Advancing Classical and Quantum Communication Systems with Machine Learning

A perspective on how machine learning can aid the next-generation of classical and quantum optical communication systems is given. We focus on the design of Raman amplifiers and phase tracking at the quantum limit.

[1]  Eleni Diamanti,et al.  Distributing Secret Keys with Quantum Continuous Variables: Principle, Security and Implementations , 2015, Entropy.

[2]  Metodi P. Yankov,et al.  End-to-end Learning for GMI Optimized Geometric Constellation Shape , 2019, 45th European Conference on Optical Communication (ECOC 2019).

[3]  Peter J Winzer,et al.  Fiber-optic transmission and networking: the previous 20 and the next 20 years [Invited]. , 2018, Optics express.

[4]  Polina Bayvel,et al.  End-to-End Deep Learning of Optical Fiber Communications , 2018, Journal of Lightwave Technology.

[5]  Anthony Leverrier.  Distributing Secret Keys with Quantum Continuous Variables , 2016 .

[6]  Alex Alvarado,et al.  Revisiting Multi-Step Nonlinearity Compensation with Machine Learning , 2019, 45th European Conference on Optical Communication (ECOC 2019).

[7]  Chao Lu,et al.  An Optical Communication's Perspective on Machine Learning and Its Applications , 2019, Journal of Lightwave Technology.

[8]  Nelson Costa,et al.  Towards multiband optical systems , 2018 .

[9]  Sebastian Kleis,et al.  Improving the Secret Key Rate of Coherent Quantum Key Distribution With Bayesian Inference , 2019, Journal of Lightwave Technology.

[10]  Andrea Carena,et al.  Inverse System Design Using Machine Learning: The Raman Amplifier Case , 2020, Journal of Lightwave Technology.

[11]  John E. Bowers,et al.  Highly-Sensitive Phase and Frequency Noise Measurement Technique Using Bayesian Filtering , 2019, IEEE Photonics Technology Letters.

[12]  E H Huntington,et al.  Adaptive optical phase estimation using time-symmetric quantum smoothing. , 2009, Physical review letters.