A5101513127- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Quantum-Dot Cellular Automata
- Microwave Imaging and Scattering Analysis
- Complex Network Analysis Techniques
- Electromagnetic Scattering and Analysis
- Advanced Thermodynamics and Statistical Mechanics
- Neural Networks and Applications
- Machine Learning in Materials Science
- Mental Health Research Topics
- Random lasers and scattering media
- Model Reduction and Neural Networks
- Energy, Environment, Economic Growth
- Electric Motor Design and Analysis
- Graph Theory and Algorithms
- Advanced Graph Neural Networks
- Adversarial Robustness in Machine Learning
- Functional Brain Connectivity Studies
- Computability, Logic, AI Algorithms
- Optical Imaging and Spectroscopy Techniques
- Advanced Image and Video Retrieval Techniques
- Opinion Dynamics and Social Influence
- Quantum and electron transport phenomena
- Electromagnetic Compatibility and Noise Suppression
- Stochastic Gradient Optimization Techniques
Peking University
2024
University of Electronic Science and Technology of China
2020-2024
Huzhou University
2024
Anhui University
2024
Queens College, CUNY
2020-2021
The Graduate Center, CUNY
2020-2021
Nanjing University of Information Science and Technology
2020
University of Maryland, College Park
2019
Molecular docking plays a pivotal role in drug discovery and precision medicine, furnishing insights into protein functionalities fostering the development of therapeutics. Here, we introduce potential alternative solution to this problem using quantum approximate optimization algorithm (QAOA) based algorithm. Our method was applied analyze diverse biological systems, including SARS-CoV-2 ${\mathrm{M}}^{\text{pro}}$ complex with PM-2-020B, DPP-4 piperidine fused imidazopyridine 34, HIV-1...
Abstract Empirical networks exhibit significant heterogeneity in node connections, resulting a few vertices playing critical roles various scenarios, including decision-making, viral marketing, and population immunization. Thus, identifying key is fundamental research problem Network Science. In this paper, we introduce vertex entanglement (VE), an entanglement-based metric capable of quantifying the perturbations caused by individual on spectral entropy, residing at intersection quantum...
Graph generation is a critical yet challenging task as empirical analyses require deep understanding of complex, non-Euclidean structures. Although diffusion models have recently made significant achievements in graph generation, these typically adapt from the frameworks designed for image making them ill-suited capturing topological properties graphs. In this work, we propose novel Higher-order Guided Diffusion (HOG-Diff) model that follows coarse-to-fine curriculum and guided by...
Abstract Variational quantum eigensolvers (VQEs) are garnering significant attention for their ability to estimate the ground-state energy of target Hamiltonians. In particular, digital-analog computation (DAQC) stands out as a crucial platform VQEs in noisy intermediate-scale (NISQ) era, leveraging single-qubit operations and natural time evolution governed by parameterized On other hand, variational quantum-neural hybrid eigensolver is also vital technique that enhances accuracy estimating...
Disorder is more the rule than exception in natural and synthetic materials. Nonetheless, wave propagation within inhomogeneously disordered materials has received scant attention. We combine microwave experiments theory to find spatial variation of generic quantities demonstrate that statistics samples any dimension are independent detailed structure a material depend only on net strengths distributed scattering reflection between observation point each boundaries.
Quantum machine learning (QML) is a rapidly rising research field that incorporates ideas from quantum computing and to develop emerging tools for scientific improving data processing. How eff... | Find, read cite all the you need on Tech Science Press
In recent years, an increasing number of studies about quantum machine learning not only provide powerful tools for chemistry and physics but also improve the classical algorithm. The hybrid quantum-classical framework, which is constructed by a variational circuit (VQC) optimizer, plays key role in latest studies. Nevertheless, these hybridframework-based models, VQC mainly with fixed structure this causes inflexibility problems. There are few focused on comparing performance generative...
The hybrid quantum-classical framework has been attracted more researchers, no matter in classical machine learning or quantum physics and chemistry. Although the existing models are implemented on small intermediate scale system datasets, they also provide foresight of large applications future. Because consists parametrized circuit well-developed optimizers, selection appropriate optimizer for different task matters final performance model. In this paper, we take nine widely employed...
Quantum neural networks (QNNs) and quantum kernels stand as prominent figures in the realm of machine learning, poised to leverage nascent capabilities near-term computers surmount classical learning challenges. Nonetheless, training-efficiency challenge poses a limitation on both QNNs kernels, curbing their efficacy when they are applied extensive datasets. To confront this concern, we present unified approach---coreset selection---aimed at expediting training by distilling judicious subset...
Molecular docking plays a pivotal role in drug discovery and precision medicine, enabling us to understand protein functions advance novel therapeutics. Here, we introduce potential alternative solution this problem, the digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA), which utilizes counterdiabatic driving QAOA on computer. Our method was applied analyze diverse biological systems, including SARS-CoV-2 Mpro complex with PM-2-020B, DPP-4 piperidine fused...
High frequency switching in an inverter fed AC electrical machine can cause high circulating bearing currents which flow through a zigzag path the stator laminations due to skin effects. This paper presents both analytical calculation and finite element analysis of impedance. The methods with frozen permeability technique account for load effect is usually neglected literature. It shown that impedance lower at full than light varies rotor position angle. results be used further prediction currents.
Quantum machine learning is expected to be one of the potential applications that can realized in near future. Finding for it has become hot topics quantum computing community. With increase digital image processing, researchers try use processing instead classical improve ability processing. Inspired by previous studies on adversarial circuit learning, we introduce a generative framework loading and image. In this paper, extend networks field show how an using circuits. By reducing gates...
Laplacian eigenmap algorithm is a typical nonlinear model for dimensionality reduction in classical machine learning. We propose an efficient quantum to exponentially speed up the original counterparts. In our work, we demonstrate that Hermitian chain product proposed linear discriminant analysis (arXiv:1510.00113,2015) can be applied implement algorithm. While requires polynomial time solve eigenvector problem, able reduction.
Quantum neural networks (QNNs) and quantum kernels stand as prominent figures in the realm of machine learning, poised to leverage nascent capabilities near-term computers surmount classical learning challenges. Nonetheless, training efficiency challenge poses a limitation on both QNNs kernels, curbing their efficacy when applied extensive datasets. To confront this concern, we present unified approach: coreset selection, aimed at expediting by distilling judicious subset from original...
As an important resource to realize quantum information, correlation displays different behaviors, freezing phenomenon and non-localization, which are dissimilar the entanglement classical correlation, respectively. In our setup, ordering of is represented for quantization methods by considering open system scenario. The machine learning method (neural network method) then adopted train construction a bridge between R\`{e}nyi discord ($\alpha=2$) geometric (Bures distance) $X$ form states....
Quantum correlation plays a critical role in the maintenance of quantum information processing and nanometer device design. In past two decades, several quantitative methods had been proposed to study certain open systems, including geometry entropy style discord methods. However, there are differences among these quantification methods, which promote deep understanding correlation. this paper, novel time-dependent three environmental system model is established This interacts with...