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Color dependence of the topological susceptibility in Yang-Mills theories

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arXiv 2022年

作者： Bennett, Ed Hong, Deog Ki Lee, Jong-Wan David Lin, C.-J. Lucini, Biagio Piai, Maurizio Vadacchino, Davide Swansea Academy of Advanced Computing Swansea University Fabian Way Wales SwanseaSA1 8EN United Kingdom Department of Physics Pusan National University Busan46241 Korea Republic of Institute for Extreme Physics Pusan National University Busan46241 Korea Republic of Institute of Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu30010 Taiwan Center for High Energy Physics Chung-Yuan Christian University Chung-Li32023 Taiwan Centre for Theoretical and Computational Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu30010 Taiwan Physics Division National Centre for Theoretical Sciences Taipei10617 Taiwan Department of Mathematics Faculty of Science and Engineering Swansea University Fabian Way Wales SwanseaSA1 8EN United Kingdom Department of Physics Faculty of Science and Engineering Swansea University Singleton Park Wales SwanseaSA2 8PP United Kingdom School of Mathematics Hamilton Mathematics Institute Trinity College Dublin 2 Ireland Centre for Mathematical Sciences University of Plymouth PlymouthPL4 8AA United Kingdom

For Yang-Mills theories in four dimensions, we propose to rescale the ratio between topological susceptibility and string tension squared in a universal way, dependent only on group factors. We apply this suggestion to SU(Nc) and Sp(Nc) groups, and compare lattice measurements performed by several independent collaborations. We show that the two sequences of (rescaled) numerical results in these two families of groups are compatible with each other. We hence perform a combined fit, and extrapolate to the common large-Nc limit. © 2022, CC BY.

关键词： Topology

Emergence of steady quantum transport in a superconducting processor

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arXiv 2024年

作者： Zhang, Pengfei Gao, Yu Xu, Xiansong Wang, Ning Dong, Hang Guo, Chu Deng, Jinfeng Zhang, Xu Chen, Jiachen Xu, Shibo Wang, Ke Wu, Yaozu Zhang, Chuanyu Jin, Feitong Zhu, Xuhao Zhang, Aosai Zou, Yiren Tan, Ziqi Cui, Zhengyi Zhu, Zitian Shen, Fanhao Li, Tingting Zhong, Jiarun Bao, Zehang Zhao, Liangtian Hao, Jie Li, Hekang Wang, Zhen Song, Chao Guo, Qiujiang Wang, H. Poletti, Dario School of Physics ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control Zhejiang University Hangzhou China Science Mathematics and Technology Cluster Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore College of Physics and Electronic Engineering Center for Computational Sciences Sichuan Normal University Chengdu610068 China Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou450001 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore Singapore

Non-equilibrium quantum transport is crucial to technological advances ranging from nanoelectronics to thermal management. In essence, it deals with the coherent transfer of energy and (quasi-)particles through quantum channels between thermodynamic baths. A complete understanding of quantum transport thus requires the ability to simulate and probe macroscopic and microscopic physics on equal footing. Using a superconducting quantum processor, we demonstrate the emergence of non-equilibrium steady quantum transport by emulating the baths with qubit ladders and realising steady particle currents between the baths. We experimentally show that the currents are independent of the microscopic details of bath initialisation, and their temporal fluctuations decrease rapidly with the size of the baths, emulating those predicted by thermodynamic baths. The above characteristics are experimental evidence of pure-state statistical mechanics and prethermalisation in non-equilibrium many-body quantum systems. Furthermore, by utilising precise controls and measurements with single-site resolution, we demonstrate the capability to tune steady currents by manipulating the macroscopic properties of the baths, including filling and spectral properties. Our investigation paves the way for a new generation of experimental exploration of non-equilibrium quantum transport in strongly correlated quantum matter. © 2024, CC BY.

关键词： Statistical mechanics

DeePMD-kit v2: A software package for Deep Potential models

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

Is there evidence for exponential quantum advantage in quantum chemistry?

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arXiv 2022年

作者： Lee, Seunghoon Lee, Joonho Zhai, Huanchen Tong, Yu Dalzell, Alexander M. Kumar, Ashutosh Helms, Phillip Gray, Johnnie Cui, Zhi-Hao Liu, Wen-Yuan Kastoryano, Michael Babbush, Ryan Preskill, John Reichman, David R. Campbell, Earl T. Valeev, Edward F. Lin, Lin Chan, Garnet Kin-Lic Division of Chemistry and Chemical Engineering California Institute of Technology PasadenaCA91125 United States Department of Chemistry Columbia University New YorkNY10027 United States Department of Mathematics University of California BerkeleyCA94720 United States Aws Center for Quantum Computing PasadenaCA91125 United States Department of Chemistry Virginia Tech BlacksburgVA24061 United States Amazon Quantum Solutions Lab SeattleWA98170 United States Google Quantum AI 340 Main Street VeniceCA90291 United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States Institute for Quantum Information and Matter California Institute of Technology PasadenaCA91125 United States Riverlane Cambridge United Kingdom

The idea to use quantum mechanical devices to simulate other quantum systems is commonly ascribed to Feynman. Since the original suggestion, concrete proposals have appeared for simulating molecular and materials chemistry through quantum computation, as a potential "killer application" [1, 2, 3, 4, 5]. Indications of potential exponential quantum advantage in artificial tasks [6, 7, 8, 9] have increased interest in this application, thus, it is critical to understand the basis for potential exponential quantum advantage in quantum chemistry. Here we gather the evidence for this case in the most common task in quantum chemistry, namely, ground-state energy estimation. We conclude that evidence for such an exponential advantage across chemical space has yet to be found. While quantum computers may still prove useful for quantum chemistry, it may be prudent to assume exponential speedups are not generically available for this problem. © 2022, CC BY.

关键词： Quantum chemistry

Sp(2N) Yang-Mills theories on the lattice: scale setting and topology

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arXiv 2022年

作者： Bennett, Ed Hong, Deog Ki Lee, Jong-Wan Lin, C.-J. David Lucini, Biagio Piai, Maurizio Vadacchino, Davide Swansea Academy of Advanced Computing Swansea University Fabian Way Swansea WalesSA1 8EN United Kingdom Department of Physics Pusan National University Busan46241 Korea Republic of Institute for Extreme Physics Pusan National University Busan46241 Korea Republic of Institute of Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu30010 Taiwan Center for High Energy Physics Chung-Yuan Christian University Chung-Li32023 Taiwan Centre for Theoretical and Computational Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu30010 Taiwan Physics Division National Centre for Theoretical Sciences Taipei10617 Taiwan Department of Mathematics Faculty of Science and Engineering Swansea University Fabian Way Swansea WalesSA1 8EN United Kingdom Department of Physics Faculty of Science and Engineering Swansea University Singleton Park Swansea WalesSA2 8PP United Kingdom School of Mathematics Hamilton Mathematics Institute Trinity College Dublin 2 Ireland Centre for Mathematical Sciences University of Plymouth PlymouthPL4 8AA United Kingdom

We study Yang-Mills lattice theories with Sp(Nc) gauge group, with Nc = 2N, for N = 1, ···, 4. We show that if we divide the renormalised couplings appearing in the Wilson flow by the quadratic Casimir C2(F) of the Sp(Nc) group, then the resulting quantities display a good agreement among all values of Nc considered, over a finite interval in flow time. We use this scaled version of the Wilson flow as a scale-setting procedure, compute the topological susceptibility of the Sp(Nc) theories, and extrapolate the results to the continuum limit for each Nc. © 2022, CC BY.

关键词： Lattice theory

EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) 2.0: A MANIFESTO OF OPEN CHALLENGES AND INTERDISCIPLINARY RESEARCH DIRECTIONS

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arXiv 2023年

作者： Longo, Luca Brcic, Mario Cabitza, Federico Choi, Jaesik Confalonieri, Roberto Ser, Javier Del Guidotti, Riccardo Hayashi, Yoichi Herrera, Francisco Holzinger, Andreas Jiang, Richard Khosravi, Hassan Lecue, Freddy Malgieri, Gianclaudio Páez, Andrés Samek, Wojciech Schneider, Johannes Speith, Timo Stumpf, Simone The Artificial Intelligence and Cognitive Load Research Lab Technological University Dublin Ireland University of Zagreb Faculty of Electrical Engineering and Computing Croatia University of Milano-Bicocca Milan Italy IRCCS Ospedale Galeazzi Sant’Ambrogio Milan Italy Kim Jaechul Graduate School of AI Korea Advanced Institute of Science & Technology Korea Republic of INEEJI Corporation Korea Republic of Department of Mathematics University of Padua Italy Derio Spain Bilbao Spain University of Pisa Pisa Italy Department of Computer Science Meiji University Tokyo Japan Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute in Data Science & Computational Intelligence University of Granada Granada Spain Human-Centered AI Lab University of Natural Resources and Life Sciences Vienna Austria School of Computing and Communications Lancaster University United Kingdom The University of Queensland Brisbane Australia Sophia Antipolis France eLaw Center for Law and Digital Technologies Leiden University Netherlands Department of Philosophy Universidad de los Andes Bogotá Colombia Center for Research & Formation in Artificial Intelligence Universidad de los Andes Bogotá Colombia Technical University of Berlin Berlin Germany Fraunhofer Heinrich Hertz Institute Berlin Germany Berlin Germany Department of Information Systems and Computer Science University of Liechtenstein Liechtenstein Liechtenstein Department of Philosophy University of Bayreuth Bayreuth Germany Center for Perspicuous Computing Saarland University Saarbrücken Germany School of Computing Science University of Glasgow United Kingdom

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. © 2023, CC BY-SA.

关键词： Ethical technology

thornado-transport: IMEX schemes for two-moment neutrino transport respecting Fermi-Dirac statistics

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Journal of Physics: Conference Series 2019年第1期1225卷

作者： Ran Chu Eirik Endeve Cory D. Hauck Anthony Mezzacappa Bronson Messer Department of Physics and Astronomy University of Tennessee Knoxville TN 37996-1200 Computational and Applied Mathematics Group Oak Ridge National Laboratory Oak Ridge TN 37831 USA Department of Physics and Astronomy University of Tennessee Knoxville TN 37996-1200 and Joint Institute for Computational Sciences Oak Ridge National Laboratory Oak Ridge TN 37831 USA Computational and Applied Mathematics Group Oak Ridge National Laboratory Oak Ridge TN 37831 USA and Department of Mathematics University of Tennessee Knoxville TN 37996-1320 Department of Physics and Astronomy University of Tennessee Knoxville TN 37996-1200 and Joint Institute for Computational Sciences Oak Ridge National Laboratory Oak Ridge TN 37831 USA Scientific Computing and Theoretical Physics Groups Oak Ridge National Laboratory Oak Ridge TN 37831 USA and Department of Physics and Astronomy University of Tennessee Knoxville TN 37996-1200

We develop implicit-explicit (IMEX) schemes for neutrino transport in a background material in the context of a two-moment model that evolves the angular moments of a neutrino phase-space distribution function. Considering the upper and lower bounds that are introduced by Pauli's exclusion principle on the moments, an algebraic moment closure based on Fermi-Dirac statistics and a convex-invariant time integrator both are demanded. A finite-volume/first-order discontinuous Galerkin(DG) method is used to illustrate how an algebraic moment closure based on Fermi-Dirac statistics is needed to satisfy the bounds. Several algebraic closures are compared with these bounds in mind, and the Cernohorsky and Bludman closure, which satisfies the bounds, is chosen for our IMEX schemes. For the convex-invariant time integrator, two IMEX schemes named PD-ARS have been proposed. PD-ARS denotes a convex-invariant IMEX Runge-Kutta scheme that is high-order accurate in the streaming limit, and works well in the diffusion limit. Our two PD-ARS schemes use second-and third-order, explicit, strong-stability-preserving Runge-Kutta methods as their explicit part, respectively, and therefore are second-and third-order accurate in the streaming limit, respectively. The accuracy and convex-invariance of our PD-ARS schemes are demonstrated in the numerical tests with a third-order DG method for spatial discretization and a simple Lax-Friedrichs flux. The method has been implemented in our high-order neutrino-radiation hydrodynamics (thornado) toolkit. We show preliminary results employing tabulated neutrino opacities.

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Success-Driven Opinion Formation Determines Social Tensions

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SSRN

SSRN 2023年

作者： Chica, Manuel Perc, Matjaž Santos, Francisco C. Andalusian Research Institute DaSCI "Data Science and Computational Intelligence" University of Granada Granada18071 Spain School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia Instituto Superior Técnico Universidade de Lisboa Porto Salvo2744-016 Portugal Faculty of Natural Sciences and Mathematics University of Maribor Koroška cesta 160 Maribor2000 Slovenia Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Alma Mater Europaea Slovenska ulica 17 Maribor2000 Slovenia Complexity Science Hub Vienna Josefstädterstraße 39 Vienna1080 Austria Department of Physics Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul Korea Republic of

Polarization is common in politics and public opinion. It is believed to be shaped by the media as well as ideologies, and often incited by misinformation. However, little is known about the microscopic dynamics behind polarization and the resulting social tensions. By coupling together opinion formation with the strategy selection in different social dilemmas, we reveal how success at an individual level transforms to global consensus or lack thereof. When defection carries with it the fear of punishment in the absence of greed, as in the stag-hunt game, opinion fragmentation is smallest. Conversely, if defection promises a higher payoff and thus evokes also greed, like in the prisoner's dilemma and the snowdrift game, consensus is much more difficult to attain. Our research thus challenges the top-down narrative of social tensions, showing that they may well originate from fundamental principles at individual level, like the desire to prevail in pairwise evolutionary comparisons. © 2023, The Authors. All rights reserved.

关键词： Polarization

The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies

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arXiv 2024年

作者： Joachimiak, Marcin P. Miller, Mark A. Caufield, J. Harry Ly, Ryan Harris, Nomi L. Tritt, Andrew Mungall, Christopher J. Bouchard, Kristofer E. Biosystems Data Science Department Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Scientific Data Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Biological Systems & Engineering Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Helen Wills Neuroscience Institute UC Berkeley BerkeleyCA94720 United States Redwood Center for Theoretical Neuroscience UC Berkeley BerkeleyCA94720 United States

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. The ontology is structured around six top-level branches: Networks, Layers, Functions, LLMs, Preprocessing, and Bias, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO's development utilized the Ontology Development Kit (ODK) for its creation and maintenance, with its content being dynamically updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting it potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub (https://***/berkeleybop/artificial-intelligence-ontology) and BioPortal (https://***/ontologies/AIO). © 2024, CC BY.

关键词： Deep neural networks