Li Ruifeng, Ma Jinyan, Li Da, Wu Yunlong, Qian Chao, Zhang Ling, Chen Hongsheng, Kottos Tsampikos, Li Er-Ping
Zhejiang University-University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining 314400, China.
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
Research (Wash D C). 2024 May 31;7:0375. doi: 10.34133/research.0375. eCollection 2024.
Pushing the information states' acquisition efficiency has been a long-held goal to reach the measurement precision limit inside scattering spaces. Recent studies have indicated that maximal information states can be attained through engineered modes; however, partial intrusion is generally required. While non-invasive designs have been substantially explored across diverse physical scenarios, the non-invasive acquisition of information states inside dynamic scattering spaces remains challenging due to the intractable non-unique mapping problem, particularly in the context of multi-target scenarios. Here, we establish the feasibility of non-invasive information states' acquisition experimentally for the first time by introducing a tandem-generated adversarial network framework inside dynamic scattering spaces. To illustrate the framework's efficacy, we demonstrate that efficient information states' acquisition for multi-target scenarios can achieve the Fisher information limit solely through the utilization of the external scattering matrix of the system. Our work provides insightful perspectives for precise measurements inside dynamic complex systems.
提高信息态的获取效率一直是在散射空间内达到测量精度极限的长期目标。最近的研究表明，通过工程模式可以实现最大信息态；然而，通常需要部分侵入。虽然在各种物理场景中对非侵入性设计进行了大量探索，但由于难以处理的非唯一映射问题，在动态散射空间内非侵入性获取信息态仍然具有挑战性，特别是在多目标场景的背景下。在这里，我们首次通过在动态散射空间内引入串联生成对抗网络框架，通过实验确立了非侵入性获取信息态的可行性。为了说明该框架的有效性，我们证明了在多目标场景中高效获取信息态仅通过利用系统的外部散射矩阵就能达到费舍尔信息极限。我们的工作为动态复杂系统中的精确测量提供了有见地的观点。
