检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Jacobs, Ryan Morgan, Dane Attarian, Siamak Meng, Jun Shen, Chen Wu, Zhenghao Xie, Clare Yijia Yang, Julia H. Artrith, Nongnuch Blaiszik, Ben Ceder, Gerbrand Choudhary, Kamal Csanyi, Gabor Cubuk, Ekin Dogus Deng, Bowen Drautz, Ralf Fu, Xiang Godwin, Jonathan Honavar, Vasant Isayev, Olexandr Johansson, Anders Kozinsky, Boris Martiniani, Stefano Ong, Shyue Ping Poltavsky, Igor Schmidt, K.J. Takamoto, So Thompson, Aidan Westermayr, Julia Wood, Brandon M. Department of Materials Science and Engineering University of Wisconsin-Madison MadisonWI55705 United States Harvard University Center for the Environment Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Materials Chemistry and Catalysis Debye Institute for Nanomaterials Science Utrecht University Utrecht3584 CG Netherlands Globus University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory CA94720 United States Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Google DeepMind Mountain ViewCA United States Ruhr-Universität Bochum Bochum44780 Germany Meta United States Orbital Materials London United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Artificial Intelligence Research Laboratory The Pennsylvania State University University ParkPA United States Center for Artificial Intelligence Foundations and Scientific Applications The Pennsylvania State University University ParkPA United States Department of Chemistry Mellon College of Science Carnegie Mellon University PittsburghPA15213 United States Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Courant Institute of Mathematical Sciences New York University New YorkNY10003 United States Center for Soft Matter Research Department of P

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3-10+ years. © 2025, CC BY.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

Tigc-Net: Transformer-Improved Graph Convolution Network for Spatio-Temporal Prediction

SSRN

引用

SSRN 2022年

作者： Chen, Kai Yang, Chunfeng Zhou, Zhengyuan Liu, Yao Ji, Tianjiao Sun, Weiya Chen, Yang School of Cyber Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China The College of Software Engineering Southeast University Nanjing210096 China Laboratory of Image Science and Technology The School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China NHC Key Laboratory of Medical Virology and Viral Diseases National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing China Beijing Institute of Tracking and Communication Technology Beijing100094 China

Modeling spatio-temporal sequences is an important topic yet challenging for existing neural networks. Most of the current spatio-temporal sequence prediction methods usually capture features separately in temporal and spatial dimensions or employ multiple mutually independent local spatio-temporal graphs to represent a spatio-temporal sequence. The first kind of method mentioned above is difficult to mine the complex spatio-temporal correlations, while the other is limited for the accuracy of long-term predictions. To handle these issues, this paper proposes a Transformer-Improved Graph Convolution Network for spatio-temporal prediction. Specifically, the temporal location encoding method is exploited to derive the spatio-temporal characteristics of the sequence utilizing a spatio-temporal feature fusion network. In addition, a spatio-temporal attention network is developed to enhance the spatio-temporal correlation of the sequence, and the dynamic spatial features of sequence are further extracted through the adaptive graph convolution network. A private dataset and a public dataset are employed to demonstrate the performance of the proposed TIGC-Net. The qualitative and quantitative results show that the proposed TIGC-Net can extract dynamic spatiotemporal properties more effectively, enhance the spatio-temporal correlation of sequences and improve the prediction accuracy compared with four stateof-the-art. © 2022, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods

arXiv

引用

arXiv 2023年

作者： Choudhary, Kamal Wines, Daniel Garrity, Kevin F. Williams, Maureen Tavazza, Francesca Li, Kangming Hattrick-Simpers, Jason Gupta, Vishu Romero, Aldo H. Krogel, Jaron T. Saritas, Kayahan Fuhr, Addis Ganesh, Panchapakesan Kent, Paul R.C. Yan, Keqiang Lin, Yuchao Ji, Shuiwang Blaiszik, Ben Reiser, Patrick Friederich, Pascal Agrawal, Ankit Tiwary, Pratyush Beyerle, Eric Minch, Peter Rhone, Trevor David Takeuchi, Ichiro Wexler, Robert B. Mannodi-Kanakkithodi, Arun Ertekin, Elif Mishra, Avanish Mathew, Nithin Wood, Mitchell Rohskopf, Andrew Dale Wang, Shih-Han Achenie, Luke E.K. Xin, Hongliang Biacchi, Adam J. Material Measurement Laboratory National Institute of Standards and Technology MD20899 United States Department of Materials Science and Engineering University of Toronto 27 King’s College Cir TorontoON Canada Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ08544 United States Ludwig Institute for Cancer Research Princeton University PrincetonNJ08544 United States Department of Physics and Astronomy West Virginia University MorgantownWV26506 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Center for Nanophase Materials Science Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States Globus University of Chicago IL60637 United States Data Science Learning Division National Lab ArgonneIL60439 United States Institute of Nanotechnology Karlsruhe Institute of Technology Kaiserstraße 12 Karlsruhe76131 Germany Institute of Theoretical Informatics Karlsruhe Institute of Technology Kaiserstraße 12 Karlsruhe76131 Germany Department of Chemistry and Biochemistry Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States Department of Materials Science and Engineering University of Maryland College ParkMD20742 United States Department of Chemistry Institute of Materials Science and Engineering Washington University in St. Louis St. LouisMO63130 United States School of Materials Engineering Purdue University West LafayetteIN47907 United States Department of Mechan

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://***/jarvis_leaderboard/ © 2023, CC BY.

关键词： Electronic structure

来源：评论

学校读者我要写书评

暂无评论

Innovation, research and Development in engineering Education. Activities of the Spanish Chapter of the IEEE Education Society: Directive Board of the IEEE Education Society Spanish Chapter 2020-2021

Innovation, Research and Development in Engineering Educatio...

引用

Technologies Applied to Electronics Teaching (TAEE)

作者： Ó scar Martí nez Bonastre Cristina Ferná ndez Lluí s Vicent Francisco Mur Manuel Castro Gabriel Diaz Jose Carpio Elio Sancristobal Sergio Martin Jose Sá nchez Francisco Javier Arcega Solsona Juan Suardiaz Muro Manuel Caeiro-Rodrí guez Martí n Llamas Inmaculada Plaza Baltasar Ferná ndez Manjó n Pedro Muñ oz-Merino Edmundo Tovar Telmo Zarraonandia Ayo Javier Garcia Zubia José Antonio Delgado Pení n Department of Statistics Mathematics and Computing Science Operations Research Centre Universidad Miguel Hern&#x00E1 ndez Elche Spain Department of Electronic Technology Universidad Carlos III de Madrid Madrid Spain UPF Barcelona School of Management Barcelona Spain Department of Electrical Electronic and Control Engineering UNED Madrid Spain Department of Electrical Engineering Universidad de Zaragoza Zaragoza Spain Department of Automatics Electrical Engineering and Electronic Technology Technical University of Cartagena Cartagena Spain atlanTTic Research Center University of Vigo Vigo Spain Department of Electronics and Communication Universidad de Zaragoza Zaragoza Spain Department of Software Engineering and Artificial Intelligence Complutense University of Madrid Madrid Spain Department of Telematic Engineering Universidad Carlos III de Madrid Madrid Spain Department of Computer Languages and Systems and Software Engineering Universidad Polit&#x00E9 cnica de Madrid Madrid Spain Department of Computer Science and Engineering Universidad Carlos III de Madrid Madrid Spain Department of Electronic Engineering Universidad de Deusto Bilbao Spain Universitat Polit&#x00E8 cnica de Catalunya Barcelona Spain

ISBN: (数字)9781665421614

ISBN: (纸本)9781665421621

The Technical Committee of the CESEI (Spanish Chapter of the IEEE Education Society) is focused on the Innovation, research and Development (I+R+D) of the education in engineering, mainly Electric and computer engineering. As in other interdisciplinary fields, the I+R+D involves several sources of information and different stakeholders, each one with its own focus and purpose. As a result, sometimes it may be complex for practitioners and researchers to identify the more appropriate entities for their interests. This paper shows how the CESEI Technical Committee tries to solve this difficulty by providing updated information regarding the main publications (journals), events (conferences) and awards to the best academic works. The paper focuses special attention on the activities held during 2020–2021.

关键词： Technological innovation Collaboration Stakeholders engineering education research and development

来源：评论

学校读者我要写书评

暂无评论

One Apollonius Circle is Enough for Many Pursuit-Evasion Games

arXiv

引用

arXiv 2021年

作者： Dorothy, Michael Maity, Dipankar Shishika, Daigo Von Moll, Alexander Computational and Information Sciences Directorate Army Research Laboratory APG MD United States Department of Electrical and Computer Engineering University of North Carolina CharlotteNC United States Department of Mechanical Engineering George Mason University FairfaxVA United States Control Science Center Air Force Research Laboratory WPAFB OH United States

This paper investigates obstacle-free simple motion pursuit-evasion problems where the pursuer is faster and game termination is point capture. It is well known that the interior of the Apollonius Circle (AC) is the evader’s dominance region, however, it was unclear whether the evader could reach outside the initial AC without being captured. We construct a pursuit strategy that guarantees the capture of an evader within an arbitrarily close neighborhood of the initial AC. The pursuer strategy is derived by reformulating the game into a nonlinear control problem, and the guarantee holds against any admissible evader strategy. Our result implies that the evader can freely select the capture location, but only inside the initial AC. Therefore, a class of problems, including those where the payoff is determined solely based on the location of capture, are now trivial. © 2021, CC BY.

关键词： Lyapunov methods

来源：评论

学校读者我要写书评

暂无评论

Varying control intensity of synchronized chaotic system with time delay

Varying control intensity of synchronized chaotic system wit...

引用

2020 International Symposium on Automation, Information and Computing, ISAIC 2020

作者： Onasanya, Babatunde O. Wen, Shiping Feng, Yuming Zhang, Wei Tang, Ning Ademola, Adeleke T. Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Wanzhou Chongqing404100 China Department of Mathematics University of Ibadan Ibadan Nigeria Australian AI Institute University of Technology Sydney UltimoNSW2007 Australia School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing404100 China Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology Chongqing Three Gorges University Wanzhou Chongqing404100 China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing404100 China Department of Mathematics Obafemi Awolowo University Post Code Ile-Ife220005 Nigeria

Hyperchaotic system is a very useful tool in secure and encrypted communications. But situations arise when engineers and scientists seek to synchronize two hyperchaotic systems. This gives another (error) system. The goal is to minimize the error as much as can be in order to make one system look like the other by synchronization. This is a particularly challenging situation. In this paper, two hyperchaotic systems are synchronized by impulsive control. Also, the condition for uniform asymptotic stability of the synchronized error system was given. Finally, the simulation results to justify the reliability of this method is also presented. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Chaotic systems

来源：评论

学校读者我要写书评

暂无评论

Climate warming restructures seasonal dynamics of grassland soil microbial communities

引用

mLife 2022年第3期1卷 245-256页

作者： Xue Guo Mengting Yuan Jiesi Lei Zhou Shi Xishu Zhou Jiabao Li Ye Deng Yunfeng Yang Liyou Wu Yiqi Luo James MTiedje Jizhong Zhou State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijingChina Institute for Environmental Genomics and Department of Microbiology and Plant Biology University of OklahomaNormanOklahomaUSA Department of Environmental Science Policyand ManagementUniversity of CaliforniaBerkeleyCaliforniaUSA Gladstone Institutes and Chan‐Zuckerberg Biohub San FranciscoCaliforniaUSA School of Minerals Processing and Bioengineering Central South UniversityChangshaChina Key Laboratory of Environmental and Applied Microbiology Chinese Academy of Sciences and Environmental Microbiology&Key Laboratory of Sichuan ProvinceChengdu Institute of BiologyChinese Academy of SciencesChengduChina Key Laboratory of Environmental Biotechnology Research Center for Eco‐Environmental SciencesChinese Academy of SciencesBeijingChina Department of Biological Sciences Center for Ecosystem Science and SocietyNorthern Arizona UniversityFlagstaffArizonaUSA Center for Microbial Ecology Michigan State UniversityEast LansingMichiganUSA School of Civil Engineering and Environmental Sciences University of OklahomaNormanOklahomaUSA Earth and Environmental Sciences Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA School of Computer Sciences University of OklahomaNormanOklahomaUSA.

Soil microbial community's responses to climate warming alter the global carbon *** temperate ecosystems,soil microbial communities function along seasonal ***,little is known about how the responses of soil microbial communities to warming vary when the season *** this study,we investigated the seasonal dynamics of soil bacterial community under experimental warming in a temperate tall‐grass prairie *** results showed that warming significantly(p=0.001)shifted community structure,such that the differences of microbial communities between warming and control plots increased nonlinearly(R^(2)=0.578,p=0.021)from spring to ***,warming significantly(p<0.050)increased microbial network complexity and robustness,especially during the colder seasons,despite large variations in network size and complexity in different *** addition,the relative importance of stochastic processes in shaping the microbial community decreased by warming in fall and winter but not in spring and *** study indicates that climate warming restructures the seasonal dynamics of soil microbial community in a temperate *** seasonality of microbial responses to warming may enlarge over time and could have significant impacts on the terrestrial carbon cycle.

关键词： climate warming microbial community microbial network seasonal dynamics

来源：评论

学校读者我要写书评

暂无评论

Infrared and visual image fusion via multi-modal decomposition and PCNN in gradient domain fusion measure 2nd

Infrared and visual image fusion via multi-modal decompositi...

引用

2nd International Conference on Smart Multimedia, ICSM 2019

作者： Tan, Wei Zhang, Jiajia Qian, Kun Du, Juan Xiang, Pei Zhou, Huixin School of Physics and Optoelectronic Engineering Xidian University Xi’an710071 China Department of Computer Science University of Copenhagen Copenhagen2100 Denmark Shanghai Institute of Spaceflight Control Technology Shanghai201109 China Infrared Detection Technology Research and Development Center of CASC Shanghai201109 China

ISBN: (纸本)9783030544065

Infrared and visual image fusion aims to obtain a complex image which contains more recognizable information. To obtain the complex image, a fusion algorithm via multi-modal decomposition and pulse-coupled neural network (PCNN) in gradient domain fusion measure is proposed. Firstly, the source images are decomposed into three layers through the decomposition model. Then, a gradient domain PCNN fusion measure is employed in the three layers. Finally, the fused image is reconstructed through the three fused layers. Experimental results demonstrate that the proposed algorithm performs effectively in both qualitative and quantitative measures. © Springer Nature Switzerland AG 2020.

关键词： Image fusion

来源：评论

学校读者我要写书评

暂无评论

Resource-efficient Direct Characterization of General Density Matrix

arXiv

引用

arXiv 2023年

作者： Xu, Liang Zhou, Mingti Tao, Runxia Zhong, Zhipeng Wang, Ben Cao, Zhiyong Xia, Hongkuan Wang, Qianyi Zhan, Hao Zhang, Aonan Yu, Shang Xu, Nanyang Dong, Ying Ren, Changliang Zhang, Lijian College of Metrology and Measurement Engineering China Jiliang University Hangzhou310018 China National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and Manipulation College of Engineering and Applied Sciences Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China Research Center for Quantum Sensing Zhejiang Lab Hangzhou310000 China School of Electronic Control Chang’an University Xi’an710064 China Quantum Optics and Laser Science Blackett Laboratory Imperial College London Prince Consort Rd LondonSW7 2AZ United Kingdom Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China

Sequential weak measurements allow for the direct extraction of individual density-matrix elements, rather than relying on global reconstruction of the entire density matrix, which opens a new avenue for the characterization of quantum systems. Nevertheless, extending the sequential scheme to multi-qudit quantum systems is challenging due to the requirement of multiple coupling processes for each qudit and the lack of appropriate precision evaluation. To address these issues, we propose a resource-efficient scheme (RES) that directly characterizes the density matrix of general multi-qudit systems while optimizing measurements and establishing a feasible estimation analysis. In the RES, an efficient observable of the quantum system is constructed such that a single meter state coupled to each qudit is sufficient to extract the corresponding density-matrix element. An appropriate model based on the statistical distribution of errors is utilized to evaluate the precision and feasibility of the scheme. We have experimentally applied the RES to the direct characterization of general single-photon qutrit states and two-photon entangled states. The results show that the RES outperforms sequential schemes in terms of efficiency and precision in both weak- and strong-coupling scenarios. This work sheds new light on the practical characterization of large-scale quantum systems and the investigation of their non-classical properties. © 2023, CC BY.

关键词： Photons

来源：评论

学校读者我要写书评

暂无评论

Traceable random numbers from a nonlocal quantum advantage

arXiv

引用

arXiv 2024年

作者： Kavuri, Gautam A. Palfree, Jasper Reddy, Dileep V. Zhang, Yanbao Bienfang, Joshua C. Mazurek, Michael D. Alhejji, Mohammad A. Siddiqui, Aliza U. Cavanagh, Joseph M. Dalal, Aagam Abellán, Carlos Amaya, Waldimar Mitchell, Morgan W. Stange, Katherine E. Beale, Paul D. Brandão, Luís T.A.N. Booth, Harold Peralta, René Nam, Sae Woo Mirin, Richard P. Stevens, Martin J. Knill, Emanuel Shalm, Lynden K. Department of Physics University of Colorado BoulderCO80309 United States The National Institute of Standards and Technology BoulderCO80305 United States Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Joint Quantum Institute National Institute of Standards and Technology University of Maryland 100 Bureau Drive GaithersburgMD20899 United States Center for Quantum Information and Control University of New Mexico AlbuquerqueNM87131 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Quside Technologies S.L. Castelldefels Barcelona Spain ICFO-Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Barcelona08860 Spain ICREA - Institució Catalana de Recerca i Estudis Avançats Barcelona08010 Spain Department of Mathematics University of Colorado BoulderCO80309 United States GaithersburgMD20899 United States Information Technology Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Physical Measurement Laboratory National Institute of Standards and Technology BoulderCO80305 United States Center for Theory of Quantum Matter University of Colorado BoulderCO80305 United States Applied and Computational Mathematics Division National Institute of Standards and Technology BoulderCO80305 United States Quantum Engineering Initiative Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States

The unpredictability of random numbers is fundamental to both digital security [1, 2] and applications that fairly distribute resources [3, 4]. However, existing random number generators have limitations—the generation processes cannot be fully traced, audited, and certified to be unpredictable. The algorithmic steps used in pseudorandom number generators [5] are auditable, but they cannot guarantee that their outputs were a priori unpredictable given knowledge of the initial seed. Device-independent quantum random number generators [6–9] can ensure that the source of randomness was unknown beforehand, but the steps used to extract the randomness are vulnerable to tampering. Here, for the first time, we demonstrate a fully traceable random number generation protocol based on device-independent techniques. Our protocol extracts randomness from unpredictable non-local quantum correlations, and uses distributed intertwined hash chains to cryptographically trace and verify the extraction process. This protocol is at the heart of a public traceable and certifiable quantum randomness beacon that we have launched [10]. Over the first 40 days of operation, we completed the protocol 7434 out of 7454 attempts—a success rate of 99.7%. Each time the protocol succeeded, the beacon emitted a pulse of 512 bits of traceable randomness. The bits are certified to be uniform with error times actual success probability bounded by 2−64. The generation of certifiable and traceable randomness represents one of the first public services that operates with an entanglement-derived advantage over comparable classical approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Random number generation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：