arXiv subjects

Pei Zhang

Publications and source records attributed to Pei Zhang.

At least 109 records · Page 6Linked to original sources

arXiv2022

Metaball-Imaging Discrete Element Lattice Boltzmann Method for fluid-particle system of complex morphologies with settling case study

Fluid-particle systems are highly sensitive to particle morphologies. While many attempts have been made on shape descriptors and coupling schemes, how to simulate the particle-particle and particle-fluid interactions with a balance between accuracy and efficiency is still a challenge, especially when complex-shaped particles are considered. This study presents a Metaball-Imaging (MI) based Discrete Element Lattice Boltzmann Method (DELBM) for fluid simulations with irregular shaped particles. The major innovation is the MI algorithm to capture the real grain shape for DELBM simulations,where the Metaball function is utilized as the mathematical representation due to its versatile and efficient expressiveness of complex shapes.The contact detection is tackled robustly by gradient calculation of the closest point with a Newton-Raphson based scheme. And the coupling with LBM is accomplished by a classic sharp-interface scheme. As for refiling, a local refiling algorithm based on the bounce back rule is implemented. Validations on three settling experiments of irregular-shaped natural cobblestones indicate the proposed model to be effective and powerful in probing micromechanics of irregular-shaped granular media immersed in fluid systems. The potential of this model on studies of shape-induced physical processes is further investigated with numerical examples on the "drafting, kissing and tumbling" phenomenon of pair particles in various shapes.

cs.CE↗

arXiv2022

Backward Stochastic Differential Equations (BSDEs) Using Infinite-dimensional Martingales with Subdifferential Operator

In this paper, we focus on a family of backward stochastic differential equations (BSDEs) with sub-differential operators that are driven by infinite-dimensional martingales which involve symmetry, that is, the process involves a positive definite nuclear operator Q. We shall show that the solution to such infinite-dimensional BSDEs exists and is unique. The existence of the solution is established using Yosida approximations, and the uniqueness is proved using Fixed Point Theorem. Furthermore, as an application of the main result, we shall show that the backward stochastic partial differential equation driven by infinite-dimensional martingales with a continuous linear operator has a unique solution under the condition that the function F equals to zero.

math.PR↗

arXiv2022

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing i) reduction of data loading time up to 4.2 times compared with a conventional method and ii) linear scaling performance for training up to 1,024 GPUs on both Summit and Perlmutter.

cs.LG↗

arXiv2022

Experimental implementation of Hardy-like quantum pigeonhole paradoxes

We present the general Hardy-like quantum pigeonhole paradoxes for \textit{n}-particle states, and find that each of such paradoxes can be simply associated to an un-colorable solution of a specific vertex-coloring problem induced from the projected-coloring graph (a kind of unconventional graph). Besides, as a special kind of Hardy's paradox, several kinds of Hardy-like quantum pigeonhole paradoxes can even give rise to higher success probability in demonstrating the conflict between quantum mechanics and local or noncontextual realism than the previous Hardy's paradoxes. Moreover, not only multi-qubit states, but high-dimensional states can exhibit the paradoxes. In contrast to only one type of contradiction presented in the original quantum pigeonhole paradox, two kinds of three-qubit projected-coloring graph states as the minimal illustration are discussed in our work, and an optical experiment to verify such stronger paradox is performed. This quantum paradox provides innovative thoughts and methods in exploring new types of stronger multi-party quantum nonlocality and may have potential applications in multi-party untrusted communications and device-independent random number generation.

quant-ph↗

arXiv2022

Coupled Metaball Discrete Element Lattice Boltzmann Method for Fluid-Particle Systems with non-spherical particle shapes: A sharp interface coupling scheme

Fluid-particle systems are very common in many natural processes and engineering applications. However, accurately and efficiently modelling fluid-particle systems with complex particle shapes is still a challenging task. Here, we present a numerical model that combines the advantages of Lattice Boltzmann Method (LBM) in solving complex flow problems and the capability of the recently introduced Metaball Discrete Element Method (MDEM) in handling non-spherical particle shapes. A sharp interface coupling scheme is developed and the numerical instability issues due to the discontinuity of interfaces are carefully addressed. A local refilling algorithm for new fluid nodes is proposed and special treatments are introduced to reduce numerical noises when two particles are close. The proposed model is validated by simulations of settling of a single sphere (with metaball representation) as well as a non-spherical particle in a viscous fluid. Good agreements are found comparing the simulations with experimental results which are also carried out in this study. The coupling scheme is also tested by multiple particle simulations which clearly illustrated the stability of the proposed model. Finally, numerical examples with complex particle shapes demonstrated that the proposed model can be a powerful tool for future applications such as shape-induced segregation in riverbeds, and phase transition of dense suspension.

physics.flu-dyn↗

arXiv2022

Retrieving High-Dimensional Quantum Steering From a Noisy Environment with N Measurement Settings

One of the most often implied benefits of high-dimensional (HD) quantum systems is to lead to stronger forms of correlations, featuring increased robustness to noise. Here, we experimentally demonstrate the $n$-setting linear HD quantum steering criterion. We verify the large violation of the steering inequalities without full-state tomography. The lower bound of the violation is $2.24\pm0.01$ in 11 dimensions, exceeding the bound ($V<2$) of 2-setting criteria. Hence, a higher strength of steering has been revealed. Moreover, we demonstrate the method for enhancing the noise robustness without increasing dimension, alternatively, by increasing measurement settings. Using the entanglement in 11 dimensions, we experimentally retrieve steering nonlocality with $63.4\pm1.4\%$ isotropic noise fraction, surpassing the $50\%$ limitation of 2-setting criteria. Our work offers the potential for practical one-sided device-independent quantum information processing that tolerates the noisy environment, lossy detection, and transcends the present transmission distance limitation.

quant-ph↗

arXiv2022

Friction-dependent rheology of dry granular systems

Understanding the rheology of granular assemblies is important for natural and engineering systems, but the relationship between inter-particle friction (or microscopic friction) and macroscopic friction is still not well understood. In this study, using the the discrete element method (DEM) with spherical particles and realistic contact laws, we investigate the mechanics of granular systems with a wide range of inter-particle frictional coefficients and aim to establish a friction-dependent rheology for dry granular flows. The corresponding results show that increasing inter-particle friction dramatically increases the effective frictional coefficient, $μ_{\textrm{eff}}$, while decreasing the solid fraction of the system and increasing the transitional inertial number that marks the division of quasi-static regimes and intermediate flow regimes. We further propose a new dimensionless number, $\mathcal{M}$, as a ratio between the inertial effect and frictional effect, which is similar to the effective aspect ratio in granular column collapses, and unifies the influence of inter-particle friction with the inertial number. We then establish a relationship between $\mathcal{M}$ and the dimensionless granular temperature, $Θ$, to further universalize the influence of inter-particle frictions. Such study can broaden the application of the $μ(I)$ rheology in natural and engineering systems and help establish a more general constitutive model for complex granular systems.

cond-mat.soft↗

arXiv2022

PI3NN: Out-of-distribution-aware prediction intervals from three neural networks

We propose a novel prediction interval (PI) method for uncertainty quantification, which addresses three major issues with the state-of-the-art PI methods. First, existing PI methods require retraining of neural networks (NNs) for every given confidence level and suffer from the crossing issue in calculating multiple PIs. Second, they usually rely on customized loss functions with extra sensitive hyperparameters for which fine tuning is required to achieve a well-calibrated PI. Third, they usually underestimate uncertainties of out-of-distribution (OOD) samples leading to over-confident PIs. Our PI3NN method calculates PIs from linear combinations of three NNs, each of which is independently trained using the standard mean squared error loss. The coefficients of the linear combinations are computed using root-finding algorithms to ensure tight PIs for a given confidence level. We theoretically prove that PI3NN can calculate PIs for a series of confidence levels without retraining NNs and it completely avoids the crossing issue. Additionally, PI3NN does not introduce any unusual hyperparameters resulting in a stable performance. Furthermore, we address OOD identification challenge by introducing an initialization scheme which provides reasonably larger PIs of the OOD samples than those of the in-distribution samples. Benchmark and real-world experiments show that our method outperforms several state-of-the-art approaches with respect to predictive uncertainty quality, robustness, and OOD samples identification.

cs.LG↗

arXiv2022

Direct Light Orbital Angular Momentum Detection in Mid-Infrared based on Type-II Weyl Semimetal TaIrTe4

The capability of direct photocurrent detection of orbital angular momentum (OAM) of light has recently been realized with topological Weyl semimetal, but limited to near infrared wavelength range. The extension of direct OAM detection to midinfrared, a wavelength range that plays important role in a vast range of applications, such as autonomous driving, night vision and motion detection, is challenging and has not yet been realized. This is because most studies of photocurrent responses are not sensitive to the phase information and the photo response is usually very poor in the mid-infrared. In this study, we designed a photodetector based on Type-II Weyl semimetal tantalum iridium tellurides with designed electrode geometries for direct detection of the topological charge of OAM through orbital photogalvanic effect. Our results indicate helical phase gradient of light can be distinguished by a current winding around the optical beam axis with a magnitude proportional to its quantized OAM mode number. The topological enhanced response at mid-infrared of TaIrTe4 further help overcome the low responsivity issues and finally render the direct orbital angular momentum detection capability in mid-infrared. Our study enables on-chip integrated OAM detection, and thus OAM sensitive focal plane arrays in mid-infrared. Such capability triggers new route to explore applications of light carrying OAM, especially that it can crucially promote the performance of many mid-infrared imaging related applications, such as intricate target recognition and night vision.

physics.optics↗

arXiv2022

Multiparameter simultaneous optimal estimation with an SU(2) coding unitary evolution

In a ubiquitous $SU(2)$ dynamics, achieving the simultaneous optimal estimation of multiple parameters is significant but difficult. Using quantum control to optimize this $SU(2)$ coding unitary evolution is one of solutions. We propose a method, characterized by the nested cross-products of the coefficient vector $\mathbf{X}$ of $SU(2)$ generators and its partial derivative $\partial_\ell \mathbf{X}$, to investigate the control-enhanced quantum multiparameter estimation. Our work reveals that quantum control is not always functional in improving the estimation precision, which depends on the characterization of an $SU(2)$ dynamics with respect to the objective parameter. This characterization is quantified by the angle $α_\ell$ between $\mathbf{X}$ and $\partial_\ell \mathbf{X}$. For an $SU(2)$ dynamics featured by $α_\ell=π/2$, the promotion of the estimation precision can get the most benefits from the controls. When $α_\ell$ gradually closes to $0$ or $π$, the precision promotion contributed to by quantum control correspondingly becomes inconspicuous. Until a dynamics with $α_\ell=0$ or $π$, quantum control completely loses its advantage. In addition, we find a set of conditions restricting the simultaneous optimal estimation of all the parameters, but fortunately, which can be removed by using a maximally entangled two-qubit state as the probe state and adding an ancillary channel into the configuration. Lastly, a spin-$1/2$ system is taken as an example to verify the above-mentioned conclusions. Our proposal sufficiently exhibits the hallmark of control-enhancement in fulfilling the multiparameter estimation mission, and it is applicable to an arbitrary $SU(2)$ parametrization process.

quant-ph↗

arXiv2022

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems

We introduce a multi-tasking graph convolutional neural network, HydraGNN, to simultaneously predict both global and atomic physical properties and demonstrate with ferromagnetic materials. We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum (FePt) with a fixed body centered tetragonal (BCT) lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range. By taking advantage of underlying physical correlations between material properties, multi-task learning (MTL) with HydraGNN provides effective training even with modest amounts of data. Moreover, this is achieved with just one architecture instead of three, as required by single-task learning (STL). The first convolutional layers of the HydraGNN architecture are shared by all learning tasks and extract features common to all material properties. The following layers discriminate the features of the different properties, the results of which are fed to the separate heads of the final layer to produce predictions. Numerical results show that HydraGNN effectively captures the relation between the configurational entropy and the material properties over the entire compositional range. Overall, the accuracy of simultaneous MTL predictions is comparable to the accuracy of the STL predictions. In addition, the computational cost of training HydraGNN for MTL is much lower than the original DFT calculations and also lower than training separate STL models for each property.

cond-mat.mtrl-sci↗

arXiv2022

Verification of Kochen-Specker-type quantum contextuality with a single photon

Contextuality provides one of the fundamental characterizations of quantum phenomena, and can be used as a resource in lots of quantum information processing. In this paper, we summarize and derive some equivalent noncontextual inequalities from different noncontextual models of the proofs for Kochen-Specker theorem based on Greenberger-Horne-Zeilinger states. These noncontextual inequalities are equivalent up to some correlation items which hold both for noncontextual hidden variable theories and quantum mechanics. Therefore, using single-photon hyperentangled Greenberger-Horne-Zeilinger states encoded by spin, path and orbital angular momentum, we experimentally verify several state-dependent noncontextual models of the proofs for the Kochen-Specker theorem by testing an extreme simplest Mermin-like inequality.

quant-ph↗

arXiv2021

Detection and demultiplexing of cylindrical vector beams enabled by rotational Doppler effect

Cylindrical vector beams (CVBs) detection is of vital significance in kinds of studies such as particle observation, mode-division multiplexing. Here we realize a comprehensive detection of cylindrical vector beams based on the rotational Doppler effect including analysis of topological charges, amplitudes, and phases for mode bases. We construct a mode demultiplexing scheme to obtain the amplitudes, phases in beating signal of collected scattering light by Fourier transformation. The method resolves both absolute values and signs of topological charges ofCVB simultaneously, which can not be simply realized by existing polarization examination techniques. It may be of big potential for related researches since an efficient, quantitative and complete scheme to detect CVBs is verified starting from this work.

physics.optics↗

arXiv2021

Experimental Demonstration of Efficient High-dimensional Quantum Gates with Orbital Angular Momentum

Quantum gates are essential for the realization of quantum computer and have been implemented in various types of two-level systems. However, high-dimensional quantum gates are rarely investigated both theoretically and experimentally even that high-dimensional quantum systems exhibit remarkable advantages over two-level systems for some quantum information and quantum computing tasks. Here we experimentally demonstrate the four-dimensional X gate and its unique higher orders with the average conversion efficiency 93\%. All these gates are based on orbital-angular-momentum degree of freedom of single photons. Besides, a set of controlled quantum gates is implemented by use of polarization degree of freedom. Our work is an important step towards the goal of achieving arbitrary high-dimensional quantum circuit and paves a way for the implementation of high-dimensional quantum communication and computation.

quant-ph↗

arXiv2021

Federated Route Leak Detection in Inter-domain Routing with Privacy Guarantee

In the inter-domain network, a route leak occurs when a routing announcement is propagated outside of its intended scope, which is a violation of the agreed routing policy. The route leaks can disrupt the internet traffic and cause large outages. The accurately detection of route leaks requires the share of AS business relationship information of ASes. However, the business relationship information between ASes is confidential due to economic issues. Thus, ASes are usually unwilling to revealing this information to the other ASes, especially their competitors. Recent advancements in federated learning make it possible to share data while maintaining privacy. Motivated by this, in this paper we study the route leak problem by considering the privacy of business relationships between ASes, and propose a method for route leak detection with privacy guarantee by using blockchain-based federated learning framework, in which ASes can train a global detection model without revealing their business relationships directly. Moreover, the proposed method provides a self-validation scheme by labeling AS triples with local routing policies, which mitigates route leaks' lack of ground truth. We evaluate the proposed method under a variety of datasets including unbalanced and balanced datasets. The different deployment strategies of the proposed method under different topologies are also examined. The results show that the proposed method has a better performance in detecting route leaks than a single AS detection regardless of whether using balanced or unbalanced datasets. In the analysis of the deployment, the results show that ASes with more peers have more possible route leaks and can contribute more on the detection of route leaks with the proposed method.

cs.NI↗

arXiv2021

Design of a HOM-Damped 166.6 MHz Compact Quarter-Wave beta=1 Superconducting Cavity for High Energy Photon Source

Superconducting cavities with low RF frequencies and heavy damping of higher order modes (HOM) are desired for the main accelerator of High Energy Photon Source (HEPS), a 6 GeV synchrotron light source promising ultralow emittance currently under construction in Beijing. A compact 166.6 MHz superconducting cavity was proposed adopting a quarter-wave beta=1 geometry. Based on the successful development of a proof-of-principle cavity, a HOM-damped 166.6 MHz compact superconducting cavity was subsequently designed. A ferrite damper was installed on the beam pipe to reduce HOM impedance below the stringent threshold of coupled-bunch instabilities. Being compact, RF field heating on the cavity vacuum seal was carefully examined against quenching the NbTi flange. The cavity was later dressed with a helium vessel and the tuning mechanism was also realized. Excellent RF and mechanical properties were eventually achieved. Finally, the two-cavity string was designed to ensure smooth transitions among components and proper shielding of synchrotron light. This paper presents a complete design of a fully dressed HOM-damped low-frequency beta=1 superconducting cavity for HEPS.

physics.acc-ph↗

arXiv2021

Security analysis method for practical quantum key distribution with arbitrary encoding schemes

Quantum key distribution (QKD) gradually has become a crucial element of practical secure communication. In different scenarios, the security analysis of genuine QKD systems is complicated. A universal secret key rate calculation method, used for realistic factors such as multiple degrees of freedom encoding, asymmetric protocol structures, equipment flaws, environmental noise, and so on, is still lacking. Based on the correlations of statistical data, we propose a security analysis method without restriction on encoding schemes. This method makes a trade-off between applicability and accuracy, which can effectively analyze various existing QKD systems. We illustrate its ability by analyzing source flaws and a high-dimensional asymmetric protocol. Results imply that our method can give tighter bounds than the Gottesman-Lo-Lütkenhaus-Preskill (GLLP) analysis and is beneficial to analyze protocols with complex encoding structures. Our work has the potential to become a reference standard for the security analysis of practical QKD.

quant-ph↗

arXiv2021

A data-based comparative review and AI-driven symbolic model for longitudinal dispersion coefficient in natural streams

A better understanding of dispersion in natural streams requires knowledge of longitudinal dispersion coefficient(LDC). Various methods have been proposed for predictions of LDC. Those studies can be grouped into three types: analytical, statistical and ML-driven researches(Implicit and explicit). However, a comprehensive evaluation of them is still lacking. In this paper, we first present an in-depth analysis of those methods and find out their defects. This is carried out on an extensive database composed of 660 samples of hydraulic and channel properties worldwide. The reliability and representativeness of utilized data are enhanced through the deployment of the Subset Selection of Maximum Dissimilarity(SSMD) for testing set selection and the Inter Quartile Range(IQR) for removal of the outlier. The evaluation reveals the rank of those methods as: ML-driven method > the statistical method > the analytical method. Whereas implicit ML-driven methods are black-boxes in nature, explicit ML-driven methods have more potential in prediction of LDC. Besides, overfitting is a universal problem in existing models. Those models also suffer from a fixed parameter combination. To establish an interpretable model for LDC prediction with higher performance, we then design a novel symbolic regression method called evolutionary symbolic regression network(ESRN). It is a combination of genetic algorithms and neural networks. Strategies are introduced to avoid overfitting and explore more parameter combinations. Results show that the ESRN model has superiorities over other existing symbolic models in performance. The proposed model is suitable for practical engineering problems due to its advantage in low requirement of parameters (only w and U* are required). It can provide convincing solutions for situations where the field test cannot be carried out or limited field information can be obtained.

cs.LG↗