arXiv subjects

Yue Luo

Publications and source records attributed to Yue Luo.

At least 19 recordsLinked to original sources

arXiv2026

Entanglement-based quantum key distribution with data in hollow-core fiber

The coexistence of quantum information and classical signals in a single fiber is essential for future quantum networks that leverage the well-established optical fiber infrastructure. Although multiplexing technologies can separate quantum and classical signals, pure silica core fibers (PSCFs) remain fundamentally limited by the high nonlinearity, which generates substantial Raman scattering and four-wave mixing noise. Hollow-core fibers (HCFs), guiding light predominantly in air, offer an attractive solution with intrinsically ultra-low nonlinearity and strongly suppressed nonlinear noise. In this work, we demonstrate the entanglement-based key coexisting with data over an 18-km HCF link. We achieve time-encoded high-dimensional quantum key distribution (HD-QKD) carrying 0 dBm of bidirectional received power, corresponding to a theoretical data capacity of up to 2.3 Tbps. During 24 hours of continuous operation, an average secret key rate (SKR) of 10.56 kbps is obtained. Theoretical analysis further predicts SKRs above 135 kbps over transmission distances exceeding 200 km using state-of-the-art low-loss HCFs. These results show significantly improved performance compared with PSCF-based systems and highlight the potential of HCFs for scalable quantum-classical coexistence compatible with the architectures of established fiber-optic networks.

quant-ph↗

arXiv2026

ARIA: Adaptive Retrieval Intelligence Assistant -- A Multimodal RAG Framework for Domain-Specific Engineering Education

Developing effective, domain-specific educational support systems is central to advancing AI in education. Although large language models (LLMs) demonstrate remarkable capabilities, they face significant limitations in specialized educational applications, including hallucinations, limited knowledge updates, and lack of domain expertise. Fine-tuning requires complete model retraining, creating substantial computational overhead, while general-purpose LLMs often provide inaccurate responses in specialized contexts due to reliance on generalized training data. To address this, we propose ARIA (Adaptive Retrieval Intelligence Assistant), a Retrieval-Augmented Generation (RAG) framework for creating intelligent teaching assistants across university-level courses. ARIA leverages a multimodal content extraction pipeline combining Docling for structured document analysis, Nougat for mathematical formula recognition, and GPT-4 Vision API for diagram interpretation, with the e5-large-v2 embedding model for high semantic performance and low latency. This enables accurate processing of complex educational materials while maintaining pedagogical consistency through engineered prompts and response controls. We evaluate ARIA using lecture material from Statics and Mechanics of Materials, a sophomore-level civil engineering course at Johns Hopkins University, benchmarking against ChatGPT-5. Results demonstrate 97.5% accuracy in domain-specific question filtering and superior pedagogical performance. ARIA correctly answered all 20 relevant course questions while rejecting 58 of 60 non-relevant queries, achieving 90.9% precision, 100% recall, and 4.89/5.0 average response quality. These findings demonstrate that ARIA's course-agnostic architecture represents a scalable framework for domain-specific educational AI deployment.

cs.IR↗

arXiv2025

Roadmap: 2D Materials for Quantum Technologies

Two-dimensional (2D) materials have emerged as a versatile and powerful platform for quantum technologies, offering atomic-scale control, strong quantum confinement, and seamless integration into heterogeneous device architectures. Their reduced dimensionality enables unique quantum phenomena, including optically addressable spin defects, tunable single-photon emitters, low-dimensional magnetism, gate-controlled superconductivity, and correlated states in Moir\'e superlattices. This Roadmap provides a comprehensive overview of recent progress and future directions in exploiting 2D materials for quantum sensing, computation, communication, and simulation. We survey advances spanning spin defects and quantum sensing, quantum emitters and nonlinear photonics, computational theory and data-driven discovery of quantum defects, spintronic and magnonic devices, cavity-engineered quantum materials, superconducting and hybrid quantum circuits, quantum dots, Moir\'e quantum simulators, and quantum communication platforms. Across these themes, we identify common challenges in defect control, coherence preservation, interfacial engineering, and scalable integration, alongside emerging opportunities driven by machine$-$learning$-$assisted design and integrated experiment$-$theory feedback loops. By connecting microscopic quantum states to mesoscopic excitations and macroscopic device architectures, this Roadmap outlines a materials-centric framework for integrating coherent quantum functionalities and positions 2D materials as foundational building blocks for next-generation quantum technologies.

quant-ph↗

arXiv2025

Hi-Reco: High-Fidelity Real-Time Conversational Digital Humans

High-fidelity digital humans are increasingly used in interactive applications, yet achieving both visual realism and real-time responsiveness remains a major challenge. We present a high-fidelity, real-time conversational digital human system that seamlessly combines a visually realistic 3D avatar, persona-driven expressive speech synthesis, and knowledge-grounded dialogue generation. To support natural and timely interaction, we introduce an asynchronous execution pipeline that coordinates multi-modal components with minimal latency. The system supports advanced features such as wake word detection, emotionally expressive prosody, and highly accurate, context-aware response generation. It leverages novel retrieval-augmented methods, including history augmentation to maintain conversational flow and intent-based routing for efficient knowledge access. Together, these components form an integrated system that enables responsive and believable digital humans, suitable for immersive applications in communication, education, and entertainment.

cs.CV↗

arXiv2025

Uncertainty on Display: The Effects of Communicating Confidence Cues in Autonomous Vehicle-Pedestrian Interactions

Uncertainty is an inherent aspect of autonomous vehicle (AV) decision-making, yet it is rarely communicated to pedestrians, which hinders transparency. This study investigates how AV uncertainty can be conveyed through two approaches: explicit communication (confidence percentage displays) and implicit communication (vehicle motion cues), across different confidence levels (high and low). Through a within-subject VR experiment (N=26), we evaluated these approaches in a crossing scenario, assessing interface qualities (visibility and intuitiveness), how well the information conveyed the vehicle's level of confidence, and their impact on participants' perceived safety, trust, and user experience. Our results show that explicit communication is more effective and preferred for conveying uncertainty, enhancing safety, trust, and user experience. Conversely, implicit communication introduces ambiguity, especially when AV confidence is low. This research provides empirical insights into how uncertainty communication shapes pedestrian interpretation of AV behaviour and offer design guidance for external interfaces that integrate uncertainty as a communicative element.

cs.HC↗

arXiv2025

Toward AI standardization: A triadic human-ai collaboration framework for multi-level autonomous mobility

The goal of the current study is to introduce a triadic human-AI collaboration framework that could be applied in transportation systems such as automated vehicles, micromobility systems, and vehicle teleoperation. Previous standards, such as SAE Levels of Automation, have focused on defining automation levels based on who controls the vehicle. However, it is still not clear how human users and AI should collaborate in real time, especially in dynamic driving contexts where roles can shift frequently. To fill this gap, this study proposed a triadic human-AI collaboration framework with three AI roles: Advisor, Co-Pilot, and Guardian. These roles can dynamically adapt to human needs based on real-time data, such as mental states and environmental conditions. The Advisor AI offers informational support without direct intervention. The Co-Pilot AI provides partial intervention when needed, with the goal of sharing control with humans. The Guardian AI performs emergency overrides if necessary. The use cases for these AI roles in micromobility devices, such as e-scooters, are presented to demonstrate how these roles can influence user preferences and trust. Overall, the study takes a first step toward a universal role-based collaborative framework for AI standardization and explores how AI technologies can be embedded in future transportation systems while considering human interactions.

cs.HC↗

arXiv2025

Development_of_a_novel_high-performance_balanced_homodyne_detector

True random numbers are extracted through measurements of vacuum fluctuations in quantum state components. We propose an improved scheme utilizing an optimization-based simulation methodology to enhance the temporal resolution of quantum state detection and processing efficiency of vacuum fluctuation signals in continuous-variable quantum random number generators (CV-QRNGs), while simultaneously maximizing the entropy content of quantum noise sources. This work presents the first application of optimization simulation methodology to balanced homodyne detector (BHD) circuit design, with particular emphasis on improving high-frequency transmission characteristics. The design framework prioritizes system stability and S-parameter sensitivity to optimize both circuit architecture and critical component parameters. The AC amplifier circuit was implemented through ADS high-frequency simulations using two ABA-52563 RF amplifiers in a cascaded configuration, with circuit modeling performed on Rogers 4350 substrate optimized for high-frequency applications. This approach enabled the development of a switched-configuration BHD featuring: 1) 1.9 GHz bandwidth, 2) 41.5 dB signal-to-noise ratio at 1.75 GHz, 3) 30 dB common-mode rejection ratio at 100 MHz, and 4) frequency response flatness within 1.5 dB across 1.3-1.7 GHz. Additionally, the Husimi function is employed for entropy analysis to reconstruct vacuum state phase-space distributions, validating the detector's quantum measurement fidelity. The implemented system demonstrates a collective generation rate of 20.0504 Gbps across four parallel channels, with all output streams successfully passing NIST SP 800-22 statistical testing requirements.

quant-ph↗

arXiv2025

Optimized quantum entanglement network enabled by a state-multiplexing quantum light source

A fully connected quantum network with a wavelength division multiplexing architecture plays an increasingly pivotal role in quantum information technology. With such architecture, an entanglement-based network has been demonstrated in which an entangled photon-pair source distributes quantum entanglement resources to many users. Despite these remarkable advances, the scalability of the architecture could be constrained by the finite spectrum resource, where O(N^2)wavelength channels are needed to connect N users, thus impeding further progress in real-world scenarios. Here, we propose an optimized scheme for the wavelength division multiplexing entanglement-based network using a state-multiplexing quantum light source. With a dual-pump configuration, the feasibility of our approach is demonstrated by generating state-multiplexing photon pairs at multiple wavelength channels with a silicon nitride microring resonator chip. In our demonstration, we establish a fully connected graph between four users with six wavelength channels - saving half of which without sacrificing functionality and performance of the secure communication. A total asymptotic secure key rate of 1946.9 bps is obtained by performing the BBM92 protocol with the distributed state. The network topology of our method has great potential for developing a scalable quantum network with significantly minimized infrastructure requirements.

quant-ph↗

arXiv2024

Existence, uniqueness and smoothing estimates for spatially homogeneous Landau-Coulomb equation in $H^{-\f12}$ space with polynomial tail

We demonstrate that the spatially homogeneous Landau-Coulomb equation exhibits global existence and uniqueness around the space $H^{-\frac12}_3\cap L^1_{7}\cap L\log L$. Additionally, we furnish several quantitative assessments regarding the smoothing estimates in weighted Sobolev spaces. The new ingredients of the proof lie in the localized techniques in both phase space and frequency space.

math.AP↗

arXiv2024

Assessing Alcohol Use Disorder: Insights from Lifestyle, Background, and Family History with Machine Learning Techniques

This study explored how lifestyle, personal background, and family history contribute to the risk of developing Alcohol Use Disorder (AUD). Survey data from the All of Us Program was utilized to extract information on AUD status, lifestyle, personal background, and family history for 6,016 participants. Key determinants of AUD were identified using decision trees including annual income, recreational drug use, length of residence, sex/gender, marital status, education level, and family history of AUD. Data visualization and Chi-Square Tests of Independence were then used to assess associations between identified factors and AUD. Afterwards, machine learning techniques including decision trees, random forests, and Naive Bayes were applied to predict an individual's likelihood of developing AUD. Random forests were found to achieve the highest accuracy (82%), compared to Decision Trees and Naive Bayes. Findings from this study can offer insights that help parents, healthcare professionals, and educators develop strategies to reduce AUD risk, enabling early intervention and targeted prevention efforts.

cs.LG↗

arXiv2024

Quantum Light Generation based on GaN Microring towards Fully On-chip Source

Integrated quantum light source is increasingly desirable in large-scale quantum information processing.~Despite recent remarkable advances, new material platform is constantly being explored for the fully on-chip integration of quantum light generation, active and passive manipulation, and detection. Here, for the first time, we demonstrate a gallium nitride (GaN) microring based quantum light generation in the telecom C-band, which has potential towards the monolithic integration of quantum light source.~In our demonstration, the GaN microring has a free spectral range of 330 GHz and a near-zero anomalous dispersion region of over 100 nm. The generation of energy-time entangled photon pair is demonstrated with a typical raw two-photon interference visibility of 95.5$\pm$6.5%, which is further configured to generate heralded single photon with a typical heralded second-order auto-correlation $g^{(2)}_{H}(0)$ of 0.045$\pm$0.001. Our results pave the way for developing chip-scale quantum photonic circuit.

quant-ph↗

arXiv2024

Weak collision effect on nonlinear Landau damping for the Vlasov-Poisson-Fokker-Planck system

We investigate the impact of weak collisions on Landau damping in the Vlasov-Poisson-Fokker-Planck system on a torus, specifically focusing on its proximity to a Maxwellian distribution. In the case where the Gevrey index satisfies $\frac{1}{s}\leq3$, we establish the global stability and enhanced dissipation of small initial data, which remain unaffected by the small diffusion coefficient $\nu$. For Gevrey index $\frac{1}{s}>3$, we prove the global stability and enhanced dissipation of initial data, whose size is on the order of $O(\nu^\frac{1-3s}{3-3s})$. Our analysis provides insights into the effects of phase mixing, enhanced dissipation, and plasma echoes.

math.AP↗

arXiv2023

Accelerated high-index saddle dynamics method for searching high-index saddle points

The high-index saddle dynamics (HiSD) method [J. Yin, L. Zhang, and P. Zhang, {\it SIAM J. Sci. Comput., }41 (2019), pp.A3576-A3595] serves as an efficient tool for computing index-$k$ saddle points and constructing solution landscapes. Nevertheless, the conventional HiSD method often encounters slow convergence rates on ill-conditioned problems. To address this challenge, we propose an accelerated high-index saddle dynamics (A-HiSD) by incorporating the heavy ball method. We prove the linear stability theory of the continuous A-HiSD, and subsequently estimate the local convergence rate for the discrete A-HiSD. Our analysis demonstrates that the A-HiSD method exhibits a faster convergence rate compared to the conventional HiSD method, especially when dealing with ill-conditioned problems. We also perform various numerical experiments including the loss function of neural network to substantiate the effectiveness and acceleration of the A-HiSD method.

math.NA↗

arXiv2023

Semi-implicit method of high-index saddle dynamics and application to construct solution landscape

We analyze the semi-implicit scheme of high-index saddle dynamics, which provides a powerful numerical method for finding the any-index saddle points and constructing the solution landscape. Compared with the explicit schemes of saddle dynamics, the semi-implicit discretization relaxes the step size and accelerates the convergence, but the corresponding numerical analysis encounters new difficulties compared to the explicit scheme. Specifically, the orthonormal property of the eigenvectors at each time step could not be fully employed due to the semi-implicit treatment, and computations of the eigenvectors are coupled with the orthonormalization procedure, which further complicates the numerical analysis. We address these issues to prove error estimates of the semi-implicit scheme via, e.g. technical splittings and multi-variable circulating induction procedure. We further analyze the convergence rate of the generalized minimum residual solver for solving the semi-implicit system. Extensive numerical experiments are carried out to substantiate the efficiency and accuracy of the semi-implicit scheme in constructing solution landscapes of complex systems.

math.NA↗

arXiv2023

Energy-time Entanglement Coexisting with Fiber Optical Communication at Telecom C-band

The coexistence of quantum and classical light in the same fiber link is extremely desired in developing quantum communication. It has been implemented for different quantum information tasks, such as classical light coexisting with polarization-entangled photons at telecom O-band, and with quantum signal based quantum key distribution (QKD). In this work, we demonstrate the coexistence of energy-time entanglement based QKD and fiber optical communication at the telecom C-band. The property of noise from the classical channel is characterized with classical light at different wavelengths. With the largest noise, i.e., the worst case, the properties of energy-time entanglement are measured at different fiber optical communication rates. By measuring the two-photon interference of energy-time entanglement, our results show that a visibility of 82.01$\pm$1.10\% is achieved with a bidirectional 20 Gbps fiber optical communication over 40 km. Furthermore, by performing the BBM92 protocol for QKD, a secret key rate of 245 bits per second could be generated with a quantum bit error rate of 8.88\% with the coexisted energy-time entanglement.~Our demonstration paves the way for developing the infrastructure for quantum networks compatible with fiber optical communication.

quant-ph↗

arXiv2022

Convergence analysis of discrete high-index saddle dynamics

Saddle dynamics is a time continuous dynamics to efficiently compute the any-index saddle points and construct the solution landscape. In practice, the saddle dynamics needs to be discretized for numerical computations, while the corresponding numerical analysis are rarely studied in the literature, especially for the high-index cases. In this paper we propose the convergence analysis of discrete high-index saddle dynamics. To be specific, we prove the local linear convergence rates of numerical schemes of high-index saddle dynamics, which indicates that the local curvature in the neighborhood of the saddle point and the accuracy of computing the eigenfunctions are main factors that affect the convergence of discrete saddle dynamics. The proved results serve as compensations for the convergence analysis of high-index saddle dynamics and are substantiated by numerical experiments.

math.NA↗

arXiv2020

Multiple Tunable Hyperbolic Resonances in Broadband Infrared Carbon-Nanotube Metamaterials

Aligned, densely-packed carbon nanotube metamaterials prepared using vacuum filtration are an emerging infrared nanophotonic material. We report multiple hyperbolic plasmon resonances, together spanning the mid-infrared, in individual resonators made from aligned and densely-packed carbon nanotubes. In the first near-field scanning optical microscopy (NSOM) imaging study of nanotube metamaterial resonators, we observe distinct deeply-subwavelength field profiles at the fundamental and higher-order resonant frequencies. The wafer-scale area of the nanotube metamaterials allows us to combine this near-field imaging with a systematic far-field spectroscopic study of the scaling properties of many resonator arrays. Thorough theoretical modeling agrees with these measurements and identifies the resonances as higher-order Fabry-Pérot (FP) resonances of hyperbolic waveguide modes. Nanotube resonator arrays show broadband extinction from 1.5-10 μm and reversibly switchable extinction in the 3-5 μm atmospheric transparency window through the coexistence of multiple modes in individual ribbons. Broadband carbon nanotube metamaterials supporting multiple resonant modes are a promising candidate for ultracompact absorbers, tunable thermal emitters, and broadband sensors in the mid-infrared.

physics.optics↗

arXiv2019

Single photon emission in $WSe_2$ up 160 K by quantum yield control

Two dimensional semiconductors hosting strain-induced quantum emitters offer unique abilities to achieve scalable architectures for deterministic coupling to nanocavities and waveguides that are required to enable chip-based quantum information processing technologies. A severe drawback remains that exciton emission from quantum emitters in $WSe_2$ quenches beyond 30 K, which requires cryogenic cooling. Here we demonstrate an approach to increase the temperature survival of exciton quantum emitters in $WSe_2$ that is based on maximizing the emitter quantum yield. Utilizing optimized material growth that leads to reduced density of nonradiative defects as well as coupling of the exciton emission to plasmonic nanocavities modes, we achieve average quantum yields up to 44%, thermal activation energies up to 92 meV, and single photon emission signatures up to temperatures of 160 K. At these values non-cryogenic cooling with thermo-electric chips becomes already feasible, while quantitative analysis shows that room temperature operation is within reach through active strain engineering.

cond-mat.mtrl-sci↗