Implementation of 400 Gbps quantum noise stream cipher encryption for 1520 km fiber transmission using end-to-end deep learning.

In the era of large models and big data, the security of optical fiber communication backbone networks has garnered significant attention. Quantum noise stream cipher (QNSC) stands as a crucial method for safeguarding the physical layer security of optical fiber communications, yet the current schemes lag behind the rate capabilities of existing 400G optical fiber backbone networks. In this paper, we introduce deep learning into QNSC and propose an end-to-end quantum noise stream cipher (E2E-QNSC) scheme, which encrypts 16 quadrature amplitude modulation (QAM) into E2E-65536QAM/QNSC. Our experiments successfully demonstrate secure optical communication with a single-channel rate of 400 Gbps, a total capacity of 8.4 Tbps, and a transmission distance of 1520 km. Even in the most extreme scenarios, the detection failure probability (DFP) of the scheme remains at an excellent level greater than 0.9999, proving the security of the approach. The experimental results presented herein represent the highest rate-distance product record of QNSC secure transmission systems, to the best of our knowledge.