Detection of quantum signals free of classical noise via quantum correlation

ORAL

Abstract

Extracting valuable signals is essential for both classical and quantum technologies. Traditional noise filtering techniques depend on distinct patterns of signal and noise in frequency or time domains, which constrains their applicability, especially in quantum sensing. Here, we introduce a signal-nature-based (not signal-pattern-based) approach that singles out a quantum signal from its classical noise background by employing the intrinsic quantum nature of the system. We design an innovative protocol to extract the Quantum Correlation signal and use it to single out the signal of a remote nuclear spin from its overwhelming classical noise backgrounds, which is impossible to be accomplished by conventional filter methods. Our research highlights the quantum/classical nature as a novel degree of freedom in quantum sensing. The broader implementation of this quantum nature-based methodology paves the way for new avenues in quantum research.

* P.W. is supported by the Talents Introduction Foundation of Beijing Normal University with Grant No.310432106. P.W. & R.B.L. were supported by Hong Kong RGC General Research Fund (143000119) and NSFC/RGC Joint Research Scheme (N CUHK403/16). J.W. acknowledges financial supports from ERC grant SMeL, EU Project ASTERIQS, DFG (GRK2642) and DFG Research group FOR 2724. S.Y. acknowledges financial supports from Hong Kong RGC (GRF/24304617, 14304618).

Publication: Yang Shen, Ping Wang, Chun Tung Cheung, J¨org Wrachtrup, Ren-Bao Liu, and Sen Yang. Detection of quantum signals free of classical noise via quantum correlation. Physical Review Letters, 130(7):070802, 2023.

Presenters

  • Yang Shen

    The Hong Kong University of Science and Technology (HKUST)

Authors

  • Yang Shen

    The Hong Kong University of Science and Technology (HKUST)

  • Ping Wang

    Beijing Normal University

  • Chun T Cheung

    The Chinese University of Hong Kong

  • Jörg Wrachtrup

    University of Stuttgart

  • Renbao Liu

    Chinese University of Hong Kong

  • Sen Yang

    Hong Kong University of Science and Technology, HKUST, The Hong Kong University of Science and Technology (HKUST), The Hong Kong University of Science and Technology