The Laboratory of Quantum Sensing and Quantum Metrology of the School of Physics of Huazhong University of Science and Technology was established in October 2014, and jointly initiated the establishment of the International Joint Laboratory of Quantum Sensing and Quantum Metrology of HUST-UULM with the University of Ulm in Germany.

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Most Recent Preprints

High-resolution wide-field magnetic imaging with sparse sampling using nitrogen-vacancy centers Keqing Liu, Jiazhao Tian, Bokun Duan, Hao Zhang, Kangze Li, GuofengZhang, Fedor Jelezko, Ressa S. Said, Jianming Cai and Liantuan Xiao,arXiv:2602.00679

We demonstrate a sparse-sampling strategy for wide-field NV-ensemble magnetometry that reconstructs 10⁴-pixel images from just 25 measurements using mean-adjusted Bayesian estimation, achieving SSIM >0.999 and twofold sensitivity enhancement via optimized pulse sequences.
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Engineered Randomness for Ubiquitous Quantum-Enhanced Metrology in Exponential-Dimensional Manifolds Yaoming Chu, Baiyi Yu, Hartmut Häffner, Markus Heyl, Nathan Goldman, Jianming Cai,arXiv:2605.31442

A fundamental discovery is revealed: the Heisenberg limit — the pinnacle of quantum precision — is not the rare exception it is often assumed to be, but instead a broadly accessible feature of the exponentially vast Hilbert space.
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Practical advantage of non-Hermitian enhanced quantum sensing Kun Yang, Yaoming Chu, Musang Gong, Ning Wang, Jianming Cai,arXiv: 2603.20612

Non-Hermitian quantum systems near exceptional points enhance response, yet their signal-to-noise advantage under realistic noise remains debated. Here, we show that non-Hermitian sensing could outperform Hermitian schemes in practical noise conditions, and supported by Fisher information analysis demonstrating regimes of improved measurement precision.
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Ramsey interferometry and radical pair mechanism for magnetic field sensing: A comparative study Xiaoyu Chen, Haibin Liu, Jianming Cai, Phys. Lett. A 592, 131928 (2026)

Through the comparison under ideal conditions and deficient experimental parameter control, we find that the Ramsey-like model performs better with an environment where the parameters are highly controllable, otherwise RPM model that does not require precise time control and accurate prior knowledge of the measured parameters will stand out. These results demonstrate a trade-off between detection precision and practical applicability.
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