We use a deep learning neural network to successfully indicate when an eavesdropper is present in a free-space optics ON-OFF keying communication scheme, in which the bit stream has passed through turbulence.
We demonstrate the efficacy of machine learning techniques in the detection of an eavesdropper in a free-space optical (FSO) communications setup. Experimentally, we use ON-OFF keying (OOK) and send strings of random bits through strong turbulence. When we apply a simulated eavesdropper to the bits in the post processing stage, a deep learning convolutional neural network (CNN) is able to successfully detect whether or not the eavesdropper is present. We vary the strength and duration of the attenuation of the simulated eavesdropper, and vary the signal-to-noise ratio (SNR) of the bit streams, and find that the strength of the eavesdropper has the greatest effect on eavesdropper detection accuracy. We are hopeful this flexible approach may be used in current and future operational FSO communications systems.
Satellite-based quantum communication is a promising approach for establishing global-scale quantum networks. In free-space quantum channels, single-mode-fiber coupling plays a crucial role in increasing the signal-to-noise ratio of daylight quantum key distribution (QKD) and ensuring compatibility with standard fiber-based QKD protocols. However, consistently achieving high efficiency and stable single-mode-fiber coupling under strong atmospheric turbulence remains an ongoing experimental challenge. In this study, we experimentally demonstrate an adaptive method based on convolutional neural networks capable of directly estimating phase information from a single defocused image. We developed a convolutional neural network to establish the relationship between intensity distribution and phase information of turbulent distortions. We demonstrate the real-time performance of our deep-learning adaptive method in increasing single-mode-fiber coupling efficiency across various turbulence scales and quantify turbulence frequencies. Notably, the method proved highly effective in strong-turbulence scenarios, with frequencies reaching up to 200 Hz, leading to a significant increase in single-mode-fiber coupling efficiency. We demonstrate the corrective capability of our adaptive method for strong turbulence, enabled by the generalization of the convolutional neural network. Our results offer an efficient solution for daytime free-space QKD applications.
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.