Zhenqi Xu | Searcharxiv

arXiv2026

Realization of Backward Retrieval in a Stark-modulated Spin-wave Quantum Memory

We report the first experimental realization of backward retrieval in a spin-wave quantum memory based on a Stark-echo-modulated protocol in Eu3+:Y2SiO5. By using Stark control, we preserve the full optical depth of the ensemble while suppressing coherent noise, enabling conditional storage fidelities above 97%. Our analysis shows that the present backward-retrieval efficiency is mainly limited by technical imperfections rather than by fundamental constraints. With realistic engineering improvements, backward retrieval in this protocol could move beyond the reabsorption-limited forward-emission regime. The protocol is also compatible with cavity-enhanced operation, offering an additional route toward higher efficiencies. These findings establish Stark-echo modulation as a practical and scalable route to high-efficiency, long-lived solid-state quantum memories.

quant-ph↗

arXiv2019

Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Information

Multi-person pose estimation is an important but challenging problem in computer vision. Although current approaches have achieved significant progress by fusing the multi-scale feature maps, they pay little attention to enhancing the channel-wise and spatial information of the feature maps. In this paper, we propose two novel modules to perform the enhancement of the information for the multi-person pose estimation. First, a Channel Shuffle Module (CSM) is proposed to adopt the channel shuffle operation on the feature maps with different levels, promoting cross-channel information communication among the pyramid feature maps. Second, a Spatial, Channel-wise Attention Residual Bottleneck (SCARB) is designed to boost the original residual unit with attention mechanism, adaptively highlighting the information of the feature maps both in the spatial and channel-wise context. The effectiveness of our proposed modules is evaluated on the COCO keypoint benchmark, and experimental results show that our approach achieves the state-of-the-art results.

cs.CV↗