learning," OSA Continuum 2, 3438-3445 (2019)
Nonlinear Optics
Electrooptical modulators
Light matter interactions
Quantum communications
Ti:sapphire lasers
Original Manuscript: June 19, 2019
Revised Manuscript: November 7, 2019
Manuscript Accepted: November 8, 2019
In this work, we demonstrate the efficacy of neural networks in the characterization of dispersive media. We also develop a neural network to make predictions for input probe pulses that propagate through a nonlinear dispersive medium, which may be applied to predicting optimal pulse shapes for a desired output. The setup requires only a single pulse for the probe, providing considerable simplification of the current method of dispersion characterization that requires frequency scanning across the entirety of the gain and absorption features. We show that the trained networks are able to predict pulse profiles as well as dispersive features that are nearly identical to their experimental counterparts. We anticipate that the use of machine learning in conjunction with optical communication and sensing methods, both classical and quantum, can provide signal enhancement and experimental simplifications even in the face of highly complex, layered non-linear light-matter interactions.
Machine learning-based pulse characterization in figure-eight mode-locked lasers
Alexey Kokhanovskiy, Anastasia Bednyakova, Evgeny Kuprikov, Aleksey Ivanenko, Mikhail Dyatlov, Daniil Lotkov, Sergey Kobtsev, and Sergey Turitsyn
Machine-Learning Method for Quality of Transmission Prediction of Unestablished Lightpaths
Cristina Rottondi, Luca Barletta, Alessandro Giusti, and Massimo Tornatore
J. Opt. Commun. Netw. 10(2) A286-A297 (2018)
Integrating machine learning techniques in quantum communication to characterize the quantum channel
Yaseera Ismail, Ilya Sinayskiy, and Francesco Petruccione
J. Opt. Soc. Am. B 36(3) B116-B121 (2019)
Machine Learning Models for Estimating Quality of Transmission in DWDM Networks
Rui Manuel Morais and João Pedro
J. Opt. Commun. Netw. 10(10) D84-D99 (2018)
Feature-based phase retrieval wavefront sensing approach using machine learning
Guohao Ju, Xin Qi, Hongcai Ma, and Changxiang Yan
V. Boyer, A. M. Marino, R. C. Pooser, and P. D. Lett, "Entangled Images from Four-Wave Mixing," Science 321(5888), 544–547 (2008).
J. Shi, G. Patera, M. I. Kolobov, and S. Han, "Quantum temporal imaging by four-wave mixing," Opt. Lett. 42(16), 3121–3124 (2017).
C. Thiel, "Four-wave mixing and its applications," Fac. Washington, Wash. DC (2008).
Y. Cai, J. Feng, H. Wang, G. Ferrini, X. Xu, J. Jing, and N. Treps, "Quantum-network generation based on four-wave mixing," Phys. Rev. A 91(1), 013843 (2015).
C. F. McCormick, V. Boyer, E. Arimondo, and P. D. Lett, "Strong relative intensity squeezing by four-wave mixing in rubidium vapor," Opt. Lett. 32(2), 178–180 (2007).
L. Wang, S. Lv, and J. Jing, "Quantum steering in cascaded four-wave mixing processes," Opt. Express 25(15), 17457 (2017).
W. Diao, C. Cai, W. Yang, X. Song, and C. Duan, "Theoretical Aspects of Continuous Variables Quantum Teleportation Based on Phase-Sensitive Four-Wave Mixing," Int. J. Theor. Phys. 58(1), 323–331 (2019).
F. Hudelist, J. Kong, C. Liu, J. Jing, Z. Y. Ou, and W. Zhang, "Quantum metrology with parametric amplifier-based photon correlation interferometers," Nat. Commun. 5(1), 3049 (2014).
A. MacRae, T. Brannan, R. Achal, and A. I. Lvovsky, "Tomography of a High-Purity Narrowband Photon from a Transient Atomic Collective Excitation," Phys. Rev. Lett. 109(3), 033601 (2012).
R. M. Camacho, P. K. Vudyasetu, and J. C. Howell, "Four-wave-mixing stopped light in hot atomic rubidium vapour," Nat. Photonics 3(2), 103–106 (2009).
N. Corzo, A. M. Marino, K. M. Jones, and P. D. Lett, "Multi-spatial-mode single-beam quadrature squeezed states of light from four-wave mixing in hot rubidium vapor," Opt. Express 19(22), 21358 (2011).
M. Turnbull, "Multi-spatial-mode quadrature squeezing from four-wave mixing in a hot atomic vapour," d_ph, University of Birmingham (2014).
L. Cao, J. Du, J. Feng, Z. Qin, A. M. Marino, M. I. Kolobov, and J. Jing, "Experimental observation of quantum correlations in four-wave mixing with a conical pump," Opt. Lett. 42(7), 1201–1204 (2017).
O. Danaci, C. Rios, and R. T. Glasser, "All-optical mode conversion via spatially multimode four-wave mixing," New J. Phys. 18(7), 073032 (2016).
J. D. Swaim, E. M. Knutson, O. Danaci, and R. T. Glasser, "Multi-mode four-wave mixing with a spatially-structured pump," arXiv:1802.03412 [physics, physics:quant-ph] (2018).
Z. Qin, L. Cao, H. Wang, A. M. Marino, W. Zhang, and J. Jing, "Experimental Generation of Multiple Quantum Correlated Beams from Hot Rubidium Vapor," Phys. Rev. Lett. 113(2), 023602 (2014).
Z. Qin, L. Cao, and J. Jing, "Experimental characterization of quantum correlated triple beams generated by cascaded four-wave mixing processes," Appl. Phys. Lett. 106(21), 211104 (2015).
H. Wang, C. Fabre, and J. Jing, "Single-step fabrication of scalable multimode quantum resources using four-wave mixing with a spatially structured pump," Phys. Rev. A 95(5), 051802 (2017).
S. Liu, H. Wang, and J. Jing, "Two-beam pumped cascaded four-wave-mixing process for producing multiple-beam quantum correlation," Phys. Rev. A 97(4), 043846 (2018).
E. M. Knutson, J. D. Swaim, S. Wyllie, and R. T. Glasser, "Optimal mode configuration for multiple phase-matched four-wave-mixing processes," Phys. Rev. A 98(1), 013828 (2018).
S. Lohani and R. T. Glasser, "Turbulence correction with artificial neural networks," Opt. Lett. 43(11), 2611–2614 (2018).
J. Lv, Z. Na, X. Liu, and Z. Deng, "Machine Learning and Its Applications in Wireless Communications," in Communications, Signal Processing, and Systems, Q. Liang, J. Mu, M. Jia, W. Wang, X. Feng, and B. Zhang, eds. (Springer, Singapore, 2019), Lecture Notes in Electrical Engineering, pp. 2429–2436.
R. C. Deo, "Machine Learning in Medicine," Circulation 132(20), 1920–1930 (2015).
A. Karpatne, I. Ebert-Uphoff, S. Ravela, H. A. Babaie, and V. Kumar, "Machine Learning for the Geosciences: Challenges and Opportunities," IEEE Trans. Knowl. Data Eng. 31(8), 1544–1554 (2019).
K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, and A. Walsh, "Machine learning for molecular and materials science," Nature 559(7715), 547–555 (2018).
C. Hegde and K. E. Gray, "Use of machine learning and data analytics to increase drilling efficiency for nearby wells," J. Nat. Gas Sci. Eng. 40, 327–335 (2017).
M.-A. T. Vu, T. Adalı, D. Ba, G. Buzsáki, D. Carlson, K. Heller, C. Liston, C. Rudin, V. S. Sohal, A. S. Widge, H. S. Mayberg, G. Sapiro, and K. Dzirasa, "A Shared Vision for Machine Learning in Neuroscience," J. Neurosci. 38(7), 1601–1607 (2018).
A. D. Tranter, H. J. Slatyer, M. R. Hush, A. C. Leung, J. L. Everett, K. V. Paul, P. Vernaz-Gris, P. K. Lam, B. C. Buchler, and G. T. Campbell, "Multiparameter optimisation of a magneto-optical trap using deep learning," Nat. Commun. 9(1), 4360 (2018).
T. Zahavy, A. Dikopoltsev, D. Moss, G. I. Haham, O. Cohen, S. Mannor, and M. Segev, "Deep learning reconstruction of ultrashort pulses," Optica 5(5), 666–673 (2018).
W. Huang, Y. Mao, C. Xie, and D. Huang, "Quantum hacking of free-space continuous-variable quantum key distribution by using a machine-learning technique," Phys. Rev. A 100(1), 012316 (2019).
G. R. Steinbrecher, J. P. Olson, D. Englund, and J. Carolan, "Quantum optical neural networks," npj Quantum Inf. 5(1), 60 (2019).
Y. Ismail, I. Sinayskiy, and F. Petruccione, "Integrating machine learning techniques in quantum communication to characterize the quantum channel," J. Opt. Soc. Am. B 36(3), B116–B121 (2019).
S. Lohani, E. M. Knutson, M. O'Donnell, S. D. Huver, and R. T. Glasser, "On the use of deep neural networks in optical communications," Appl. Opt. 57(15), 4180–4190 (2018).
T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, "Convolutional Neural Network-Based Optical Performance Monitoring for Optical Transport Networks," J. Opt. Commun. Netw. 11(1), A52–A59 (2019).
B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, "Multimode optical fiber transmission with a deep learning network," Light: Sci. Appl. 7(1), 69 (2018).
T. Doster and A. T. Watnik, "Machine learning approach to OAM beam demultiplexing via convolutional neural networks," Appl. Opt. 56(12), 3386–3396 (2017).
S. Lohani and R. T. Glasser, "Robust free space oam communications with unsupervised machine learning," in Frontiers in Optics (Optical Society of America, 2019), pp. FTu5B–3.
M. M. Lotfinejad, R. Hafezi, M. Khanali, S. S. Hosseini, M. Mehrpooya, and S. Shamshirband, "A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study," Energies 11(5), 1188 (2018).
H. Ye, Q. Ren, X. Hu, T. Lin, L. Shi, G. Zhang, and X. Li, "Modeling energy-related CO2 emissions from office buildings using general regression neural network," Resour. Conserv. Recycl. 129, 168–174 (2018).
Y. Xu, J. Du, L. Dai, and C. Lee, "A Regression Approach to Speech Enhancement Based on Deep Neural Networks," IEEE/ACM Trans. Audio Speech Lang. Process. 23(1), 7–19 (2015).
L. K. Tan, Y. M. Liew, E. Lim, and R. A. McLaughlin, "Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences," Med. Image Anal. 39, 78–86 (2017).
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, "TensorFlow: Large-scale machine learning on heterogeneous systems," (2015). Software available from tensorflow.org.
Abadi, M.
Achal, R.
Adali, T.
Agarwal, A.
Arimondo, E.
Ba, D.
Babaie, H. A.
Barham, P.
Boyer, V.
Brannan, T.
Brevdo, E.
Buchler, B. C.
Butler, K. T.
Buzsáki, G.
Cai, C.
Cai, Y.
Camacho, R. M.
Campbell, G. T.
Cao, L.
Carlson, D.
Carolan, J.
Cartwright, H.
Citro, C.
Cohen, O.
Corrado, G. S.
Corzo, N.
Dai, L.
Danaci, O.
Davies, D. W.
Davis, A.
Dean, J.
Deng, Z.
Deo, R. C.
Devin, M.
Diao, W.
Dikopoltsev, A.
Doster, T.
Duan, C.
Dzirasa, K.
Ebert-Uphoff, I.
Everett, J. L.
Fabre, C.
Feng, J.
Ferrini, G.
Ghemawat, S.
Glasser, R. T.
Goodfellow, I.
Gray, K. E.
Hafezi, R.
Haham, G. I.
Han, S.
Harp, A.
Hegde, C.
Heller, K.
Hoshida, T.
Hosseini, S. S.
Howell, J. C.
Hu, X.
Huang, D.
Huang, W.
Hudelist, F.
Hush, M. R.
Huver, S. D.
Irving, G.
Isard, M.
Isayev, O.
Ismail, Y.
Jia, Y.
Jing, J.
Jones, K. M.
Jozefowicz, R.
Kaiser, L.
Karpatne, A.
Kato, T.
Khanali, M.
Knutson, E. M.
Kolobov, M. I.
Kong, J.
Konstantinou, G.
Kudlur, M.
Kumar, V.
Lam, P. K.
Lee, C.
Lett, P. D.
Leung, A. C.
Levenberg, J.
Li, X.
Liew, Y. M.
Lim, E.
Lin, T.
Liston, C.
Liu, C.
Liu, S.
Liu, X.
Lohani, S.
Loterie, D.
Lotfinejad, M. M.
Lv, J.
Lv, S.
Mané, D.
Mannor, S.
Mao, Y.
Marino, A. M.
Mayberg, H. S.
McCormick, C. F.
McLaughlin, R. A.
Mehrpooya, M.
Monga, R.
Moore, S.
Morikawa, H.
Moser, C.
Moss, D.
Murray, D.
Na, Z.
O'Donnell, M.
Olah, C.
Olson, J. P.
Ou, Z. Y.
Patera, G.
Paul, K. V.
Petruccione, F.
Pooser, R. C.
Psaltis, D.
Qin, Z.
Rahmani, B.
Ravela, S.
Ren, Q.
Rios, C.
Rudin, C.
Sapiro, G.
Schuster, M.
Segev, M.
Shamshirband, S.
Shi, J.
Shi, L.
Shlens, J.
Sinayskiy, I.
Slatyer, H. J.
Sohal, V. S.
Song, X.
Steinbrecher, G. R.
Steiner, B.
Sutskever, I.
Swaim, J. D.
Talwar, K.
Tan, L. K.
Tanimura, T.
Thiel, C.
Tranter, A. D.
Treps, N.
Tucker, P.
Turnbull, M.
Vanhoucke, V.
Vasudevan, V.
Vernaz-Gris, P.
Viégas, F.
Vinyals, O.
Vu, M.-A. T.
Vudyasetu, P. K.
Walsh, A.
Warden, P.
Watanabe, S.
Watnik, A. T.
Wattenberg, M.
Wicke, M.
Widge, A. S.
Wyllie, S.
Xie, C.
Xu, X.
Xu, Y.
Yang, W.
Ye, H.
Yu, Y.
Zahavy, T.
Zheng, X.
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Fig. 1. (a.) Experimental setup for detecting reference (input) pulses. A Ti:Sapphire laser is locked to a wavelength of approximately 795 nm and passed through a $\lambda /2$ waveplate (WP) then split on a polarizing beam splitter (PBS). A portion of the light is passed through an acoustic-optical modulator (AOM), which is modulated with various pulses and waveforms (inset a-I) from an arbitrary waveform generator. The intensity-modulated and frequency-shifted light (orange) then bypasses a flip mirror (FM) and is incident on a detector (PDA), resulting in the reference probe pulses (a-II). (b.) Experimental setup for detecting gain lines and output probe pulses. The AOM is either scanned in frequency over $\sim$100 MHz (b-I) or pulsed as before (b-II), resulting in a gain line (b-III) or output probe pulses (b-IV) respectively. An energy level diagram for the FWM process is shown in inset b-V.
Fig. 2. Architecture of the neural network for unknown probe prediction using gain and desired outputs as the two channel input, or outputs only (without gain) as the single channel input. Here the measured output probe and dispersion profiles are used to predict the input probe pulse. As discussed in the text the scheme is easily altered to predict the dispersion profile using the input and output probe pulses as the two channel input, or the output pulses only (without input pulses) as the single channel input.
Fig. 3. (a-d) Input probe (pulse) predictions using, green: FWM output probes and gain profiles as two channels training inputs, and red: FWM output probes only (without gain) as a single channel data to train the network.
Fig. 4. Predicting the gain curve of a non-linear medium peaked approximately at a detuning of (a) 3.047 GHz, and (b) 3.071 GHz using FWM output probes and input probes as two channel training inputs (green), and FWM output probes only as single channel training inputs (red) to the network. The mean square loss at each epoch is shown in inset of (b).
Fig. 5. Gain curve predictions using the training data with a set of (a) 2, (b) 4, (c) 8, and (d) 16 pulses, respectively.