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Hai Feng

Publications and source records attributed to Hai Feng.

3 recordsLinked to original sources

Mitigating sloppiness with intermediate controls in sequential multiparameter spin interferometry

Multiparameter quantum sensing requires different parameters to produce distinguishable probe responses while allowing their information to be extracted simultaneously. In sequential sensing without intermediate control, two phases encoded through the same generator are locally indistinguishable. Here we show that parameter-independent intermediate controls overcome this limitation in a collective-spin interferometer by separating the corresponding effective generators in the probe covariance geometry. Specifically, a transverse collective rotation provides an analytically tractable realization, and intermediate one-axis twisting (OAT) generates additional nonlinear operator directions and a broad regime of enhanced joint estimation. For fixed total particle number $N$ and matched twisting resource, both marginal precision bounds lie below those of a separated-OAT strategy while measurement incompatibility remains weak. For intermediate OAT, conditional optimization of an upper bound on the Holevo cost yields asymptotic $N^{-2}$ scaling. For this protocol, a fixed rotated-Dicke measurement retains simultaneous two-parameter enhancement over a broad phase region. These results show that nonlinear intermediate control can mitigate sloppiness while preserving quantum-enhanced precision with a common readout.

quant-ph↗

Streamlined Photoacoustic Image Processing with Foundation Models: A Training-Free Solution

Foundation models have rapidly evolved and have achieved significant accomplishments in computer vision tasks. Specifically, the prompt mechanism conveniently allows users to integrate image prior information into the model, making it possible to apply models without any training. Therefore, we propose a method based on foundation models and zero training to solve the tasks of photoacoustic (PA) image segmentation. We employed the segment anything model (SAM) by setting simple prompts and integrating the model's outputs with prior knowledge of the imaged objects to accomplish various tasks, including: (1) removing the skin signal in three-dimensional PA image rendering; (2) dual speed-of-sound reconstruction, and (3) segmentation of finger blood vessels. Through these demonstrations, we have concluded that deep learning can be directly applied in PA imaging without the requirement for network design and training. This potentially allows for a hands-on, convenient approach to achieving efficient and accurate segmentation of PA images. This letter serves as a comprehensive tutorial, facilitating the mastery of the technique through the provision of code and sample datasets.

cs.CV↗

Atomic-site dependent pairing gap in monolayer FeSe/SrTiO$_3$(001)- ($\sqrt{13} \times \sqrt{13}$)

The interfacial FeSe/TiO$_{2-δ}$ coupling induces high-temperature superconductivity in monolayer FeSe films. Using cryogenic atomically resolved scanning tunneling microscopy/spectroscopy, we obtained atomic-site dependent surface density of states, work function, and pairing gap in the monolayer FeSe on SrTiO$_3$(001)-($\sqrt{13} \times \sqrt{13}$)-33.7$°$ surface. Our results disclosed the out-of-plane Se-Fe-Se triple layer gradient variation, switched DOS for Fe sites on and off TiO$_{5\square}$, and inequivalent Fe sublattices, which gives global spatial modulation of pairing gap contaminant with the ($\sqrt{13} \times \sqrt{13}$) pattern. Moreover, the coherent lattice coupling induces strong inversion asymmetry and in-plane anisotropy in the monolayer FeSe, which is demonstrated to correlate with the particle-hole asymmetry in coherence peaks. The strong atomic-scale correlations in lattice and electronic structure, and pairing gap in particular, put constraints on exploring the unconventional high-temperature superconductivity emerging from interface coupling, e.g., strong demand for atomic-scale interface engineering and characterization.

cond-mat.supr-con↗