arXiv subjects

Hanqing Liu

Publications and source records attributed to Hanqing Liu.

At least 37 records · Page 2Linked to original sources

arXiv2026

Single-Photon Advantage in Quantum Cryptography Beyond QKD

Quantum key distribution (QKD) can be used to establish a secret key between trusted parties. Many practical use-cases in communication networks, however, involve parties who do not trust each other. A fundamental cryptographic building block for such distrustful scenarios is quantum coin flipping, which has been investigated only in few experimental studies to date, all of which used probabilistic quantum light sources imposing fundamental limitations. Here, we experimentally implement a quantum strong coin flipping protocol using single-photon states and demonstrate a quantum advantage compared to both classical realizations and implementations using faint laser pulses. We achieve this by employing a state-of-the-art deterministic quantum dot light source in combination with fast, random polarization-state encoding enabling sufficiently low quantum bit error ratio. By demonstrating a single-photon quantum advantage in a cryptographic primitive beyond QKD, our work represents a major advance towards the implementation of complex cryptographic tasks in a future quantum internet.

quant-ph↗

arXiv2026

A Fiber-pigtailed Quantum Dot Device Generating Indistinguishable Photons at GHz Clock-rates

Solid-state quantum light sources based on semiconductor quantum dots (QDs) are increasingly employed in photonic quantum information applications. Especially when moving towards real-world scenarios outside shielded lab environments, the efficient and robust coupling of nanophotonic devices to single-mode optical fibers offers substantial advantage by enabling "plug-and-play" operation. In this work we present a fiber-pigtailed cavity-enhanced source of flying qubits emitting single indistinguishable photons at clock-rates exceeding $1\,$GHz. This is achieved by employing a fully deterministic technique for fiber-pigtailing optimized QD-devices based on hybrid circular Bragg grating (hCBG) micro-cavities. The fabricated fiber-pigtailed hCBGs feature radiative emission lifetimes of $<80\,$ps, corresponding to a Purcell factor of $\sim$9, a suppression of multi-photon emission events with $g^{(2)}(0)<1\%$, a photon-indistinguishability $>80\%$ and a measured single-photon coupling efficiency of 53$\%$ in a high numerical aperture single-mode fiber, corresponding to 1.2 Megaclicks per second at the single-photon detectors under $80\,$MHz excitation clock-rates. Furthermore, we show that high multi-photon suppression and indistinguishability prevail for excitation clock-rates exceeding $1\,$GHz. Our results show that Purcell-enhanced fiber-pigtailed quantum light sources based on hCBG cavities are a prime candidate for applications of quantum information science.

quant-ph↗

arXiv2026

Passive Daytime Radiative Cooling Enabled by Bio-Derived Ceramic-Polymer Coatings on Rapid-Curing Fiberglass Casts

Passive daytime radiative cooling (PDRC) provides an energy-free approach to suppress surface temperatures by reflecting solar irradiation while emitting thermal radiation through the mid-infrared atmospheric window. Despite rapid progress in optical performance, most PDRC systems remain limited by rigid, fragile, or planar substrates, restricting their use on flexible, curved, or wearable surfaces. Here, we report a biocompatible and structurally robust PDRC system integrated onto a commercial rapid-curing fiberglass cast, a conformal substrate widely used in orthopedic and industrial applications. The cooling architecture adopts a bilayer polymer design consisting of a polyvinyl alcohol (PVA) adhesion layer and a polymethyl methacrylate (PMMA) protective layer, both embedded with calcium pyrophosphate (CPP) ceramic particles derived from processed animal bone waste. The bio-derived CPP simultaneously enables broadband solar scattering and high mid-infrared emittance, while offering sustainability and biocompatibility advantages. The resulting composite exhibits over 90% solar reflectance and achieves up to 15 C sub-ambient cooling under direct outdoor sunlight.

physics.optics↗

arXiv2025

Bell Inequality Violation with Vacuum-One-Photon Number Superposition States

Entanglement is a central resource in quantum technologies, and the realization of photonic entanglement necessarily relies on interaction with matter. Resonance fluorescence (RF), originating from the coherent interaction between a driving field and a two-level system, plays a pivotal role in quantum optics. Here, we demonstrate a novel route to entanglement generation based on RF from a single quantum dot. Rather than relying on generation of multiphoton states, our approach directly exploits vacuum-one-photon number superposition states created under resonant excitation. By delocalizing this superposition via a beam splitter, we realize time-bin entanglement and observe a clear violation of the Clauser-Horn-Shimony-Holt Bell inequality using Franson-type interferometry. Our scheme removes the need for multiphoton generation, simplifies the experimental requirements, and establishes a scalable pathway toward solid-state entangled photon sources.

quant-ph↗

arXiv2025

PEOD: A Pixel-Aligned Event-RGB Benchmark for Object Detection under Challenging Conditions

Robust object detection for challenging scenarios increasingly relies on event cameras, yet existing Event-RGB datasets remain constrained by sparse coverage of extreme conditions and low spatial resolution (<= 640 x 480), which prevents comprehensive evaluation of detectors under challenging scenarios. To address these limitations, we propose PEOD, the first large-scale, pixel-aligned and high-resolution (1280 x 720) Event-RGB dataset for object detection under challenge conditions. PEOD contains 130+ spatiotemporal-aligned sequences and 340k manual bounding boxes, with 57% of data captured under low-light, overexposure, and high-speed motion. Furthermore, we benchmark 14 methods across three input configurations (Event-based, RGB-based, and Event-RGB fusion) on PEOD. On the full test set and normal subset, fusion-based models achieve the excellent performance. However, in illumination challenge subset, the top event-based model outperforms all fusion models, while fusion models still outperform their RGB-based counterparts, indicating limits of existing fusion methods when the frame modality is severely degraded. PEOD establishes a realistic, high-quality benchmark for multimodal perception and facilitates future research.

cs.CV↗

arXiv2025

Parameter-Free Fine-tuning via Redundancy Elimination for Vision Foundation Models

Vision foundation models (VFMs) have demonstrated remarkable capabilities in learning universal visual representations. However, adapting these models to downstream tasks conventionally requires parameter updates, with even parameter-efficient fine-tuning methods necessitating the modification of thousands to millions of weights. In this paper, we investigate the redundancies in the segment anything model (SAM) and then propose a novel parameter-free fine-tuning method. Unlike traditional fine-tuning methods that adjust parameters, our method emphasizes selecting, reusing, and enhancing pre-trained features, offering a new perspective on fine-tuning foundation models. Specifically, we introduce a channel selection algorithm based on the model's output difference to identify redundant and effective channels. By selectively replacing the redundant channels with more effective ones, we filter out less useful features and reuse more task-irrelevant features to downstream tasks, thereby enhancing the task-specific feature representation. Experiments on both out-of-domain and in-domain datasets demonstrate the efficiency and effectiveness of our method in different vision tasks (e.g., image segmentation, depth estimation and image classification). Notably, our approach can seamlessly integrate with existing fine-tuning strategies (e.g., LoRA, Adapter), further boosting the performance of already fine-tuned models. Moreover, since our channel selection involves only model inference, our method significantly reduces GPU memory overhead.

cs.CV↗

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

Optomechanical method for characterizing thermal transport across van der Waals interfaces

For the development of nanoscale electronics and photonics using atomically thin two-dimensional (2D) materials, it is important to realize van der Waals (vdW) interfaces with low thermal resistance, to minimize performance reduction caused by heat accumulation. However, characterizing the thermal interface resistance between vdW materials is still a challenge. Here, we introduce a novel optomechanical methodology to characterize the thermal transport across interfaces in 2D heterostructures. We first determine the specific heat and thermal conductivity as the function of temperature for the upper and lower material layers separately and then extract the thermal boundary conductance (TBC) of the heterostructure from its thermal time constant. We obtain a TBC of $2.41 \pm 1.03$ and $4.14 \pm 1.74$~\si{MW m^{2} K^{-1}} for FePS$_3$/WSe$_2$ and MoS$_2$/FePS$_3$ interfaces, respectively, which are comparable to values reported in the literature. Moreover, they agree with a Debye model including the acoustic impedance mismatch of flexural phonons. This work enables efficient thermal management down to the nanoscale and offers new insights into energy dissipation in vdW heterostructures.

physics.app-ph↗

arXiv2025

Mechanical Reinforcement of Graphene via Wrinkling

Mechanical cantilevers are central to nanotechnology, with ultimate sensitivity achieved at the atomic limit, where low bending rigidity makes stability the fundamental challenge. Here, we introduce a wrinkle-induced stiffening approach that enhances the bending rigidity of monolayer graphene by several orders of magnitude, enabling the fabrication of mechanically robust graphene cantilevers. When suspended over microcavities, these wrinkled membranes exhibit significant increases in both in-plane and out-of-plane stiffness, as confirmed by nanoindentation and resonance measurements, which also reveal that enhanced bending rigidity strongly influences their vibrational response. This behavior marks a transition from tension-dominated mechanics to a regime where bending effects become prominent, even in a single atomic layer. By sculpting these structures, we realize graphene cantilevers with measured bending rigidities between $10^6$ and $10^7$ eV, while maintaining femtogram-scale mass. These findings open new directions in nanomechanical sensing and cantilever-based technologies.

cond-mat.mes-hall↗

arXiv2025

Fast Adversarial Training with Weak-to-Strong Spatial-Temporal Consistency in the Frequency Domain on Videos

Adversarial Training (AT) has been shown to significantly enhance adversarial robustness via a min-max optimization approach. However, its effectiveness in video recognition tasks is hampered by two main challenges. First, fast adversarial training for video models remains largely unexplored, which severely impedes its practical applications. Specifically, most video adversarial training methods are computationally costly, with long training times and high expenses. Second, existing methods struggle with the trade-off between clean accuracy and adversarial robustness. To address these challenges, we introduce Video Fast Adversarial Training with Weak-to-Strong consistency (VFAT-WS), the first fast adversarial training method for video data. Specifically, VFAT-WS incorporates the following key designs: First, it integrates a straightforward yet effective temporal frequency augmentation (TF-AUG), and its spatial-temporal enhanced form STF-AUG, along with a single-step PGD attack to boost training efficiency and robustness. Second, it devises a weak-to-strong spatial-temporal consistency regularization, which seamlessly integrates the simpler TF-AUG and the more complex STF-AUG. Leveraging the consistency regularization, it steers the learning process from simple to complex augmentations. Both of them work together to achieve a better trade-off between clean accuracy and robustness. Extensive experiments on UCF-101 and HMDB-51 with both CNN and Transformer-based models demonstrate that VFAT-WS achieves great improvements in adversarial robustness and corruption robustness, while accelerating training by nearly 490%.

cs.CV↗

arXiv2025

Universality of $SU(\infty)$ relaxation dynamics for $SU(n_f)$-symmetric spin-models

Spin-models, where the $N$ spins interact pairwise with a $SU(n_f)$ symmetry preserving hamiltonian, famously simplify in the large $n_f$, $N$ limits, as derived by Sachdev and Ye when exploring mean-field behavior of spin-glasses. We present numerical evidence that for a large class of models, the large $n_f$ limit is not necessary: the same dynamical equations can describe the relaxation processes at high temperatures for a set of classical models inspired from mean-field treatments of interacting dense neutrino gases, up to times set by the radius of convergence of the perturbation series for the correlation function. After a simple rescaling of time, the dynamics display a surprising universality, being identical for any value of $n_f$ as long as the rank of the coupling matrix is small. As a corollary of our results, we find that the direct interaction approximation originating from the study of stochastic flows in fluid turbulence should be thought of as only a short-time approximation for generic random coupling systems.

hep-th↗

arXiv2025

scAgent: Universal Single-Cell Annotation via a LLM Agent

Cell type annotation is critical for understanding cellular heterogeneity. Based on single-cell RNA-seq data and deep learning models, good progress has been made in annotating a fixed number of cell types within a specific tissue. However, universal cell annotation, which can generalize across tissues, discover novel cell types, and extend to novel cell types, remains less explored. To fill this gap, this paper proposes scAgent, a universal cell annotation framework based on Large Language Models (LLMs). scAgent can identify cell types and discover novel cell types in diverse tissues; furthermore, it is data efficient to learn novel cell types. Experimental studies in 160 cell types and 35 tissues demonstrate the superior performance of scAgent in general cell-type annotation, novel cell discovery, and extensibility to novel cell type.

cs.CL↗

arXiv2025

When Lighting Deceives: Exposing Vision-Language Models' Illumination Vulnerability Through Illumination Transformation Attack

Vision-Language Models (VLMs) have achieved remarkable success in various tasks, yet their robustness to real-world illumination variations remains largely unexplored. To bridge this gap, we propose \textbf{I}llumination \textbf{T}ransformation \textbf{A}ttack (\textbf{ITA}), the first framework to systematically assess VLMs' robustness against illumination changes. However, there still exist two key challenges: (1) how to model global illumination with fine-grained control to achieve diverse lighting conditions and (2) how to ensure adversarial effectiveness while maintaining naturalness. To address the first challenge, we innovatively decompose global illumination into multiple parameterized point light sources based on the illumination rendering equation. This design enables us to model more diverse lighting variations that previous methods could not capture. Then, by integrating these parameterized lighting variations with physics-based lighting reconstruction techniques, we could precisely render such light interactions in the original scenes, finally meeting the goal of fine-grained lighting control. For the second challenge, by controlling illumination through the lighting reconstrution model's latent space rather than direct pixel manipulation, we inherently preserve physical lighting priors. Furthermore, to prevent potential reconstruction artifacts, we design additional perceptual constraints for maintaining visual consistency with original images and diversity constraints for avoiding light source convergence. Extensive experiments demonstrate that our ITA could significantly reduce the performance of advanced VLMs, e.g., LLaVA-1.6, while possessing competitive naturalness, exposing VLMS' critical illuminiation vulnerabilities.

cs.CV↗

arXiv2025

AdvDreamer Unveils: Are Vision-Language Models Truly Ready for Real-World 3D Variations?

Vision Language Models (VLMs) have exhibited remarkable generalization capabilities, yet their robustness in dynamic real-world scenarios remains largely unexplored. To systematically evaluate VLMs' robustness to real-world 3D variations, we propose AdvDreamer, the first framework capable of generating physically reproducible Adversarial 3D Transformation (Adv-3DT) samples from single-view observations. In AdvDreamer, we integrate three key innovations: Firstly, to characterize real-world 3D variations with limited prior knowledge precisely, we design a zero-shot Monocular Pose Manipulation pipeline built upon generative 3D priors. Secondly, to ensure the visual quality of worst-case Adv-3DT samples, we propose a Naturalness Reward Model that provides continuous naturalness regularization during adversarial optimization, effectively preventing convergence to hallucinated or unnatural elements. Thirdly, to enable systematic evaluation across diverse VLM architectures and visual-language tasks, we introduce the Inverse Semantic Probability loss as the adversarial optimization objective, which solely operates in the fundamental visual-textual alignment space. Based on the captured Adv-3DT samples with high aggressiveness and transferability, we establish MM3DTBench, the first VQA benchmark dataset tailored to evaluate VLM robustness under challenging 3D variations. Extensive evaluations of representative VLMs with varying architectures reveal that real-world 3D variations can pose severe threats to model performance across various tasks.

cs.CV↗

arXiv2025

Full Polarization Control of Photons with Evanescent Wave Coupling in the Ultra Subwavelength Gap of Photonic Molecules

Polarization of photons plays a key role in quantum optics and light-matter interactions, however, it is difficult to control in nanosystems since the eigenstate of a nanophotonic cavity is usually fixed and linearly polarized. Here we reveal polarization control of photons using photonic molecules (PMs) that host supermodes of two coupled nanobeam cavities. In contrast to conventional PMs in a 2D photonic crystal slab, for the two 1D photonic crystal nanobeam cavities the shift and gap between them can be tuned continuously. With an ultra subwavelength gap, the coupling between the two cavities is dominated by the evanescent wave coupling in the surrounding environment, rather not the emission wave coupling for conventional PMs. As such, non-Hermiticity of the system becomes pronounced, and the supermodes consist of a non-trivial phase difference between bare eigenstates that supports elliptical polarization. We observe that both the polarization degree and polarization angle of the antisymmetric mode strongly depend on the shift and gap between the two cavities, exhibiting polarization states from linear to circular. This full polarization control indicates great potential of PMs in quantum optical devices and spin-resolved cavity quantum electrodynamics.

physics.optics↗

arXiv2025

Coherence in resonance fluorescence

Resonance fluorescence of a two-level emitter displays persistently anti-bunching irrespective of the excitation intensity, but inherits the driving laser's linewidth under weak monochromatic excitation. These properties are commonly explained in terms of two disjoined pictures, i.e., the emitter's single photon saturation or passively scattering light. Here, we propose a unified model that treats all fluorescence photons as spontaneous emission, one at a time, and can explain simultaneously both the spectral and correlation properties of the emission. We theoretically derive the excitation power dependencies, measurable at the single-photon incidence level, of the first-order coherence of the whole resonance fluorescence and super-bunching of the spectrally filtered, followed by experimental confirmation on a semiconductor quantum dot micro-pillar device. Furthermore, our model explains peculiar coincidence bunching observed in phase-dependent two-photon interference experiments. Our work provides an intuitive understanding of coherent light-matter interaction and may stimulate new applications.

quant-ph↗

arXiv2024

Global Challenge for Safe and Secure LLMs Track 1

This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks. With the increasing integration of LLMs in critical sectors such as healthcare, finance, and public administration, ensuring these models are resilient to adversarial attacks is vital for preventing misuse and upholding ethical standards. This competition focused on two distinct tracks designed to evaluate and enhance the robustness of LLM security frameworks. Track 1 tasked participants with developing automated methods to probe LLM vulnerabilities by eliciting undesirable responses, effectively testing the limits of existing safety protocols within LLMs. Participants were challenged to devise techniques that could bypass content safeguards across a diverse array of scenarios, from offensive language to misinformation and illegal activities. Through this process, Track 1 aimed to deepen the understanding of LLM vulnerabilities and provide insights for creating more resilient models.

cs.CR↗

arXiv2024

Boosting Jailbreak Transferability for Large Language Models

Large language models have drawn significant attention to the challenge of safe alignment, especially regarding jailbreak attacks that circumvent security measures to produce harmful content. To address the limitations of existing methods like GCG, which perform well in single-model attacks but lack transferability, we propose several enhancements, including a scenario induction template, optimized suffix selection, and the integration of re-suffix attack mechanism to reduce inconsistent outputs. Our approach has shown superior performance in extensive experiments across various benchmarks, achieving nearly 100% success rates in both attack execution and transferability. Notably, our method has won the first place in the AISG-hosted Global Challenge for Safe and Secure LLMs. The code is released at https://github.com/HqingLiu/SI-GCG.

cs.AI↗