8 papers
Machine Learning based Optimization of CV-QKD Under Practical Constraints
Svitlana Matsenko, Amirhossein Ghazisaeidi, Marcin Jarzyna +4
Practical hardware limitations, including finite transmitter and receiver filter lengths as well as the finite resolution of digital-to-analog and analog-to-digital converters, lea…
Sequential Neural Probabilistic Amplitude Shaping: Learning the Channel's Language
Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi
We present the first neural probabilistic amplitude shaping that outperforms existing methods while accounting for all implementation losses, using a block-less, easily implementab…
Optimization of CV-QKD Under Practical Constraints
Svitlana Matsenko, Amirhossein Ghazisaeidi, Marcin Jarzyna +2
Using reinforcement learning, we optimize for practical hardware constraints, including limited FIR filter taps at the transmitter and receiver, mean photon number and finite DAC/A…
Neural Probabilistic Amplitude Shaping for Nonlinear Fiber Channels
Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi
We introduce neural probabilistic amplitude shaping, a joint-distribution learning framework for coherent fiber systems. The proposed scheme provides a 0.5 dB signal-to-noise ratio…
Accurate and Effective Model for Coexistence of Classical and Quantum Signals In Optical Fibers
Lucas Alves Zischler, ÃaÄla Ãzkan, Tristan Vosshenrich +10
The rising interest in quantum-level communication has resulted in proposals for coexistence schemes with classical signals within the same fiber optic channel, where the most rece…
Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications
Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi
We present an autoregressive end-to-end learning approach for probabilistic shaping on nonlinear fiber channels. Our proposed scheme learns the joint symbol distribution and provid…