arXiv subjects

Wending Zhao

Publications and source records attributed to Wending Zhao.

9 recordsLinked to original sources

arXiv2026

Solve Crude Oil Scheduling Problems by Using Quantum-Classical Hybrid Algorithms

The optimization of front-end crude oil scheduling is a critical determinant of refinery profitability and operational stability. However, the coupling of discrete logistics events (e.g., vessel berthing) with continuous material flows (e.g., pipeline transfers) renders this problem an NP-hard Mixed-Integer Linear Programming (MILP) challenge, often intractable for classical solvers at industrial scales. This study proposes a novel hybrid quantum-classical framework to address these computational bottlenecks. We employ Benders Decomposition to decouple the monolithic model into a discrete Master Problem (MP) and a continuous Subproblem (SP). To exploit the search capabilities of quantum computing, the MP is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) model and solved via a hybrid quantum solver, while the SP enforces mass balance and quality constraints through iterative optimality and feasibility cuts. Extensive experiments on 15 multi-scale instances demonstrate that the proposed framework significantly outperforms traditional metaheuristics (e.g., Genetic Algorithms, Tabu Search), reducing total operating costs by approximately 73--80% and achieving computational speeds comparable to state-of-the-art commercial solvers (Gurobi). By effectively leveraging global optimality cuts, the method overcomes the tendency of heuristic approaches to trap in local optima, providing a robust and scalable solution for complex refinery logistics.

quant-ph↗

arXiv2025

A brain-inspired paradigm for scalable quantum vision

One of the fundamental tasks in machine learning is image classification, which serves as a key benchmark for validating algorithm performance and practical potential. However, effectively processing high-dimensional, detail-rich images, a capability that is inherent in biological vision, remains a persistent challenge. Inspired by the human brain's efficient ``Forest Before Trees'' cognition, we propose a novel Guiding Paradigm for image recognition, leveraging classical neural networks to analyze global low-frequency information and guide targeted quantum circuit towards critical high-frequency image regions. We present the Brain-Inspired Quantum Classifier (BIQC), implementing this paradigm via a complementarity architecture where a quantum pathway analyzes the localized intricate details identified by the classical pathway. Numerical simulations on diverse datasets, including high-resolution images, show the BIQC's superior accuracy and scalability compared to existing methods. This highlights the promise of brain-inspired, hybrid quantum-classical approach for developing next-generation visual systems.

quant-ph↗

arXiv2025

3DGAA: Realistic and Robust 3D Gaussian-based Adversarial Attack for Autonomous Driving

Camera-based perception in connected and autonomous vehicles remains exposed to physical adversarial attacks. Prior attacks often either optimize image-plane textures, weakening cross-view consistency, or rely on shape modifications that are difficult to fabricate and deploy. Both cases limit their value for security and safety assessment of camera-based perception. This paper introduces a fabrication-first framework that learns view-consistent, geometry-preserving adversarial wraps for vehicles. The method performs three-dimensional multi-view optimization with a 3D Gaussian splatting surrogate to enforce consistency under an expectation over viewpoint, illumination, and occlusion, while the final artifact is constrained to print-only textures that keep vehicle geometry unchanged. We evaluate the framework through matched CARLA simulations, controlled miniature-vehicle physical evidence, and tests on multiple modern detectors. The wraps produce substantial and stable reductions in detection confidence and average precision across views while maintaining perceptual realism. Ablation studies isolate the effects of physical filtering, physical augmentation, and shape-consistency regularization, while efficiency analyses report convergence time and memory profiles. Additional experiments report ASR, mIoU-based segmentation degradation, cross-detector transfer against physical attack baselines, perturbation-area fairness diagnostics, and robustness under common input preprocessing defenses. By coupling three-dimensional optimization with a geometry-preserving realization, the study supports a practical pathway to systematically stress-test camera perception in safety-critical autonomous driving systems and provides evidence that can inform defense design and evaluation protocols in information forensics and security.

cs.CV↗

arXiv2025

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment

This paper reports on the NTIRE 2025 challenge on Text to Image (T2I) generation model quality assessment, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2025. The aim of this challenge is to address the fine-grained quality assessment of text-to-image generation models. This challenge evaluates text-to-image models from two aspects: image-text alignment and image structural distortion detection, and is divided into the alignment track and the structural track. The alignment track uses the EvalMuse-40K, which contains around 40K AI-Generated Images (AIGIs) generated by 20 popular generative models. The alignment track has a total of 371 registered participants. A total of 1,883 submissions are received in the development phase, and 507 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. The structure track uses the EvalMuse-Structure, which contains 10,000 AI-Generated Images (AIGIs) with corresponding structural distortion mask. A total of 211 participants have registered in the structure track. A total of 1155 submissions are received in the development phase, and 487 submissions are received in the test phase. Finally, 8 participating teams submitted their models and fact sheets. Almost all methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on T2I model quality assessment.

cs.CV↗

arXiv2025

Clustering-Based Sub-QUBO Extraction for Hybrid QUBO Solvers

Quantum Approximate Optimization Algorithm (QAOA) can be used to solve quadratic unconstrained binary optimization (QUBO) problems. However, the size of the solvable problem is limited by the number of qubits. To leverage noisy intermediate-scale quantum (NISQ) devices to solve large-scale QUBO problems, one possible way is to decompose the full problem into multiple sub-problems, which we refer to as the Sub-QUBO Formalism. In this work, we enhance this formalism by proposing a sub-QUBO extraction protocol. To do so, we define a measure to quantify correlations between variables and use it to build a correlation matrix. This matrix serves as the input for clustering algorithms to group variables. Variables belonging to the same group form sub-QUBOs and are subsequently solved using QAOA. Our numerical analysis on several classes of randomly generated QUBO problems demonstrates that this grouping method outperforms previous approaches in terms of objective function values, while maintaining a comparable number of quantum subroutine calls. This method offers wide applicability for solving QUBO problems on NISQ devices.

quant-ph↗

arXiv2023

SoccerNet 2023 Challenges Results

The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet.

cs.CV↗

arXiv2023

Probing Complex-energy Topology via Non-Hermitian Absorption Spectroscopy in a Trapped Ion Simulator

Non-Hermitian systems generically have complex energies, which may host topological structures, such as links or knots. While there has been great progress in experimentally engineering non-Hermitian models in quantum simulators, it remains a significant challenge to experimentally probe complex energies in these systems, thereby making it difficult to directly diagnose complex-energy topology. Here, we experimentally realize a two-band non-Hermitian model with a single trapped ion whose complex eigenenergies exhibit the unlink, unknot or Hopf link topological structures. Based on non-Hermitian absorption spectroscopy, we couple one system level to an auxiliary level through a laser beam and then experimentally measure the population of the ion on the auxiliary level after a long period of time. Complex eigenenergies are then extracted, illustrating the unlink, unknot or Hopf link topological structure. Our work demonstrates that complex energies can be experimentally measured in quantum simulators via non-Hermitian absorption spectroscopy, thereby opening the door for exploring various complex-energy properties in non-Hermitian quantum systems, such as trapped ions, cold atoms, superconducting circuits or solid-state spin systems.

quant-ph↗

arXiv2023

An Improved Baseline Framework for Pose Estimation Challenge at ECCV 2022 Visual Perception for Navigation in Human Environments Workshop

This technical report describes our first-place solution to the pose estimation challenge at ECCV 2022 Visual Perception for Navigation in Human Environments Workshop. In this challenge, we aim to estimate human poses from in-the-wild stitched panoramic images. Our method is built based on Faster R-CNN for human detection, and HRNet for human pose estimation. We describe technical details for the JRDB-Pose dataset, together with some experimental results. In the competition, we achieved 0.303 $\text{OSPA}_{\text{IOU}}$ and 64.047\% $\text{AP}_{\text{0.5}}$ on the test set of JRDB-Pose.

cs.CV↗

arXiv2017

The Structure and Charge Density Waves in NbSe_2 by First-Principles Calculations

In this paper, we investigated structures and charge density waves, including its origin of CDW, by using first-principles calculations. Firstly, we performed structure searches of NbSe_2 under different pressures to explore the possible structures of the CDW phase in NbSe_2. The stability of the resulted structure is proved by extensive studies of energy calculations and phonon spectra. Based on the detailed analysis on electronic properties (both in real space and K space) and band structures during CDW transitions, we believe the structure distortion and the displacement of atoms are the origin of CDW, which also cause the change of the Fermi surfaces and electronic density of states (DOS). In the end, the results of band structure and DOS were used to explain the reason why CDW and superconducting orders compete with each other at low temperature.

cond-mat.str-el↗