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arXiv 2024年

作者： Lei, Lingxiao Wang, Weichen Huang, Guangyao Hu, Shun Cao, Xi Zhang, Xinfang Deng, Mingtang Chen, Ping-Xing Institute for Quantum Science and Technology College of Science National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Greatwall Quantum Laboratory Changsha410006 China Hefei National Laboratory Hefei230088 China

The synthetic Floquet lattice, generated by multiple strong drives with mutually incommensurate frequencies, provides a powerful platform for the quantum simulation of topological phenomena. In this study, we propose a 4-band tight-binding model of the Chern insulator with a Chern number C = ±2 by coupling two layers of the half Bernevig-Hughes-Zhang lattice and subsequently mapping it onto the Floquet lattice to simulate its topological properties. To determine the Chern number of our Floquet-version model, we extend the energy pumping method proposed by Martin et al. [Phys. Rev. X 7, 041008 (2017)] and the topological oscillation method introduced by Boyers et al. [Phys. Rev. Lett. 125, 160505 (2020)], followed by numerical simulations for both methodologies. The simulation results demonstrate the successful extraction of the Chern number using either of these methods, providing an excellent prediction of the phase diagram that closely aligns with the theoretical one derived from the original bilayer half Bernevig-Hughes-Zhang model. Finally, we briefly discuss a potential experimental implementation for our model. Our work demonstrates significant potential for simulating complex topological matter using quantum computing platforms, thereby paving the way for constructing a more universal simulator for non-interacting topological quantum states and advancing our understanding of these intriguing phenomena. © 2024, CC BY.

关键词： quantum computers

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Ggf: Global Geometric Feature for Rotation Invariant Point Cloud Understanding

SSRN

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SSRN 2022年

作者： Xiao, Yunzhe Li, Fu Wang, Haotian Yang, Shaowu Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

Some point cloud understanding tasks are supposed to be rotation-invariant. However, rotation-invariance is not promised in most existing deep learning approaches. Extra data argumentation or a complex professional network is always required to increase the generalization to arbitrary orientations, which cannot benefit generic point cloud understanding tasks. To avoid these problems, we propose a simple yet generic plug-and-play feature, namely Global Geometric Feature(GGF), to eliminate the rotation influence in generic point cloud understanding tasks. GGF projects the point cloud to a rotation-invariant feature space by extracting the geometric relationship among points and obtaining a distributed global description. We find that GGF can be easily plugged into the generic point cloud processing approaches as a low-level feature without further adapting. The extensive experimental results show that the proposed GGF can enable the generic framework to achieve comparable performance accuracy compared to the dedicated rotation-invariant methods. © 2022, The Authors. All rights reserved.

关键词： Rotation

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Chern dartboard insulator: sub-Brillouin zone topology and skyrmion multipoles

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Communications Physics 2024年第1期7卷 32页

作者： Chen, Yun-Chung Lin, Yu-Ping Kao, Ying-Jer Department of Physics and Center for Theoretical Physics National Taiwan University Taipei10607 Taiwan Department of Physics University of California BerkeleyCA94720 United States Center for Quantum Science and Technology National Taiwan University Taipei10607 Taiwan National Center for High-Performance Computing Hsinchu City30076 Taiwan

Topology plays a crucial role in many physical systems, leading to interesting states at the surface. A paradigmatic example is the Chern number defined in the Brillouin zone that leads to robust gapless edge states. Here we introduce the reduced Chern number, defined in the subregions of Brillouin zone, and construct a family of Chern dartboard insulators with quantized reduced Chern numbers but with trivial bulk topology. Chern dartboard insulators are protected by the mirror symmetries and exhibit distinct pseudospin textures, including (anti)skyrmions, inside the sub-Brillouin zone. These Chern dartboard insulators host exotic gapless edge states, such as Möbius fermions and midgap corner states, and can be realized in the photonic crystals. Our work opens up new possibilities for exploring sub-Brillouin zone topology and nontrivial surface responses in topological systems. © 2024, The Author(s).

关键词： Topology

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An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly Detection

arXiv

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arXiv 2022年

作者： Miao, Qiucheng Xu, Chuanfu Zhan, Jun Zhu, Dong Wu, Chengkun Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Hunan Changsha China

Anomaly detection of multivariate time series is meaningful for system behavior monitoring. This paper proposes an anomaly detection method based on unsupervised Short- and Long-term Mask Representation learning(SLMR). The main idea is to extract short-term local dependency patterns and long-term global trend patterns by using multi-scale residual dilated convolution and Gated Recurrent Unit(GRU) respectively. Furthermore, our approach can comprehend temporal contexts and feature correlations by combining spatial-temporal masked self-supervised representation learning and sequence split. It considers the importance of features is different, and we introduce the attention mechanism to adjust the contribution of each feature. Finally, a forecasting-based model and a reconstruction-based model are integrated to focus on single timestamp prediction and latent representation of time series. Experiments show that the performance of our method outperforms other state-of-the-art models on three real-world datasets. Further analysis shows that our method is good at anomaly localization. © 2022, CC BY.

关键词： Convolution

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Caroli–de Gennes–Matricon Analogs in Full-Shell Hybrid Nanowires

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Physical Review Letters 2025年第20期134卷 206302-206302页

作者： M. T. Deng Carlos Payá Pablo San-Jose Elsa Prada C. M. Marcus S. Vaitiekėnas Center for Quantum Devices Niels Bohr Institute University of Copenhagen 2100 Copenhagen Denmark Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer Science and Technology NUDT Changsha 410073 China Instituto de Ciencia de Materiales de Madrid (ICMM) CSIC 28049 Madrid Spain Materials Science and Engineering and Department of Physics University of Washington Seattle Washington 98195 USA

We report tunneling spectroscopy of Andreev subgap states in hybrid nanowires with a thin superconducting full shell surrounding a semiconducting core. The combination of the quantized fluxoid of the shell and the Andreev reflection at the superconductor-semiconductor interface gives rise to analogs of Caroli–de Gennes–Matricon states found in Abrikosov vortices in type-II superconductors. Unlike in metallic superconductors, Caroli–de Gennes–Matricon analogs in full-shell hybrid nanowires manifest as one-dimensional Van Hove singularities with energy spacings comparable to the superconducting gap and independent of the Fermi energy, making them readily observable. Evolution of these analogs with axial magnetic field, skewed within the Little-Parks lobe structure, is consistent with theory and yields information about the radial distribution and angular momenta of the corresponding subbands.

关键词： Nanowires

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STAR NRE: Solving supernova selection effects with set-based truncated auto-regressive neural ratio estimation

arXiv

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arXiv 2024年

作者： Karchev, Konstantin Trotta, Roberto via Bonomea 265 Trieste34136 Italy Astrophysics Group Physics Department Blackett Lab Imperial College London Prince Consort Road LondonSW7 2AZ United Kingdom INFN-National Institute for Nuclear Physics Via Valerio 2 Trieste34127 Italy Italian Research Center on High-Performance Computing Big Data and Quantum Computing Italy

Accounting for selection effects in supernova type Ia (SN Ia) cosmology is crucial for unbiased cosmological parameter inference-even more so for the next generation of large, mostly photometric-only surveys. The conventional "bias correction" procedure has a built-in systematic bias towards the fiducial model used to derive it and fails to account for the additional Eddington bias that arises in the presence of significant redshift uncertainty. On the other hand, Bayesian hierarchical models scale poorly with the data set size and require explicit assumptions for the selection function that may be inaccurate or contrived. To address these limitations, we introduce STAR NRE, a simulation-based approach that makes use of a conditioned deep set neural network and combines efficient high-dimensional global inference with subsampling-based truncation in order to scale to very large survey sizes while training on sets with varying cardinality. Applying it to a simplified SN Ia model consisting of standardised brightnesses and redshifts with Gaussian uncertainties and a selection procedure based on the expected LSST sensitivity, we demonstrate precise and unbiased inference of cosmological parameters and the redshift evolution of the volumetric SN Ia rate from ≈100 000 mock SNa Ia. Our inference procedure can incorporate arbitrarily complex selection criteria, including transient classification, in the forward simulator and be applied to complex data like light curves. We outline these and other steps aimed at integrating STAR NRE into an end-to-end simulation-based pipeline for the analysis of future photometric-only SN Ia data. Copyright © 2024, The Authors. All rights reserved.

关键词： Cosmology

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ParallelDualSPHysics: supporting efficient parallel fluid simulations through MPI-enabled SPH method 22

ParallelDualSPHysics: supporting efficient parallel fluid si...

引用

Proceedings of the 51st International Conference on Parallel Processing

作者： Sifan Long Xiao-Wei Guo Xiaokang Fan Chao Li Kelvin Wong Ran Zhao Yi Liu Sen Zhang Canqun Yang College of Computer National University of Defense Technology China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology China College of Computer Central South University China

ISBN: (纸本)9781450397339

Smoothed Particle Hydrodynamics (SPH) is a classical mesh-free particle method which has been successfully applied in the field of Computational Fluid Dynamics (CFD). Its advantages over traditional mesh-based methods have made it very popular in simulating problems involving large deformation and free-surface flow. The high computational cost of the SPH method has obstructed its vast application. A lot of research effort has been devoted to accelerating the SPH method using GPU and multi threading. However, developing efficient parallel SPH algorithms on modern high-performance computers (HPCs) remains significantly challenging, especially for simulating real-world engineering problems involving hundreds of millions of particles. In this paper, we proposed an MPI-enabled parallel SPH algorithm and developed the ParallelDualSPHysics1, an open-source software supporting efficient parallel fluid simulations. Based on an efficient domain decomposition scheme, the essential data structure and algorithms of DualSPHysics were refactored to build the parallel version. For collaborating with evenly distributed particles on a distributed-memory HPC system, the parallel particle interaction and particle update modules were introduced, which enabled the SPH solver to synchronize computations among multiple processors using MPI. In addition, the redesigned pre-processing and post-processing capabilities of the ParallelDualSPHysics supported the applications of this software in a wide range of areas. Real-life test cases with up to 120 million particles were simulated and analyzed on a modern HPC system. The results showed that the parallel efficiency of ParallelDualSPHysics exceeds 90 with up to 1024 CPU cores. It indicated that ParallelDualSPHysics has the potential for large-scale engineering applications.

关键词： Computational Fluid Dynamics Smoothed Particle Hydrodynamics ParallelDualSPHysics Parallel computing

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CNN+LSTM Accelerated Turbulent Flow Simulation with Link-Wise Artificial Compressibility Method 21

CNN+LSTM Accelerated Turbulent Flow Simulation with Link-Wis...

引用

50th International Conference on Parallel Processing, ICPP 2021

作者： Fan, Sijiang Fei, Jiawei Guo, Xiao-Wei Yang, Canqun Revell, Alistair Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China School of Mechanical Aerospace and Civil Engineering University of Manchester Manchester United Kingdom

ISBN: (纸本)9781450390682

The simulation of turbulent flow, the most common form of fluid, is indispensable in computational fluid dynamics (CFD). The synthetic eddy method (SEM) generates the turbulent inflow and is adopted as the inlet boundary condition of simulation. However, SEM is time-consuming and can significantly slow down the simulation process which occupies 58% of the whole computational time. This is highly inefficient especially since SEM is only used as the inlet. In this paper we propose an efficient alternative. In particular, we leverage CNN+LSTM to replace SEM to obtain the turbulence statistics and combine it with link-wise artificial compressibility method (LW-ACM), which is a fast numerical method of CFD. We validate the predicted results by CNN+LSTM and prove that our model can provide the correct turbulence statistics even after a long time. Experiment results show that our CNN+LSTM module achieves over 15 × speedup compared with SEM, which greatly reduces the time consumption of turbulent inflow generation (from 58% to 7%). As a result, the whole time of turbulent flow simulation is more than halved. Compared with a newly released GPU-accelerated standard lattice Boltzmann method solver, our combination of CNN+LSTM and LW-ACM is about 8.6 × faster. Among all studies reported to date, our work is the fastest implementation for simulating turbulent channel flow, an important step for the field of fast CFD analysis. © 2021 ACM.

关键词： Computational fluid dynamics

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Emerging theory and phenomena in thermal conduction: A selective review

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Science China(Physics,Mechanics & Astronomy) 2022年第11期65卷 2-17页

作者： Jie Chen Jia He Dongkai Pan Xiaotian Wang Nuo Yang Jiaojiao Zhu Shengyuan A.Yang Gang Zhang Center for Phononics and Thermal Energy Science China-EU Joint Lab for NanophononicsMOE Key Laboratory of Advanced Microstructured MaterialsSchool of Physics Science and EngineeringTongji UniversityShanghai 200092China School of Energy and Power Engineering Huazhong University of Science and Technology(HUST)Wuhan 430074China School of Physical Science and Technology Southwest UniversityChongqing 400715China Research Laboratory for Quantum Materials Singapore University of Technology and DesignSingapore 487372Singapore Institute of High Performance Computing Agency for ScienceTechnology and Research(A*STAR)Singapore 138632Singapore

Recently, emerging phonon phenomena have been discovered and rapidly developed, which have become an active hot research topic. In this review article, we present state-of-the-art advances in several fascinating phonon transport phenomena. First, we summarize the recent progress on the wave nature of phonons, including phonon coherence and its effects on thermal conductivity and the topological properties of phonons. Then, we discuss the particle nature of phonons, including the weak coupling of phonons and the high-order phonon anharmonicity. Finally, we present the summary and a brief outlook. This review presents the advanced understanding of some emerging phonon phenomena in solid materials, which provides new opportunities for further advancement in a wide variety of applications.

关键词： phonon transport coherence topological phonon high-order phonon interaction

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Photonic Flatband Resonances in Multiple Light Scattering

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Physical Review Letters 2024年第4期132卷 043803-043803页

作者： Thanh Xuan Hoang Daniel Leykam Yuri Kivshar Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Republic of Singapore Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore 117543 Singapore Nonlinear Physics Center Research School of Physics Australian National University Canberra ACT 2601 Australia

We introduce the concept of photonic flatband resonances and demonstrate it for an array of high-index dielectric particles. We employ the multiple Mie scattering theory and demonstrate that both short- and long-range interactions between the resonators are crucial for the emerging collective resonances and their associated photonic flatbands. By examining both near- and far-field characteristics, we uncover how the flatbands emerge due to a fine tuning of resonators’ radiation fields, and predict that hybridization of a flatband resonance with an electric hot spot can lead to giant values of the Purcell factor for the electric dipolar emitters.

关键词： Flat bands Mie scattering Nanophotonics

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