Bridging disorder and order in random lasers for cryptographic applications via deep learning

Abstract Random lasers rely on multiple scattering in disordered media to generate emission with complex spectral behavior. While their high-entropy output is valuable for random number generation, the inherent unpredictability has historically limited their utility in structured information processing. Here, we demonstrate the coexistence of spectral randomness and determinism within these systems. Using deep neural networks for multi-dimensional spectral analysis, we identify inter-modal correlations that permit partial recovery of spectral intensity components, despite temporal fluctuations. We then develop a key generation and distribution scheme utilizing the dual nature of random lasers: the disorder ensures cryptographic randomness, while the underlying order facilitates accurate key transmission. This work presents a framework for physical-layer security, suggesting potential applications in photonic-based cryptography and secure communications.