Non-Invasive Self-Adaptive Information States' Acquisition inside Dynamic Scattering Spaces

Ruifeng Li; Jinyan Ma; Da Li; Yunlong Wu; Chao Qian; Ling Zhang; Hongsheng Chen; Tsampikos Kottos; Er-Ping Li

doi:10.34133/research.0375

Non-Invasive Self-Adaptive Information States' Acquisition inside Dynamic Scattering Spaces

Research (Wash D C). 2024 May 31:7:0375. doi: 10.34133/research.0375. eCollection 2024.

Authors

Ruifeng Li^{1

2}, Jinyan Ma^{1

2}, Da Li^{1

2}, Yunlong Wu^{1

2}, Chao Qian^{1

2}, Ling Zhang^{1

2}, Hongsheng Chen^{1

2}, Tsampikos Kottos³, Er-Ping Li^{1

2}

Affiliations

¹ Zhejiang University-University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining 314400, China.
² College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
³ Wave Transport in Complex Systems Lab, Department of Physics, Wesleyan University, Middletown, CT 06459, USA.

Abstract

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.