This paper deals with a channel-parameter estimation for continuous-variable quantum key distribution (CV-QKD) over a satellite-to-submarine link. In particular, we focus on the channel transmittances and the excess noise which are affected by atmospheric turbulence, surface roughness, zenith angle of the satellite, wind speed, submarine depth, etc. The estimation method is based on proposed algorithms and is applied to low-Earth orbits using the Monte Carlo approach. For light at 550 nm with a repetition frequency of 1 MHz, the effects of the estimated parameters on the performance of the CV-QKD system are assessed by a simulation by comparing the secret key bit rate in the daytime and at night. Our results show the feasibility of satellite-to-submarine CV-QKD, providing an unconditionally secure approach to achieve global networks for underwater communications.

Removing quantum hacking attacks is a highly challenging task for quantum key distribution (QKD) since one needs to correctly detect the types of attacks. There is a big gap between the idealized theoretical model and the practical physical system, which results in various hacking strategies exploited from security loopholes. To bridge this gap, previous continuous-variable (CV) QKD systems adopted a universal statistical model to represent different dimensional characters of the physical layer. The challenge is to discriminate different attacks from the exposed key physical details. Here we present a semisupervised deep-learning method to detect the known attacks or potential threats that are hard to be captured in previous CVQKD systems. The proof-of-principle experiment result shows that it could break the present limitations that different attacks are relatively independent, and key features computed from previous threshold-based methods are not correlated with each other. We anticipate that our proposal will give insight into how quantum hacking attacks can be detected under the real system modeling constraints, and will enable the present quantum communication systems to reach a higher level of practical security without adding new assumptions.

We introduce a reliable scheme for continuous-variable quantum key distribution (CV-QKD) by using orthogonal frequency division multiplexing (OFDM). As a spectrally efficient multiplexing technique, OFDM allows a large number of closely spaced orthogonal subcarrier signals used to carry data on several parallel data streams or channels. We place emphasis on modulator impairments which would inevitably arise in the OFDM system and analyze how these impairments affect the OFDM-based CV-QKD system. Moreover, we also evaluate the security in the asymptotic limit and the Pirandola-Laurenza-Ottaviani-Banchi upper bound. Results indicate that although the emergence of imperfect modulation would bring about a slight decrease in the secret key bit rate of each subcarrier, the multiplexing technique combined with CV-QKD results in a desirable improvement on the total secret key bit rate which can raise the numerical value about an order of magnitude.

The contact force network, usually organized inhomogeneously by the inter-particle forces on the bases of the contact network topologies, is essential to the rigidity and stability in amorphous solids. How to capture such a “backbone” is crucial to the understanding of various anomalous properties or behaviors in those materials, which remains a central challenge presently in physics, engineering, or material science. Here, we use a novel graph neural network to predict the contact force network in two-dimensional granular materials under uniaxial compression. With the edge classification model in the framework of the deep graph library, we show that the inter-particle contact forces can be accurately estimated purely from the knowledge of the static microstructures, which can be acquired from a discrete element method or directly visualized from experimental methods. By testing the granular packings with different structural disorders and pressure, we further demonstrate the robustness of the optimized graph neural network to changes in various model parameters. Our research tries to provide a new way of extracting the information about the inter-particle forces, which substantially improves the efficiency and reduces the costs compared to the traditional experiments.

As the existence of non-zero reflection coefficients in the real component of continuous-variable quantum key distribution (CV-QKD) systems, Eve can probe the system by sending the bright light pulses into Alice’s set-up. With the analysis of back-reflections, Eve only takes a few back-reflected photons to intercept information and obtain the raw key bit. In this paper, the attack problems are converted into the information leakage problems. First, we analyzed the Trojan horse attacks with different wavelengths and confirmed its side effects, such as crosstalk and anti-Stokes Raman scattering, by a numerical simulation. Then, based on the wavelength-dependent property of beam splitter, we presented a practical way to estimate the deviation of shot noise and therefore correct the excess noise by inserting different wavelength pulses under joint attacks. Finally, we specified the security bounds of the system through quantifying the excess noise bounds caused by the Trojan horse attacks and provided a theoretical reference for the secret key transmission of system. As a consequence, the transmission errors within the security bounds can be negligible and the legitimate users will not perceive the presence of Eve.

Quantum key distribution (QKD) offers information-theoretical security, while real systems are thought not to promise practical security effectively. In the practical continuous-variable (CV) QKD system, the deviations between realistic devices and idealized models might introduce vulnerabilities for eavesdroppers and stressors for two parties. However, the common quantum hacking strategies and countermeasures inevitably increase the complexity of practical CV systems. Machine-learning techniques are utilized to explore how to perceive practical imperfections. Here, we review recent works on secure CVQKD systems with machine learning, where the methods for detections and attacks were studied.