检索结果-内蒙古大学图书馆

AN ALGORITHM FOR CONVEX POLYTOPES

学校读者我要写书评

暂无评论

JOURNAL OF THE ACM 1970年第1期17卷 78-&页

作者： CHAND, DR KAPUR, SS Advanced Computing Techniques Department Lockheed-Georgia Company Marietta Georgia Research Laboratory Lockheed-Georgia Company Marietta Georgia

An algorithm, one that is economical and fast, for generating the convex polytope of a set S of points lying in an n-dimensional Euclidean space En is described. In the existing brute force method for determining the ... 详细信息

An algorithm, one that is economical and fast, for generating the convex polytope of a set S of points lying in an n-dimensional Euclidean space Eⁿ is described. In the existing brute force method for determining the convex hull of a set of points lying in a two-dimensional space, one computes all possible straight lines joining each pair of points of S and tests whether the lines bound the given set S. This method can easily be generalized for computing the convex hull of a set S ⊂ Eⁿ, > 2. However, it turns out that this approach is not feasible due to excessive computer run time for a set of points lying in E n when n > 3. The algorithm described in this paper avoids all the unnecessary calculations, and the convex polytope of a set S ⊂ E n is generated by systematically computing the faces from the edges of the desired convex polytope. A numerical comparison indicates that this new approach is far superior to the existing brute force technique. © 1970, ACM. All rights reserved.

关键词： algorithm convex convex hull edges faces fiats hyperplanes polytope

Integrated software, process, algorithm and application visualization

学校读者我要写书评

暂无评论

The Journal of Visualization and Computer Animation 1992年第4期3卷 201-218页

作者： Friedell, Mark Kochhar, Sandeep Lapolla, Mark Vincent Marks, Joe Department of Computer Science University of Waterloo Waterloo Ontario N2L 3G1 Canada Bedford Massachusetts 01730 United States Lockheed Advanced Computing Laboratory Palo Alto California 94304 United States DEC Cambridge Research Laboratory Cambridge Massachusetts 02139 United States

We present a unifying view of visualization in which comprehensive support for software, algorithm, process and application (including scientific) visualizations is treated as an essential component of the computing environment. We describe our recent work toward making this view a reality: a tool for computer‐assisted design of visualizations and a technique for monitoring a computational process in a non‐invasive fashion. Copyright © 1992 John Wiley & Sons, Ltd

关键词： Algorithm visualization Automated design of graphical displays Co‐operative design and modelling Human‐computer interaction Program visualization Scientific visualization Software diagrams Visualization techniques

Quantum simulation of φ4theories in qudit systems

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Kurkcuoglu, Doga Murat Alam, M. Sohaib Job, Joshua Adam Li, Andy C.Y. MacRidin, Alexandru Perdue, Gabriel N. Providence, Stephen Fermi National Accelerator Laboratory BataviaIL60510 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Rigetti Computing BerkeleyCA94701 United States Lockheed Martin Advanced Technology Center SunnyvaleCA94089 United States Coppin State University BaltimoreMD21216 United States

We discuss the implementation of quantum algorithms for lattice Φ4theory on a circuit quantum electrodynamics (cQED) system. The field is represented on qudits using a discretized field amplitude basis. The main advantage of a qudit system is that its multi-level characteristics allows the field interaction to be implemented with only diagonal single-qudit gates. Considering the set of universal gates formed by the single-qudit phase gate and the displacement gate, we address initial state preparation and single-qudit gate synthesis with variational methods. © 2021, CC BY.

关键词： Quantum chemistry

Benchmarking variational quantum eigensolvers for the square-octagon-lattice Kitaev model

学校读者我要写书评

暂无评论

Physical Review Research 2023年第3期5卷 033071-033071页

作者： Andy C. Y. Li M. Sohaib Alam Thomas Iadecola Ammar Jahin Joshua Job Doga Murat Kurkcuoglu Richard Li Peter P. Orth A. Barış Özgüler Gabriel N. Perdue Norm M. Tubman Fermi National Accelerator Laboratory Batavia Illinois 60510 USA Rigetti Computing Berkeley California 94701 USA Department of Physics and Astronomy Iowa State University Ames Iowa 50011 USA Ames National Laboratory Ames Iowa 50011 USA Department of Physics University of Florida 2001 Museum Road Gainesville Florida 32611 USA Lockheed Martin Advanced Technology Center Sunnyvale California 94089 USA Department of Physics Yale University New Haven Connecticut 06520 USA Quantum Artificial Intelligence Laboratory Exploration Technology Directorate NASA Ames Research Center Moffett Field California 94035 USA

Quantum spin systems may offer the first opportunities for beyond-classical quantum computations of scientific interest. While general quantum simulation algorithms likely require error-corrected qubits, there may be applications of scientific interest prior to the practical implementation of quantum error correction. The variational quantum eigensolver (VQE) is a promising approach to finding energy eigenvalues on noisy quantum computers. Lattice models are of broad interest for use on near-term quantum hardware due to the sparsity of the number of Hamiltonian terms and the possibility of matching the lattice geometry to the hardware geometry. Here, we consider the Kitaev spin model on a hardware-native square-octagon qubit connectivity map, and examine the possibility of efficiently probing its rich phase diagram with VQE approaches. By benchmarking different choices of variational Ansatz states and classical optimizers, we illustrate the advantage of a mixed optimization approach using the Hamiltonian variational Ansatz (HVA) and the potential of probing the system's phase diagram using VQE. We further demonstrate the implementation of HVA circuits on Rigetti's Aspen-9 chip with error mitigation.

关键词： Quantum algorithms Quantum computation Quantum simulation

The SunPy Project: An Interoperable Ecosystem for Solar Data Analysis

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Barnes, Will T. Christe, S. Freij, N. Hayes, Laura A. Stansby, David Ireland, Jack Mumford, Stuart J. Ryan, Daniel F. Shih, Albert Y. NASA Goddard Space Flight Center GreenbeltMD United States American University WashingtonDC United States Lockheed Martin Solar and Astrophysics Laboratory Palo AltoCA United States Bay Area Environmental Research Institute Moffett FieldCA United States ESTEC European Space Agency Noordwijk Netherlands Advanced Research Computing Centre University College London London United Kingdom University of Applied Sciences and Arts Northwest Switzerland Windisch Switzerland Aperio Software Ltd Leeds United Kingdom

The SunPy Project is a community of scientists and software developers creating an ecosystem of Python packages for solar physics. The project includes the sunpy core package as well as a set of affiliated packages. The sunpy core package provides general purpose tools to access data from different providers, read image and time series data, and transform between commonly used coordinate systems. Affiliated packages perform more specialized tasks that do not fall within the more general scope of the sunpy core package. In this article, we give a high-level overview of the SunPy Project, how it is broader than the sunpy core package, and how the project curates and fosters the affiliated package system. We demonstrate how components of the SunPy ecosystem, including sunpy and several affiliated packages, work together to enable multi-instrument data analysis workflows. We also describe members of the SunPy Project and how the project interacts with the wider solar physics and scientific Python communities. Finally, we discuss the future direction and priorities of the SunPy Project. © 2023, CC BY.

关键词： Python

Benchmarking variational quantum eigensolvers for the square-octagon-lattice Kitaev model

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Li, Andy C.Y. Alam, M. Sohaib Iadecola, Thomas Jahin, Ammar Job, Joshua Kurkcuoglu, Doga Murat Li, Richard Orth, Peter P. Özgüler, A. Barış Perdue, Gabriel N. Tubman, Norm M. Fermi National Accelerator Laboratory BataviaIL60510 United States Rigetti Computing BerkeleyCA94701 United States Department of Physics and Astronomy Iowa State University AmesIA50011 United States Ames National Laboratory AmesIA50011 United States Department of Physics University of Florida 2001 Museum Rd GainesvilleFL32611 United States Lockheed Martin Advanced Technology Center SunnyvaleCA94089 United States Department of Physics Yale University New HavenCT06520 United States Exploration Technology Directorate NASA Ames Research Center Moffett FieldCA94035 United States

Quantum spin systems may offer the first opportunities for beyond-classical quantum computations of scientific interest. While general quantum simulation algorithms likely require error-corrected qubits, there may be applications of scientific interest prior to the practical implementation of quantum error correction. The variational quantum eigensolver (VQE) is a promising approach to finding energy eigenvalues on noisy quantum computers. Lattice models are of broad interest for use on near-term quantum hardware due to the sparsity of the number of Hamiltonian terms and the possibility of matching the lattice geometry to the hardware geometry. Here, we consider the Kitaev spin model on a hardware-native square-octagon qubit connectivity map, and examine the possibility of efficiently probing its rich phase diagram with VQE approaches. By benchmarking different choices of variational ansatz states and classical optimizers, we illustrate the advantage of a mixed optimization approach using the Hamiltonian variational ansatz (HVA) and the potential of probing the system’s phase diagram using VQE. We further demonstrate the implementation of HVA circuits on Rigetti’s Aspen-9 chip with error mitigation. Copyright © 2021, The Authors. All rights reserved.

关键词： Hamiltonians

The association for the advancement of artificial intelligence 2020 workshop program

学校读者我要写书评

暂无评论

The association for the advancement of artificial intelligen...

作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词：

Slow Solar Wind Connection Science during Solar Orbiter’s First Close Perihelion Passage

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Yardley, Stephanie L. Owen, Christopher J. Long, David M. Baker, Deborah Brooks, David H. Polito, Vanessa Green, Lucie M. Matthews, Sarah Owens, Mathew Lockwood, Mike Stansby, David James, Alexander W. Valori, Gherardo Giunta, Alessandra Janvier, Miho Ngampoopun, Nawin Mihailescu, Teodora To, Andy S.H. van Driel-Gesztelyi, Lidia Démoulin, Pascal D’Amicis, Raffaella French, Ryan J. Suen, Gabriel H.H. Roulliard, Alexis P. Pinto, Rui F. Réville, Victor Watson, Christopher J. Walsh, Andrew P. De Groof, Anik Williams, David R. Zouganelis, Ioannis Müller, Daniel Berghmans, David Auchère, Frédéric Harra, Louise Schuehle, Udo Barczynski, Krysztof Buchlin, Éric Cuadrado, Regina Aznar Kraaikamp, Emil Mandal, Sudip Parenti, Susanna Peter, Hardi Rodriguez, Luciano Schwanitz, Conrad Smith, Phil Teriaca, Luca Verbeeck, Cis Zhukov, Andrei N. De Pontieu, Bart Horbury, Tim Solanki, Sami K. del Toro Iniesta, Jose Carlos Woch, Joachim Gandorfer, Achim Hirzberger, Johann Súarez, David Orozco Appourchaux, Thierry Calchetti, Daniele Sinjan, Jonas Kahil, Fatima Albert, Kinga Volkmer, Reiner Carlsson, Mats Fludra, Andrzej Hassler, Don Caldwell, Martin Fredvik, Terje Grundy, Tim Guest, Steve Haberreiter, Margit Leeks, Sarah Pelouze, Gabriel Plowman, Joseph Schmutz, Werner Sidher, Sunil Thompson, William T. Louarn, Philippe Federov, Andrei Department of Meteorology University of Reading Reading United Kingdom University College London Mullard Space Science Laboratory Holmbury St. Mary Dorking SurreyRH5 6NT United Kingdom Paseo Manuel de Lardizabal 4 San Sebastián20018 Spain Queen’s University Belfast University Road BelfastBT7 1NN United Kingdom College of Science George Mason University 4400 University Drive FairfaxVA22030 United States Bay Area Environmental Research Institute NASA Research Park Moffett FieldCA94035 United States Lockheed Martin Solar & Astrophysics Laboratory 3251 Hanover Street Palo AltoCA94304 United States Department of Physics Oregon State University 301 Weniger Hall CorvallisOR97331 United States Advanced Research Computing Centre University College London Gower St Bloomsbury LondonWC1E 6BT United Kingdom Max-Planck-Institut für Sonnensystemforschung Justus-von-Liebig-Weg 3 Göttingen37077 Germany RAL Space UKRI STFC Rutherford Appleton Laboratory Harwell DidcotOX11 0QX United Kingdom Université Paris-Saclay CNRS Institut d’Astrophysique Spatiale OrsayF-91405 France Laboratoire Cogitamus rue Descartes Paris75005 France LESIA Observatoire de Paris Université PSL CNRS Sorbonne Université Univ. Paris Diderot Sorbonne Paris Cité 5 place Jules Janssen Meudon92195 France Konkoly Observatory Research Centre for Astronomy and Earth Sciences Konkoly Thege út 15-17. BudapestH-1121 Hungary National Institute for Astrophysics Institute for Space Astrophysics and Planetology Rome Italy National Solar Observatory 3665 Innovation Drive BoulderCO80303 United States IRAP Université Toulouse III – Paul Sabatier CNRS CNES Toulouse France Dept. d’Astrophysique/AIM CEA/IRFU CNRS INSU Université Paris-Saclay Gif-sur-Yvette91191 Cedex France Camino Bajo del Castillo s/n Villanueva De La Cañada MadridE-28692 Spain European Space Agency ESTEC Noordwijk Netherlands Solar-Terrestrial Centre of Excellence – SIDC Royal Observatory of Belgium R

The Slow Solar Wind Connection Solar Orbiter Observing Plan (Slow Wind SOOP) was developed to utilise the extensive suite of remote sensing and in situ instruments on board the ESA/NASA Solar Orbiter mission to answer significant outstanding questions regarding the origin and formation of the slow solar wind. The Slow Wind SOOP was designed to link remote sensing and in situ measurements of slow wind originating at open–closed magnetic field boundaries. The SOOP ran just prior to Solar Orbiter’s first close perihelion passage during two remote sensing windows (RSW1 and RSW2) between 2022 March 3–6 and 2022 March 17–22, while Solar Orbiter was at a heliocentric distance of 0.55–0.51 and 0.38–0.34 au from the Sun, respectively. Coordinated observation campaigns were also conducted by Hinode and IRIS. The magnetic connectivity tool was used, along with low latency in situ data, and full-disk remote sensing observations, to guide the target pointing of Solar Orbiter. Solar Orbiter targeted an active region complex during RSW1, the boundary of a coronal hole, and the periphery of a decayed active region during RSW2. Post-observation analysis using the magnetic connectivity tool along with in situ measurements from MAG and SWA/PAS, show that slow solar wind, with velocities between ∼210 and 600 km s−1, arrived at the spacecraft originating from two out of three of the target regions. The Slow Wind SOOP, despite presenting many challenges, was very successful, providing a blueprint for planning future observation campaigns that rely on the magnetic connectivity of Solar Orbiter. Copyright © 2023, The Authors. All rights reserved.

关键词： Solar wind