arXiv subjects

Ziqi Zeng

Publications and source records attributed to Ziqi Zeng.

5 recordsLinked to original sources

arXiv2026

Engineering energy-time entanglement from resonance fluorescence

Resonance fluorescence from a coherently driven two-level emitter is a minimal quantum optical field that combines phase coherence with single-photon-level nonlinearity. Here we show that it can be engineered, using only passive linear interferometry, into energy-time entanglement. By injecting resonance fluorescence from a single quantum dot into an asymmetric Mach--Zehnder interferometer operated near destructive interference of the single-photon component, we generate an output field whose coincidence statistics are dominated by the simultaneous two-photon contribution |2> and the temporally separated photon-pair contribution |11>. In a Franson geometry, these two sectors are resolved on the coincidence-delay axis, and both exhibit high-visibility nonlocal interference fringes and violate the Clauser--Horne--Shimony--Holt Bell inequality. Our results reveal a general route for engineering entanglement from resonance fluorescence using passive optics.

quant-ph↗

arXiv2025

Two-photon interference between mutually-detuned resonance fluorescence signals scattered off a semiconductor quantum dot

The radiative linewidth of a two-level emitter (TLE) fundamentally limits the bandwidth available for quantum information processing. Despite its importance, no prior experiment has systematically examined how driving detuning affects the indistinguishability of photons scattered from a TLE - a parameter critical for photonic quantum computing. Here, we perform post-selective two-photon interference measurements between mutually detuned resonance fluorescence signals from an InAs quantum dot embedded in a micropillar cavity. At small mutual laser detunings (<=0.5GHz), the results are accurately described by the pure-state model [Nat. Commun. 16, 6453 (2025)], which treats all resonance-fluorescence photons as spontaneous emission. At larger detunings, we uncover an anomalous feature in the two-photon interference, where the normalised second-order correlation function under orthogonal polarisations yields g2_vert(0) < 0.5.

quant-ph↗

arXiv2025

A Clinical-grade Universal Foundation Model for Intraoperative Pathology

Intraoperative pathology is pivotal to precision surgery, yet its clinical impact is constrained by diagnostic complexity and the limited availability of high-quality frozen-section data. While computational pathology has made significant strides, the lack of large-scale, prospective validation has impeded its routine adoption in surgical workflows. Here, we introduce CRISP, a clinical-grade foundation model developed on over 100,000 frozen sections from eight medical centers, specifically designed to provide Clinical-grade Robust Intraoperative Support for Pathology (CRISP). CRISP was comprehensively evaluated on more than 15,000 intraoperative slides across nearly 100 retrospective diagnostic tasks, including benign-malignant discrimination, key intraoperative decision-making, and pan-cancer detection, etc. The model demonstrated robust generalization across diverse institutions, tumor types, and anatomical sites-including previously unseen sites and rare cancers. In a prospective cohort of over 2,000 patients, CRISP sustained high diagnostic accuracy under real-world conditions, directly informing surgical decisions in 92.6% of cases. Human-AI collaboration further reduced diagnostic workload by 35%, avoided 105 ancillary tests and enhanced detection of micrometastases with 87.5% accuracy. Together, these findings position CRISP as a clinical-grade paradigm for AI-driven intraoperative pathology, bridging computational advances with surgical precision and accelerating the translation of artificial intelligence into routine clinical practice.

cs.LG↗

arXiv2024

Semantic-guided Adversarial Diffusion Model for Self-supervised Shadow Removal

Existing unsupervised methods have addressed the challenges of inconsistent paired data and tedious acquisition of ground-truth labels in shadow removal tasks. However, GAN-based training often faces issues such as mode collapse and unstable optimization. Furthermore, due to the complex mapping between shadow and shadow-free domains, merely relying on adversarial learning is not enough to capture the underlying relationship between two domains, resulting in low quality of the generated images. To address these problems, we propose a semantic-guided adversarial diffusion framework for self-supervised shadow removal, which consists of two stages. At first stage a semantic-guided generative adversarial network (SG-GAN) is proposed to carry out a coarse result and construct paired synthetic data through a cycle-consistent structure. Then the coarse result is refined with a diffusion-based restoration module (DBRM) to enhance the texture details and edge artifact at second stage. Meanwhile, we propose a multi-modal semantic prompter (MSP) that aids in extracting accurate semantic information from real images and text, guiding the shadow removal network to restore images better in SG-GAN. We conduct experiments on multiple public datasets, and the experimental results demonstrate the effectiveness of our method.

cs.CV↗

arXiv2024

Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement

Underwater images suffer from complex and diverse degradation, which inevitably affects the performance of underwater visual tasks. However, most existing learning-based Underwater image enhancement (UIE) methods mainly restore such degradations in the spatial domain, and rarely pay attention to the fourier frequency information. In this paper, we develop a novel UIE framework based on spatial-frequency interaction and gradient maps, namely SFGNet, which consists of two stages. Specifically, in the first stage, we propose a dense spatial-frequency fusion network (DSFFNet), mainly including our designed dense fourier fusion block and dense spatial fusion block, achieving sufficient spatial-frequency interaction by cross connections between these two blocks. In the second stage, we propose a gradient-aware corrector (GAC) to further enhance perceptual details and geometric structures of images by gradient map. Experimental results on two real-world underwater image datasets show that our approach can successfully enhance underwater images, and achieves competitive performance in visual quality improvement. The code is available at https://github.com/zhihefang/SFGNet.

cs.CV↗