The development of quantum internet demands on-chip quantum processor nodes and interconnection between the nodes. Path-encoded photonic qubits are suitable for on-chip quantum information processors, while time-bin encoded ones are good at long-distance communication. It is necessary to develop an on-chip converter between the two encodings to satisfy the needs of the quantum internet. In this work, a quantum photonic circuit is proposed to convert time-bin-encoded photonic qubits to path-encoded ones via a thin-film lithium niobate high-speed optical switch and low-loss matched optical delay lines. The performance of the encoding converter is demonstrated by the experiment of time-bin to path encoding conversion on the fabricated sample chip. The converted path qubits have an average fidelity higher than 97%. The potential of the encoding converter on applications in quantum networks is demonstrated by the experiments of entanglement distribution and quantum key distribution. The results show that the on-chip encoding converter can serve as a foundational component in the future quantum internet, bridging the gap between quantum information transmission and on-chip processing based on photons.

Vision Transformer (ViT) has made significant advancements in computer vision, thanks to its token mixer's sophisticated ability to capture global dependencies between all tokens. However, the quadratic growth in computational demands as the number of tokens increases limits its practical efficiency. Although recent methods have combined the strengths of convolutions and self-attention to achieve better trade-offs, the expensive pairwise token affinity and complex matrix operations inherent in self-attention remain a bottleneck. To address this challenge, we propose S2AFormer, an efficient Vision Transformer architecture featuring novel Strip Self-Attention (SSA). We design simple yet effective Hybrid Perception Blocks (HPBs) to effectively integrate the local perception capabilities of CNNs with the global context modeling of Transformer's attention mechanisms. A key innovation of SSA lies in its reducing the spatial dimensions of $K$ and $V$ while compressing the channel dimensions of $Q$ and $K$. This design significantly reduces computational overhead while preserving accuracy, striking an optimal balance between efficiency and effectiveness. We evaluate the robustness and efficiency of S2AFormer through extensive experiments on multiple vision benchmarks, including ImageNet-1k for image classification, ADE20k for semantic segmentation, and COCO for object detection and instance segmentation. Results demonstrate that S2AFormer achieves significant accuracy gains with superior efficiency in both GPU and non-GPU environments, making it a strong candidate for efficient vision Transformers.

Revealing the momentum-resolved electronic structure of infinite-layer nickelates is essential for understanding this new class of unconventional superconductors, but has been hindered by the formidable challenges in improving the sample quality. In this work, we report for the first time the angle-resolved photoemission spectroscopy of superconducting La$_{0.8}$Sr$_{0.2}$NiO$_{2}$ films prepared by molecular beam epitaxy and ${\mathrm{\textit{in situ}}}$ atomic-hydrogen reduction. The measured Fermi topology closely matches theoretical calculations, showing a large Ni-$d_{x^2-y^2}$ derived Fermi sheet that evolves from hole-like to electron-like along $k_{z}$, and a three-dimensional (3D) electron pocket centered at Brillouin zone corner. The Ni-$d_{x^2-y^2}$ derived bands show a mass enhancement ($m^*/m_{\rm{DFT}}$) of 2-3,while the 3D electron band shows negligible band renormalization. Moreover, the Ni-$d_{x^2-y^2}$ derived states also display a band dispersion anomaly at higher binding energy, reminiscent of the waterfall feature and kinks observed in cuprates.

We tackle human image synthesis, including human motion imitation, appearance transfer, and novel view synthesis, within a unified framework. It means that the model, once being trained, can be used to handle all these tasks. The existing task-specific methods mainly use 2D keypoints to estimate the human body structure. However, they only express the position information with no abilities to characterize the personalized shape of the person and model the limb rotations. In this paper, we propose to use a 3D body mesh recovery module to disentangle the pose and shape. It can not only model the joint location and rotation but also characterize the personalized body shape. To preserve the source information, such as texture, style, color, and face identity, we propose an Attentional Liquid Warping GAN with Attentional Liquid Warping Block (AttLWB) that propagates the source information in both image and feature spaces to the synthesized reference. Specifically, the source features are extracted by a denoising convolutional auto-encoder for characterizing the source identity well. Furthermore, our proposed method can support a more flexible warping from multiple sources. To further improve the generalization ability of the unseen source images, a one/few-shot adversarial learning is applied. In detail, it firstly trains a model in an extensive training set. Then, it finetunes the model by one/few-shot unseen image(s) in a self-supervised way to generate high-resolution (512 x 512 and 1024 x 1024) results. Also, we build a new dataset, namely iPER dataset, for the evaluation of human motion imitation, appearance transfer, and novel view synthesis. Extensive experiments demonstrate the effectiveness of our methods in terms of preserving face identity, shape consistency, and clothes details. All codes and dataset are available on this https URL.

Quantum key distribution (QKD) can provide unconditional secure communication between two distant parties. Although the significance of QKD is undisputed, its feasibility has been questioned because of certain limitations in the practical application of real-life QKD systems. It is a common belief the lack of perfect single-photon source and the existence of detection loss will handicap the feasibility of QKD by creating security loopholes and distance limitations. The measurement device independent QKD (MDIQKD) with decoy-state method removes the security threats from both the imperfect single-photon source and the detection loss. Lengthening the distance and improving the key rate of QKD with such a superior method is thus the central issue in the practical application of QKD. Here, we report the results of MDIQKD over 404 km of ultralow-loss optical fibre and 311 km of standard optical fibre by employing an optimized four-intensity decoy-state method. This record-breaking implementation of MDIQKD method not only provides a new distance record for both MDIQKD and all types of QKD systems, more significantly, it achieves a distance that the traditional BB84 QKD would not be able to achieve with the same detection devices even with ideal single-phone sources. For the first time, our work demonstrates that with the MDIQKD method, imperfect devices can achieve better results than what ideal sources could have achieved. This work represents a significant step towards proving and developing a feasible long-distance QKD.

Measurement-device-independent quantum key distribution (MDIQKD) protocol is immune to all attacks on detection and guarantees the information-theoretical security even with imperfect single photon detectors. Recently, several proof-of-principle demonstrations of MDIQKD have been achieved. Those experiments, although novel, are implemented through limited distance with a key rate less than 0.1 bps. Here, by developing a 75 MHz clock rate fully-automatic and highly-stable system, and superconducting nanowire single photon detectors with detection efficiencies more than 40%, we extend the secure transmission distance of MDIQKD to 200 km and achieve a secure key rate of three orders of magnitude higher. These results pave the way towards a quantum network with measurement-device-independent security.

Altermagnets are characterized by anisotropic band/spin splittings in momentum space, dictated by their spin-space group symmetries. However, the real-space modulations of altermagnetism are often neglected and have not been explored experimentally. Here we combine neutron diffraction, angle-resolved photoemission spectroscopy (ARPES), spin-resolved ARPES and density functional theory to demonstrate that Cs$_{1-\delta}$V$_2$Te$_2$O realizes a spatially modulated form of altermagnetism, i.e., hidden altermagnetism. Such a state in Cs$_{1-\delta}$V$_2$Te$_2$O results from its G-type antiferromagnetism and two-dimensional electronic states, allowing for the development of spatially alternating altermagnetic layers, whose local spin polarizations are directly verified by spin-resolved ARPES measurements. Our experimental discovery of hidden altermagnetism broadens the scope of unconventional magnetism and opens routes to exploring emergent phenomena from real-space modulations of altermagnetic order.

This work reports a theoretical framework that combines the auxiliary coherent potential approximation (ACPA-DMFT) with dynamical mean-field theory to study strongly correlated and disordered electronic systems with both diagonal and off-diagonal disorders. In this method, by introducing an auxiliary coupling space with extended local degree of freedom,the diagonal and off-diagonal disorders are treated in a unified and self-consistent framework of coherent potential approximation, within which the dynamical mean-field theory is naturally combined to handle the strongly correlated Anderson-Hubbard model. By using this approach, we compute matsubara Green's functions for a simple cubic lattice at finite temperatures and derive impurity spectral functions through the maximum entropy method. Our results reveal the critical influence of off-diagonal disorder on Mott-type metal-insulator transitions. Specifically, a reentrant phenomenon is identified, where the system transitions between insulating and metallic states under varying interaction strengths. The ACPA-DMFT method provides an efficient and robust computational method for exploring the intricate interplay of disorder and strong correlations.

Quantum key distribution (QKD) aims to generate secure private keys shared by two remote parties. With its security being protected by principles of quantum mechanics, some technology challenges remain towards practical application of QKD. The major one is the distance limit, which is caused by the fact that a quantum signal cannot be amplified while the channel loss is exponential with the distance for photon transmission in optical fiber. Here using the 3-intensity sending-or-not-sending protocol with the actively-odd-parity-pairing method, we demonstrate a fiber-based twin-field QKD over 1002 km. In our experiment, we developed a dual-band phase estimation and ultra-low noise superconducting nanowire single-photon detectors to suppress the system noise to around 0.02 Hz. The secure key rate is $9.53\times10^{-12}$ per pulse through 1002 km fiber in the asymptotic regime, and $8.75\times10^{-12}$ per pulse at 952 km considering the finite size effect. Our work constitutes a critical step towards the future large-scale quantum network.

In this paper, we propose a fast extrinsic calibration method for fusing multiple inertial measurement units (MIMU) to improve visual-inertial odometry (VIO) localization accuracy. Currently, data fusion algorithms for MIMU highly depend on the number of inertial sensors. Based on the assumption that extrinsic parameters between inertial sensors are perfectly calibrated, the fusion algorithm provides better localization accuracy with more IMUs, while neglecting the effect of extrinsic calibration error. Our method builds two non-linear least-squares problems to estimate the MIMU relative position and orientation separately, independent of external sensors and inertial noises online estimation. Then we give the general form of the virtual IMU (VIMU) method and propose its propagation on manifold. We perform our method on datasets, our self-made sensor board, and board with different IMUs, validating the superiority of our method over competing methods concerning speed, accuracy, and robustness. In the simulation experiment, we show that only fusing two IMUs with our calibration method to predict motion can rival nine IMUs. Real-world experiments demonstrate better localization accuracy of the VIO integrated with our calibration method and VIMU propagation on manifold.

The rapid developments of high-speed trains (HSTs) introduce new challenges to HST wireless communication systems. Realistic HST channel models play a critical role in designing and evaluating HST communication systems. Due to the length limitation, bounding of tunnel itself, and waveguide effect, channel characteristics in tunnel scenarios are very different from those in other HST scenarios. Therefore, accurate tunnel channel models considering both large-scale and small-scale fading characteristics are essential for HST communication systems. Moreover, certain characteristics of tunnel channels have not been investigated sufficiently. This article provides a comprehensive review of the measurement campaigns in tunnels and presents some tunnel channel models using various modeling methods. Finally, future directions in HST tunnel channel measurements and modeling are discussed.

Due to the applications in meteorological broadcasts, radio navigation and underwater communications, low-frequency(LF) receiving antennas have been actively studied. However, because the frequency range of LF antenna is 30kHz to 300kHz, its electromagnetic wavelength is 1km to 10km, which makes LF electromagnetic antennas difficult to be implemented in miniaturized or portable devices. This article presents a miniaturized LF magnetoelectric(ME) receiving antenna with an integrated DC magnetic bias. The antenna is based on the magnetoelectric effect and operates by resonance at its mechanical resonant frequency. Thus, compared with traditional LF wire antennas, the dimension of ME antenna is reduced significantly. Compared with prior art of ME antennas which do not have DC magnetic bias, higher performance can be achieved by integrating the miniaturized DC magnetic bias. Compared with prior art of an ME antenna with bulky external DC magnetic bias, the ME antenna with an integrated DC magnetic bias significantly reduce its dimension. Magnetostrictive TbDyFe2(Terfenol-D) and piezoelectric lead zirconate titanate(PZT) thin films are bonded together to form the 38x12x5.8mm3 ME receiving antenna. Four 10x10x10mm3 Rb magnets are implemented to provide an optimal DC bias for the antenna. A maximum operation distance of 2.5m is demonstrated with the DC magnetic field bias, 2.27 times of the maximum operation distance of the antenna without DC magnet field bias. The efficiency, gain and quality factor the ME receiving antenna is also characterized. The miniaturized LF ME antenna could have potential applications in portable electronics, internet of things and underwater communications.

Self-supervised monocular depth estimation has received widespread attention because of its capability to train without ground truth. In real-world scenarios, the images may be blurry or noisy due to the influence of weather conditions and inherent limitations of the camera. Therefore, it is particularly important to develop a robust depth estimation model. Benefiting from the training strategies of generative networks, generative-based methods often exhibit enhanced robustness. In light of this, we employ the generative-based diffusion model with a unique denoising training process for self-supervised monocular depth estimation. Additionally, to further enhance the robustness of the diffusion model, we probe into the influence of perturbations on image features and propose a hierarchical feature-guided denoising module. Furthermore, we explore the implicit depth within reprojection and design an implicit depth consistency loss. This loss function is not interfered by the other subnetwork, which can be targeted to constrain the depth estimation network and ensure the scale consistency of depth within a video sequence. We conduct experiments on the KITTI and Make3D datasets. The results indicate that our approach stands out among generative-based models, while also showcasing remarkable robustness.

We develop a nonequilibrium multi-orbital extension of the two-particle self-consistent theory and apply it to the bilayer Hubbard model as a minimal platform to investigate correlation effects in the presence of interlayer interactions and tunneling. The method determines vertex corrections in the spin and charge channels self-consistently at the two-particle level, thereby avoiding the spurious finite-temperature phase transitions that limit dynamical mean-field theory in two dimensions. We derive the spectral self-energy and implement the framework directly on the real-frequency axis within the Keldysh nonequilibrium Green's function formalism, enabling the treatment of both equilibrium and non-equilibrium steady states without relying on numerical analytic continuation. As an application, we demonstrate that a pseudogap can emerge in the bilayer Hubbard model when spin, charge, or excitonic fluctuations become sufficiently strong. Instabilities in different channels are also evaluated in an unbiased manner across the parameter space. Remarkably, we find that the excitonic susceptibility grows with increasing interlayer bias, before it gets suppressed at large biases by the charge imbalance between the layers. This work establishes a versatile and computationally efficient framework for investigating correlated multi-orbital systems under nonequilibrium conditions.

Exploring new unconventional superconductors is of great value for both fundamental research and practical applications. It is a long-term challenge to develop and study more hole-doped superconductors in 1111 system of iron-based superconductors. Here we report the discovery of superconductivity with a critical transition temperature up to 30.7 K in the compound CaFeAsF by a post-annealing treatment in air atmosphere. The superconducting behaviors are verified in both the single-crystalline and polycrystalline samples by the resistance and magnetization measurements. The analysis by combining the depth-resolved time-of-flight secondary ion mass spectrometry (TOF-SIMS) and X-ray photoelectron spectroscopy (XPS) measurements shows that the introduction of oxygen elements and the consequent changing in Fe valence by the annealing treatment may lead to the hole-type doping, which is the origin for the occurrence of superconductivity. Our results pave the way for further in-depth investigations on the hole-doped 1111 system in iron-based superconductors.

High-speed ultra-broadband detectors play a crucial role in aerospace technology, and national security etc. The interfacial work function internal photoemission (IWIP) detector employs multiple absorption mechanism comprehensively across different wavelength band to achieve complete photon type detection, which makes it possible to realize high-speed and ultra-broadband simultaneously. We propose a ratchet heterojunction IWIP (HEIWIP) detector, which shows 3-165THz ultra-broadband coverage. The high-speed response is investigated in detail by both microwave rectification technology and high-speed modulated terahertz light. Up to 5.1GHz 3dB bandwidth is acquired in terms of microwave rectification measurement. And 4.255GHz inter-mode optical beat note signal was successfully detected.

To avoid the unpredictable phase deviations of the spaceborne phased array (SPA), this paper considers the over-the-air (OTA) phase calibration of the SPA for the low earth orbit (LEO) satellite communications, where the phase deviations of the SPA and the unknown channel are jointly estimated with multiple transmissions of the pilots. Moreover, the Cramer Rao Bound (CRB) is derived, and the optimization of beam patterns is also presented to lower the root mean squared error (RMSE) of the OTA calibration. The simulation results verify the effectiveness of the proposed OTA phase calibration algorithm as the RMSEs of the phase estimates closely approach the corresponding CRB, and the beam pattern optimization scheme is also validated for more than 4dB gain of SNR over the randomly generated beam patterns.

Measurement-device-independent quantum key distribution (MDI-QKD) can eliminate all detector side channels and it is practical with current technology. Previous implementations of MDI-QKD all use two symmetric channels with similar losses. However, the secret key rate is severely limited when different channels have different losses. Here we report the results of the first high-rate MDI-QKD experiment over $asymmetric$ channels. By using the recent 7-intensity optimization approach, we demonstrate $>$10x higher key rate than previous best-known protocols for MDI-QKD in the situation of large channel asymmetry, and extend the secure transmission distance by more than 20-50 km in standard telecom fiber. The results have moved MDI-QKD towards widespread applications in practical network settings, where the channel losses are asymmetric and user nodes could be dynamically added or deleted.

Recently, Neural Video Compression (NVC) techniques have achieved remarkable performance, even surpassing the best traditional lossy video codec. However, most existing NVC methods heavily rely on transmitting Motion Vector (MV) to generate accurate contextual features, which has the following drawbacks. (1) Compressing and transmitting MV requires specialized MV encoder and decoder, which makes modules redundant. (2) Due to the existence of MV Encoder-Decoder, the training strategy is complex. In this paper, we present a noval Single Stream NVC framework (SSNVC), which removes complex MV Encoder-Decoder structure and uses a one-stage training strategy. SSNVC implicitly use temporal information by adding previous entropy model feature to current entropy model and using previous two frame to generate predicted motion information at the decoder side. Besides, we enhance the frame generator to generate higher quality reconstructed frame. Experiments demonstrate that SSNVC can achieve state-of-the-art performance on multiple benchmarks, and can greatly simplify compression process as well as training process.

With the frequent use of self-supervised monocular depth estimation in robotics and autonomous driving, the model's efficiency is becoming increasingly important. Most current approaches apply much larger and more complex networks to improve the precision of depth estimation. Some researchers incorporated Transformer into self-supervised monocular depth estimation to achieve better performance. However, this method leads to high parameters and high computation. We present a fully convolutional depth estimation network using contextual feature fusion. Compared to UNet++ and HRNet, we use high-resolution and low-resolution features to reserve information on small targets and fast-moving objects instead of long-range fusion. We further promote depth estimation results employing lightweight channel attention based on convolution in the decoder stage. Our method reduces the parameters without sacrificing accuracy. Experiments on the KITTI benchmark show that our method can get better results than many large models, such as Monodepth2, with only 30 parameters. The source code is available at this https URL.