arXiv subjects

Qing Chen

Publications and source records attributed to Qing Chen.

At least 19 recordsLinked to original sources

arXiv2026

Effects of coherent and incoherent measurement imperfections on multipartite quantum nonlocality and quantum key distribution

Multipartite Bell nonlocality is a central resource for device-independent quantum information protocols, but its practical certification is inevitably affected by imperfect measurements. We analyze how coherent angular misalignment and incoherent outcome flipping affect Bell-value degradation and nonlocality thresholds in $n$-partite GHZ states based on the Mermin, Svetlichny, and Mermin--Ardehali--Belinskii--Klyshko (MABK) inequalities. Coherent misalignment produces periodic angular violation windows whose individual widths shrink with the number of parties. In contrast, incoherent outcome flipping yields a single critical outcome-flipping probability, which increases with $n$ for MABK and the odd-$n$ Mermin inequalities, but decreases with $n$ for the Svetlichny inequality. Connecting the degraded Bell values to asymptotic Devetak--Winter key-rate bounds under a convex-combination attack model shows that secret-key generation imposes stricter constraints on measurement imperfections than nonlocality certification. These results provide quantitative benchmarks for robust multipartite nonlocality certification and key-rate estimation under measurement imperfections.

quant-ph↗

arXiv2026

S-matrix bootstrap bounds on self-interacting dark matter

Self-interacting dark matter turns the structure of galactic halos into a direct requirement on a low-energy scattering amplitude. We show that, for weakly coupled scalar dark matter, this requirement implies a much stronger mass bound on the dark matter particle than partial-wave unitarity alone. Using analyticity, crossing symmetry, locality and partial-wave unitarity, we compute the maximal allowed threshold amplitude with a dispersive primal S-matrix bootstrap, assuming only a weakly coupled EFT below a scale $\Lambda$ and allowing arbitrary UV particle content above $\Lambda$. For the benchmark self-interaction cross section $\sigma_{\rm self}=10^{-24}(M/\mathrm{GeV})\mathrm{cm}^2$, the mass of a generic weakly coupled scalar satisfies $M\lesssim 0.3\,\mathrm{GeV}$ in the controlled EFT regime. If dark matter is a derivative-dominated pseudo-Nambu-Goldstone boson, the mass bound is lowered to the MeV scale or below, depending on the hierarchy $M/\Lambda$.

hep-ph↗

arXiv2026

Optimized Gottesman-Kitaev-Preskill Error Correction via Tunable Preprocessing

The Gottesman-Kitaev-Preskill (GKP) code is a promising bosonic candidate for realizing fault-tolerant quantum computation. Among existing error-correction protocols for GKP code, the Steane-type scheme is a canonical and widely adopted paradigm, yet its intrinsic noise propagation pattern limits further performance improvement. In this work, we propose a preprocessing-based Steane-type (P-Steane) scheme, which introduces a tunable preprocessing stage with squeezing parameters $a$ and $b$ to actively reshape noise propagation, thereby constituting a parameter framework. This framework spans a spectrum of protocols beyond existing methods, reproducing the performance of both the ME-Steane scheme ($a=1$, $b=1$) and the teleportation-based scheme ($a=1/\sqrt{2}$, $b=\sqrt{2}$) as special cases. Crucially, in the small-noise regime and when the data qubit is noisier than the ancilla qubits, P-Steane scheme achieves the minimum product of position- and momentum-quadrature output noise variances when $2a = b$, and consistently outperforms the ME-Steane scheme within a specific squeezing-parameter range under this condition.

quant-ph↗

arXiv2026

Beyond Input-Output: Rethinking Creativity through Design-by-Analogy in Human-AI Collaboration

While the proliferation of foundation models has significantly boosted individual productivity, it also introduces a potential challenge: the homogenization of creative content. In response, we revisit Design-by-Analogy (DbA), a cognitively grounded approach that fosters novel solutions by mapping inspiration across domains. However, prevailing perspectives often restrict DbA to early ideation or specific data modalities, while reducing AI-driven design to simplified input-output pipelines. Such conceptual limitations inadvertently foster widespread design fixation. To address this, we expand the understanding of DbA by embedding it into the entire creative process, thereby demonstrating its capacity to mitigate such fixation. Through a systematic review of 85 studies, we identify six forms of representation and classify techniques across seven stages of the creative process. We further discuss three major application domains: creative industries, intelligent manufacturing, and education and services, demonstrating DbA's practical relevance. Building on this synthesis, we frame DbA as a mediating technology for human-AI collaboration and outline the potential opportunities and inherent risks for advancing creativity support in HCI and design research.

cs.HC↗

arXiv2026

Network Nonlocality Sharing in Generalized Star Network from Bipartite Bell Inequalities

This work investigates network nonlocality sharing for a broad class of bipartite Bell inequalities in a generalized star network with an $(n,m,k)$ configuration, comprising $n$ independent branches, $m$ sequential Alices per branch, and $k$ measurement settings per party. On each branch, the intermediate Alices implement optimal weak measurements, whereas the final Alice and the central Bob perform sharp projective measurements. Network nonlocality sharing is witnessed when the quantum values of the network correlations associated with relevant parties simultaneously violate a star-network Bell inequality generated from the given class of bipartite Bell inequalities. We streamline the calculation of the quantum values of the network correlations and derive an analytical expression for the bipartite quantum correlator, valid for arbitrary measurement settings and weak-measurement strengths. The network nonlocality sharing for V\'ertesi inequalities has been studied within the framework, and simultaneous violations are found in $(2,2,6)$ and $(2,2,465)$ cases, with the latter exhibiting greater robustness. Our approach suggests a practical route to studying network nonlocality sharing by utilizing diverse bipartite Bell inequalities beyond the commonly used CHSH-type constructions.

quant-ph↗

arXiv2025

Extending SLUSCHI for Automated Diffusion Calculations

We present an extension of the SLUSCHI package (Solid and Liquid in Ultra Small Coexistence with Hovering Interfaces) to enable automated diffusion calculations from first-principles molecular dynamics. While the original SLUSCHI workflow was designed for melting temperature estimation via solid-liquid coexistence, we adapt its input and output handling to isolate the volume search stage and generate one production trajectory suitable for diffusion analysis. Post-processing tools parse VASP outputs, compute mean-square displacements (MSD), and extract tracer diffusivities using the Einstein relation with robust error estimates through block averaging. Diagnostic plots, including MSD curves, running slopes, and velocity autocorrelations, are produced automatically to help identify diffusive regimes. The method has been validated through representative case studies: self- and inter-diffusion in Al-Cu liquid alloys, sublattice melting in Li_7La_3Zr_2O_12 and Er_2O_3, interstitial oxygen transport in bcc and fcc Fe, and oxygen diffusivity in Fe-O liquids with variable Si and Al contents. Viscosity and diffusivity are linked through the Stokes-Einstein relation, with composition dependence assessed via simple linear mixing. This capability broadens SLUSCHI from melting-point predictions to transport property evaluation, enabling high-throughput, fully first-principles datasets of diffusion coefficients and viscosities across metals and oxides.

cond-mat.mtrl-sci↗

arXiv2025

Effective Theory for Light Portal Dark Matter Detection

We develop a general framework for the computation of light portal dark matter direct detection, incorporating a consistent treatment of finite momentum transfer. In this framework, dark matter interacts with Standard Model matter through a light mediator, which simultaneously serves as the force carrier for dark matter self-interaction, potentially with a distinct coupling strength. The corresponding effective theory relevant for detecting this class of dark matter is systematically constructed. Our analysis focuses on light (semi)relativistic dark matter, which may originate from cosmic-ray boosting and can be probed in high threshold experiments such as large-volume neutrino detectors. In this context, the nucleon matrix elements of the effective operators at finite momentum transfer are required, made available through recent advances in lattice QCD and related nonperturbative methods. The relativistic Fermi gas model is used to convert the nucleon-level momentum transfer to the nuclear level, thereby incorporating nuclear effects pertinent to heavy target experiments. To demonstrate the utility of the framework, we present ultraviolet-complete examples featuring spin-1 and spin-2 portal dark matter. For these models, we compute the differential cross sections with respect to momentum transfer, adopting parameter choices that address the so-called core-cusp problem in astrophysical observations via dark matter self-interactions.

hep-ph↗

arXiv2025

An Efficient, Reliable and Observable Collective Communication Library in Large-scale GPU Training Clusters

Large-scale LLM training requires collective communication libraries to exchange data among distributed GPUs. As a company dedicated to building and operating large-scale GPU training clusters, we encounter several practical limitations of NCCL in production, including 1) SM competition between computation and communication, 2) expensive restart costs under link failures, and 3) insufficient observability of transient collective communication anomalies. To address these challenges, we propose VCCL, an efficient, reliable, and observable collective communication library in large-scale GPU training clusters. VCCL removes SM-consuming P2P kernels by moving intra-node data movement and stream dependency enforcement to CPU threads and GPU copy engines. VCCL also introduces a primary-backup QP mechanism to tolerate frequent NIC port failures, and designs a window-based monitor to observe network anomalies at O({\mu}s) level. We opensource VCCL and deploy it in production training clusters for several months. Compared with NCCL, VCCL improves training throughput by up to 5.28% and reduces massive GPU resource wastage through runtime fault tolerance and finegrained monitor. We also share experience and lessons we learned during the deployment of VCCL in large-scale clusters.

cs.DC↗

arXiv2025

Molecular-Size Control of Properties of Therapeutic Nano-Paper Allows for Selective Drug Storage in Small Doses

A novel concept of nano-scaled interwoven templates for drug delivery with alternating hydro- and lipophilicity properties is introduced. They are built from cellulose and peptide hydrogel in tandem, and characterized by a nano-stacked interwoven design, thus enabling for tuning the lipophilicity in the mesh nano-domains in which drug candidates of complementary lipophilicities can be embedded. This allows for low-dose-controlled consumption and therapeutic applications. Time-resolved and in-situ grazing incidence X-ray scattering studies confirm the design of the therapeutic nano-paper and create conditions suitable for the drug storage of complementary properties. The molecular design has the potential of a locally controlled, site-specific drug release on a beyond-nanomolar scale. Generalized, the design may contribute to facile developments of personalized medicine.

physics.med-ph↗

arXiv2025

PathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Tasks

The complexity and variability inherent in high-resolution pathological images present significant challenges in computational pathology. While pathology foundation models leveraging AI have catalyzed transformative advancements, their development demands large-scale datasets, considerable storage capacity, and substantial computational resources. Furthermore, ensuring their clinical applicability and generalizability requires rigorous validation across a broad spectrum of clinical tasks. Here, we present PathOrchestra, a versatile pathology foundation model trained via self-supervised learning on a dataset comprising 300K pathological slides from 20 tissue and organ types across multiple centers. The model was rigorously evaluated on 112 clinical tasks using a combination of 61 private and 51 public datasets. These tasks encompass digital slide preprocessing, pan-cancer classification, lesion identification, multi-cancer subtype classification, biomarker assessment, gene expression prediction, and the generation of structured reports. PathOrchestra demonstrated exceptional performance across 27,755 WSIs and 9,415,729 ROIs, achieving over 0.950 accuracy in 47 tasks, including pan-cancer classification across various organs, lymphoma subtype diagnosis, and bladder cancer screening. Notably, it is the first model to generate structured reports for high-incidence colorectal cancer and diagnostically complex lymphoma-areas that are infrequently addressed by foundational models but hold immense clinical potential. Overall, PathOrchestra exemplifies the feasibility and efficacy of a large-scale, self-supervised pathology foundation model, validated across a broad range of clinical-grade tasks. Its high accuracy and reduced reliance on extensive data annotation underline its potential for clinical integration, offering a pathway toward more efficient and high-quality medical services.

cs.CV↗

arXiv2025

Probing cellular activity via charge-sensitive quantum nanoprobes

Nitrogen-vacancy (NV) based quantum sensors hold great potential for real-time single-cell sensing with far-reaching applications in fundamental biology and medical diagnostics. Although highly sensitive, the mapping of quantum measurements onto cellular physiological states has remained an exceptional challenge. Here we introduce a novel quantum sensing modality capable of detecting changes in cellular activity. Our approach is based on the detection of environment-induced charge depletion within an individual particle that, owing to a previously unaccounted transverse dipole term, induces systematic shifts in the zero-field splitting (ZFS). Importantly, these charge-induced shifts serve as a reliable indicator for lipopolysaccharide (LPS)-mediated inflammatory response in macrophages. Furthermore, we demonstrate that surface modification of our diamond nanoprobes effectively suppresses these environment-induced ZFS shifts, providing an important tool for differentiating electrostatic shifts caused by the environment from other unrelated effects, such as temperature variations. Notably, this surface modification also leads to significant reductions in particle-induced toxicity and inflammation. Our findings shed light on systematic drifts and sensitivity limits of NV spectroscopy in a biological environment with ramification on the critical discussion surrounding single-cell thermogenesis. Notably, this work establishes the foundation for a novel sensing modality capable of probing complex cellular processes through straightforward physical measurements.

physics.app-ph↗

arXiv2025

An experimental comparison of tree-data structures for connectivity queries on fully-dynamic undirected graphs (Extended Version)

During the past decades significant efforts have been made to propose data structures for answering connectivity queries on fully dynamic graphs, i.e., graphs with frequent insertions and deletions of edges. However, a comprehensive understanding of how these data structures perform in practice is missing, since not all of them have been implemented, let alone evaluated experimentally. We provide reference implementations for the proposed data structures and experimentally evaluate them on a wide range of graphs. Our findings show that the current solutions are not ready to be deployed in systems as is, as every data structure has critical weaknesses when used in practice. Key limitations that must be overcome are the space and time overhead incurred by balanced data structures, the degeneration of the runtime of space-efficient data structures in worst case scenarios, and the maintenance costs for balanced data structures. We detail our findings in the experimental evaluation and provide recommendations for implementing robust solutions for answering connectivity queries on dynamic graphs.

cs.DB↗

arXiv2024

Accelerating ab initio melting property calculations with machine learning: Application to the high entropy alloy TaVCrW

Melting properties are critical for designing novel materials, especially for discovering high-performance, high-melting refractory materials. Experimental measurements of these properties are extremely challenging due to their high melting temperatures. Complementary theoretical predictions are, therefore, indispensable. The conventional free energy approach using density functional theory (DFT) has been a gold standard for such purposes because of its high accuracy. However,it generally involves expensive thermodynamic integration using ab initio molecular dynamic simulations. The high computational cost makes high-throughput calculations infeasible. Here, we propose a highly efficient DFT-based method aided by a specially designed machine learning potential. As the machine learning potential can closely reproduce the ab initio phase space, even for multi-component alloys, the costly thermodynamic integration can be fully substituted with more efficient free energy perturbation calculations. The method achieves overall savings of computational resources by 80% compared to current alternatives. We apply the method to the high-entropy alloy TaVCrW and calculate its melting properties, including melting temperature, entropy and enthalpy of fusion, and volume change at the melting point. Additionally, the heat capacities of solid and liquid TaVCrW are calculated. The results agree reasonably with the calphad extrapolated values.

cond-mat.mtrl-sci↗

arXiv2024

Super-concentrated alkali hydroxide electrolytes for rechargeable Zn batteries

Rechargeable Zn batteries offer safe, inexpensive energy storage, but when deeply discharged to compete with lithium-ion batteries, they are plagued by parasitic reactions at the Zn anodes. We apply super-concentrated alkaline electrolytes to suppress two key parasitic reactions, hydrogen evolution and ZnO passivation. An electrolyte with 15 M KOH displays a broad electrochemical window (>2.5 V on Au), a high ZnO solubility (>1.5 M), and an exceptionally high ionic conductivity (>0.27 S/cm at 25 C). Spectroscopies and ab-initio molecular dynamics simulation suggest K+-OH- pairs and a tightened water network to underpin the stability. The simulation further reveals unique triggered proton hopping that offsets the lack of water wires to sustain the conductivity. Low hydrogen evolution, confirmed via online mass spectroscopy, and slow passivation enable a NiOOH||Zn battery to deliver a cumulative capacity of 8.4 Ah cm-2 and a Zn-air battery to last for over 110 hours.

cond-mat.mtrl-sci↗

arXiv2024

Beyond Numbers: Creating Analogies to Enhance Data Comprehension and Communication with Generative AI

Unfamiliar measurements usually hinder readers from grasping the scale of the numerical data, understanding the content, and feeling engaged with the context. To enhance data comprehension and communication, we leverage analogies to bridge the gap between abstract data and familiar measurements. In this work, we first conduct semi-structured interviews with design experts to identify design problems and summarize design considerations. Then, we collect an analogy dataset of 138 cases from various online sources. Based on the collected dataset, we characterize a design space for creating data analogies. Next, we build a prototype system, AnalogyMate, that automatically suggests data analogies, their corresponding design solutions, and generated visual representations powered by generative AI. The study results show the usefulness of AnalogyMate in aiding the creation process of data analogies and the effectiveness of data analogy in enhancing data comprehension and communication.

cs.HC↗

arXiv2024

Leveraging Foundation Models for Crafting Narrative Visualization: A Survey

Narrative visualization transforms data into engaging stories, making complex information accessible to a broad audience. Foundation models, with their advanced capabilities such as natural language processing, content generation, and multimodal integration, hold substantial potential for enriching narrative visualization. Recently, a collection of techniques have been introduced for crafting narrative visualizations based on foundation models from different aspects. We build our survey upon 66 papers to study how foundation models can progressively engage in this process and then propose a reference model categorizing the reviewed literature into four essential phases: Analysis, Narration, Visualization, and Interaction. Furthermore, we identify eight specific tasks (e.g. Insight Extraction and Authoring) where foundation models are applied across these stages to facilitate the creation of visual narratives. Detailed descriptions, related literature, and reflections are presented for each task. To make it a more impactful and informative experience for diverse readers, we discuss key research problems and provide the strengths and weaknesses in each task to guide people in identifying and seizing opportunities while navigating challenges in this field.

cs.HC↗

arXiv2023

The space of germs of extremal K\" ahler metrics in one dimension comprises three distinct ${\Bbb R}^3$ components

In the 1980s, Eugenio Calabi introduced the concept of {\it extremal K\" ahler metrics} as critical points of the $L^2$-norm functional of scalar curvature in the space of K\" ahler metrics belonging to a fixed K\"ahler class of a compact complex manifold $X$. Calabi demonstrated that extremal K\" ahler metrics always degenerate into Einstein metrics on compact Riemann surfaces. We define a K\"ahler metric $g$ on a domain of ${\Bbb C}^n$ as a {\it local extremal K\"ahler metric} of dimension $n$ if it satisfies the Euler-Lagrange equation of this functional, i.e. holomorphic is the $(1,0)$-part of the gradient vector field of the scalar curvature of $g$, in the domain. Our main result establishes that the space of all germs of local extremal, non-Einstein K\"ahler metrics of dimension one comprises three components, each diffeomorphic to ${\Bbb R}^3$.

math.DG↗

arXiv2023

Bi-gravity Portal Dark Matter

We consider a model where the interaction between dark matter and the Standard Model particles are mediated by a ghost-free bi-gravity portal. The bi-gravity model invokes a massive spin-2 particle coupled to the usual massless graviton as well as generic bi-metric matter couplings. The cross-sections for dark matter direct detection are computed and confronted with the experimental bounds. The presence of the massive spin-2 mediator resolves the core-cusp problem, which in turn significantly constrains the dark matter coupling in such a bi-gravity theory. Yet, there remains a window of the parameter space where the model can be tested in the upcoming direct detection experiments such as XENONnT and PandaX-30T. The model also predicts a reheating temperature of the order of $10^6$ GeV.

hep-ph↗