Export citation

Export citation

Choose format for download:

Download Citation

    Classical-correlation-enhanced weak-value amplification resilient to persistent noises

    Xu-Song Hong1,2,3,4, Gong-Chu Li1,2,3,4, Lei Chen1,2,3, Si-Qi Zhang1,2,3, Hua-Qin Xu1,2, Yuancheng Liu1,2, Shengshi Pang4,5,*, Andrew N. Jordan6,7,8,†, Geng Chen1,2,3,4,‡ et al.

    Chuan-Feng Li1,2,3,4,§ and Guang-Can Guo1,2,3,4

    • *Contact author: pangshsh@mail.sysu.edu.cn
    • †Contact author: jordan@chapman.edu
    • ‡Contact author: chengeng@ustc.edu.cn
    • §Contact author: cfli@ustc.edu.cn

    Phys. Rev. Applied 25, 054071 – Published 27 May, 2026

    DOI: https://doi.org/10.1103/755l-23f6

    Abstract

    Weak measurement (WM) offers the advantage of amplifying small signals at the cost of postselecting a small portion of the probes. This so-called weak-value amplification effect makes it compare favorably with conventional techniques (without postselection) to overcome various technical noises. However, certain types of technical noise, such as jitter and pixelation, present insurmountable limitations for both WM and conventional techniques. In this work, we propose an advanced variant of WM that incorporates time-momentum correlation (TMC) into biased weak measurement (BWM). By employing the Fisher information metric, we theoretically show that TMC-BWM can overcome jitter and pixelation, and meanwhile extract significantly higher Fisher information. This dual advantage was experimentally validated in a magnetic-sensing application when applying severe jitter and pixelation, and it remains robust under natural conditions, achieving a 24.8-dB improvement in precision over the standard WM scheme. Moreover, merely classical resources are required to achieve this dual metrological advantage.

    Physics Subject Headings (PhySH)

    Authorization Required

    We need you to provide your credentials before accessing this content.

    References (Subscription Required)

    Outline

    Information

    Sign In to Your Journals Account

    Filter

    Filter

    Article Lookup

    Enter a citation