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Frontiers in Optics

Physics-informed neural networks for classical and quantum communication with multimode fibers

Qian Zhang, Jiali Sun, Stefan Rothe, Jürgen W. Czarske

Published Jan 1, 2024• 1 citation
Publisher

Abstract

Physics-informed neural networks provide mode decomposition based on intensity-only images in high-dimensional quantum communication using multimode fibers.
DOI
10.1364/fio.2024.fm4b.3

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As the demand for data capacity continues to grow, optical fiber communication systems are progressively approaching their theoretical limits, thereby motivating the exploration of high-dimensional (HD) degrees of freedom such as orbital angular momentum (OAM). However, strong intermodal coupling in multimode fibers (MMFs) poses substantial challenges for the transmission and recovery of HD optical information. Here, we propose and experimentally demonstrate an HD OAM-array-based multichannel multitask optical communication framework via a single MMF. By encoding different information tasks into HD OAM superposition states across multiple wave-vector (k) channels, the proposed scheme enables the simultaneous transmission of heterogeneous optical information within a unified fiber platform. At the receiver, a residual neural network constrained by a multi-channel quantum-state fidelity loss function directly reconstructs the transmitted signals from single-frame speckle intensity measurements, without requiring phase retrieval or channel inversion. Experiments demonstrate the synchronous communication of multichannel classical image signals and HD mutually unbiased basis states through the same MMF, while maintaining high reconstruction fidelity and low interchannel crosstalk under strong modal mixing conditions. This approach provides a scalable route toward high-capacity hybrid classical–quantum optical communication in fiber systems.