Continuous variable-quantum key distribution (CV-QKD) protocols attract increasing attentions in recent years because they enjoy high secret key rate (SKR) and good compatibility with existing optical communication infrastructure. Classical coherent receivers are widely employed in coherent states based CV-QKD protocols, whose detection performance is bounded by the standard quantum limit (SQL). Recently, quantum receivers based on displacement operators are experimentally demonstrated with detection performance outperforming the SQL in various practical conditions. However, potential applications of quantum receivers in CV-QKD protocols under turbulent channels are still not well explored, while practical CV-QKD protocols must survive from the atmospheric turbulence in satellite-to-ground optical communication links. In this paper, we consider the possibility of using a quantum receiver called multi-stage CD-Kennedy receiver to enhance the SKR performance of a quadrature phase shift keying (QPSK) modulated CV-QKD protocol in turbulent channels. We first derive the error probability of the multi-stage CD-Kennedy receiver for detecting QPSK signals in turbulent channels and further propose three types of multi-stage CD-Kennedy receiver with different displacement choices, i.e., the Type-I, Type-II, and Type-III receivers. Then we derive the SKR of a QPSK modulated CV-QKD protocol using the multi-stage CD-Kennedy receiver and post-selection strategy in turbulent channels. Numerical results show that the multi-stage CD-Kennedy receiver can outperform the classical coherent receiver in turbulent channels in terms of both error probability and SKR performance and the Type-II receiver can tolerate worse channel conditions compared with Type-I and Type-III receivers in terms of error probability performance.

Channel modeling for the satellite-to-underwater laser communication (StULC) link remains challenging due to long distances and the diversity of the channel constituents. The StULC channel is typically segmented into three isolated channels: the atmospheric channel, the air-water interface channel, and the underwater channel. Previous studies involving StULC channel modeling either focused on separated channels or neglected the combined effects of particles and turbulence on laser propagation. In this paper, we established a comprehensive StULC channel model by an analytical-Monte Carlo hybrid approach, taking into account the effects of particles, bubbles, and turbulence. We first obtained the intensity distribution of the transmitted laser beam after passing through the turbulent atmosphere based on the extended Huygens-Fresnel principle. Then we derived a closed-form probability density function of angular fluctuations of the beam refracted by the random sea surface. These analytical results are then mapped to initialize photon states for the Monte Carlo simulation of the underwater channel. Based on the proposed StULC channel model, we analyzed the bit error rate and the outage probability under different environmental conditions and system parameters. Numerical results demonstrated that the influence of underwater particle concentration on the communication performance is more pronounced than that of both the atmospheric turbulence and the underwater turbulence. In addition, enlarging the receiver aperture provides a more effective performance improvement than increasing the receiver field-of-view angle.

With the increasing popularity of autonomous driving and 3D reconstruction, keypoint detection, as a key link in visual localization, has become a hot topic in current research. However, existing keypoint detection methods rarely pay attention to the difficulty differences of samples and lack a progressive learning mechanism, which often leads to overfitting for simple samples and underfitting for complex samples, limiting the overall performance of the model. To address these issues, we propose a novel progressive gradient-guided self-distillation method (PG${^{2}}$SD) for keypoint detection, which possesses self-evolutionary learning capabilities. Specifically, we propose a progressive gradient constraint strategy (PGCS) that dynamically adjusts the gradient contributions of different samples, enabling the model to adapt to the evolving learning capability during training. On this basis, we propose a gradient-guided self-distillation strategy (G${^{2}}$SDS), which integrates seamlessly with PGCS to alleviate the insufficient feature representation of hard samples in the early training stage. We further design a novel loss function to achieve dynamic collaboration between PGCS and G${^{2}}$SDS, allowing G${^{2}}$SDS to adaptively adjust the self-distillation parameters through the PGCS. Experimental results on multiple benchmark datasets show that our method achieves state-of-the-art performance on image matching, visual localization, and 3D reconstruction tasks without designing a proprietary network, indicating broad application prospects.