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Erratum to: Search for exclusive Higgs and Z boson decays to ϕγ and ργ with the ATLAS detector

Journal of high Energy Physics 2023年第12期2023卷 1-21页

作者： Aaboud, M. Aad, G. Abbott, B. Abdinov, O. Abeloos, B. Abidi, S. H. AbouZeid, O. S. Abraham, N. L. Abramowicz, H. Abreu, H. Abreu, R. Abulaiti, Y. Acharya, B. S. Adachi, S. Adamczyk, L. Adelman, J. Adersberger, M. Adye, T. Affolder, A. A. Afik, Y. Agatonovic-Jovin, T. Agheorghiesei, C. Aguilar-Saavedra, J. A. Ahlen, S. P. Ahmadov, F. Aielli, G. Akatsuka, S. Akerstedt, H. Åkesson, T. P. A. Akilli, E. Akimov, A. V. Alberghi, G. L. Albert, J. Albicocco, P. Alconada Verzini, M. J. Alderweireldt, S. C. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexander, G. Alexopoulos, T. Alhroob, M. Ali, B. Aliev, M. Alimonti, G. Alison, J. Alkire, S. P. Allbrooke, B. M. M. Allen, B. W. Allport, P. P. Aloisio, A. Alonso, A. Alonso, F. Alpigiani, C. Alshehri, A. A. Alstaty, M. I. Alvarez Gonzalez, B. Álvarez Piqueras, D. Alviggi, M. G. Amadio, B. T. Amaral Coutinho, Y. Amelung, C. Amidei, D. Amor Dos Santos, S. P. Amoroso, S. Amundsen, G. Anastopoulos, C. Ancu, L. S. Andari, N. Andeen, T. Anders, C. F. Anders, J. K. Anderson, K. J. Andreazza, A. Andrei, V. Angelidakis, S. Angelozzi, I. Angerami, A. Anisenkov, A. V. Anjos, N. Annovi, A. Antel, C. Antonelli, M. Antonov, A. Antrim, D. J. Anulli, F. Aoki, M. Aperio Bella, L. Arabidze, G. Arai, Y. Araque, J. P. Araujo Ferraz, V. Arce, A. T. H. Ardell, R. E. Arduh, F. A. Arguin, J-F. Argyropoulos, S. Arik, M. Armbruster, A. J. Armitage, L. J. Arnaez, O. Arnold, H. Arratia, M. Arslan, O. Artamonov, A. Artoni, G. Artz, S. Asai, S. Asbah, N. Ashkenazi, A. Asquith, L. Assamagan, K. Astalos, R. Atkinson, M. Atlay, N. B. Augsten, K. Avolio, G. Axen, B. Ayoub, M. K. Azuelos, G. Baas, A. E. Baca, M. J. Bachacou, H. Bachas, K. Backes, M. Bagnaia, P. Bahmani, M. Bahrasemani, H. Baines, J. T. Bajic, M. Baker, O. K. Bakker, P. J. Baldin, E. M. Balek, P. Balli, F. Balunas, W. K. Banas, E. Bandyopadhyay, A. Banerjee, Sw. Bannoura, A. A. E. Barak, L. Barberio, E. L. Barberis, D. Barbero, M. Barillari, T. Barisits, M.-S. Barkeloo, J. T. Barklow, T. Barlow, N. Barnes, S. L. Barnett, B. M. Barnett, R. M. Barno Faculté des Sciences Université Mohamed Premier and LPTPM Oujda Morocco CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman United States of America Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan LAL Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France Department of Physics University of Toronto Toronto Canada Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz United States of America Department of Physics and Astronomy University of Sussex Brighton United Kingdom Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel Center for High Energy Physics University of Oregon Eugene United States of America Department of Physics Stockholm University Stockholm Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London United Kingdom International Center for Elementary Particle Physics and Department of Physics The University of Tokyo Tokyo Japan AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics Northern Illinois University DeKalb United States of America Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Institute of Physics University of Belgrade Belgrade Serbia Department of Physics Alexandru Ioan Cuza University of Iasi Iasi Romania Laboratório de Instrumentação e Física Experimental de Partículas – LIP Lisboa Portugal Departamento de Fisica Teorica y del Cosmos Universidad de Granada Granada Portugal Department of Physics Boston University Boston United States of America Joi

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computing Image Intersection and Union Regions for Drosophila Neurons Based on Multi-core CPUs 16th

Computing Image Intersection and Union Regions for Drosophil...

引用

16th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2019

作者： Guo, Ming-Yan Cheng, Hui-Jun Lin, Chun-Yuan Lin, Yen-Jen Chiang, Ann-Shyn Department of Computer Science and Information Engineering Chang Gung University Taoyuan33302 Taiwan AI Innovation Research Center Chang Gung University Taoyuan33302 Taiwan Division of Rheumatology Allergy and Immunology Chang Gung Memorial Hospital Taoyuan33302 Taiwan National Center for High-Performance Computing Hsinchu30076 Taiwan Brain Research Center National Tsing Hua University Hsinchu30013 Taiwan Institute of Biotechnology and Department of Life Science National Tsing Hua University Hsinchu30013 Taiwan Kavli Institute for Brain and Mind University of California at San Diego La Jolla San DiegoCA United States Department of Biomedical Science and Environmental Biology Kaohsiung Medical University Kaohsiung80708 Taiwan Genomics Research Center Academia Sinica NankangTaipei11529 Taiwan

ISBN: (纸本)9783030301422

With more and more Drosophila Driver and Neuron images, it is an important work to find the similarity relationships among them as the functional inference. There is a general problem that how to find a Drosophila Driver image, which can cover a set of Drosophila Neuron images. In order to solve this problem, the intersection/union region for a set of Drosophila Neuron images should be computed at first, then a comparison work is used to calculate the similarities between the region and Drosophila Driver image(s). In this paper, three encoding schemes, namely Integer, Boolean, Decimal, are proposed to encode each Drosophila Driver and Neuron image as a one-dimensional structure, respectively. Then, the intersection/union region from these images can be computed by using the various operations, such as Boolean operators and lookup-table search method. Finally, the comparison work is done as the union region computation, and the similarity score can be calculated by the definition of Tanimoto coefficient. The above methods for the region computation are also implemented in the multi-core CPUs environment with the OpenMP. From the experimental results, in the encoding phase, the performance by Boolean scheme is the best than that by others;in the region computation phase, the performance by Decimal is the best when the number of images is large. The speedup ratio can achieve 13 based on 16 CPUs. © 2019, Springer Nature Switzerland AG.

关键词： Neurons

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Blockchain and smart contract for digital certificate

Blockchain and smart contract for digital certificate

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International Conference on Applied System Innovation (ICASI)

作者： Jiin-Chiou Cheng Narn-Yih Lee Chien Chi Yi-Hua Chen Department of Computer Science and Information Engineering Southern Taiwan University of Science and Technology Tainan Taiwan National Applied Research Laboratories Natinal Center for High-Performance Computing

According to the Taiwan Ministry of Education statistics, about one million graduates each year, some of them will go to countries, high schools or tertiary institutions to continue to attend, and some will be ready to enter the workplace employment. During the course of study, the students' all kinds of excellent performance certificates, score transcripts, diplomas, etc., will become an important reference for admitting new schools or new works. As schools make various awards or diplomas, only the names of the schools and the students are input. Due to the lack of effective anti-forge mechanism, events that cause the graduation certificate to be forged often get noticed. In order to solve the problem of counterfeiting certificates, the digital certificate system based on blockchain technology would be proposed. By the unmodifiable property of blockchain, the digital certificate with anti-counterfeit and verifiability could be made. The procedure of issuing the digital certificate in this system is as follows. First, generate the electronic file of a paper certificate accompanying other related data into the database, meanwhile calculate the electronic file for its hash value. Finally, store the hash value into the block in the chain system. The system will create a related QR-code and inquiry string code to affix to the paper certificate. It will provide the demand unit to verify the authenticity of the paper certificate through mobile phone scanning or website inquiries. Through the unmodifiable properties of the blockchain, the system not only enhances the credibility of various paper-based certificates, but also electronically reduces the loss risks of various types of certificates.

关键词： Contracts Companies Conferences Technological innovation Distributed databases Certification

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performance evaluation of container-based high performance computing ecosystem using OpenPOWER 32nd

Performance evaluation of container-based high performance c...

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32nd International Conference on high performance computing, ISC high performance 2017

作者： Kuity, Animesh Peddoju, Sateesh Kumar High Performance Computing Lab Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India

ISBN: (纸本)9783319676296

Container-based high performance computing (HPC) has started gaining popularity due to its almost negligible performance penalty compared to the BareMetal hardware. Although HPC hardware architectures and programming models are continuously evolving, the platform models are suffering from the HPC community’s awareness. Power-awareness, hardware and application-aware co-design along with security related concerns have attracted the most in recent time to empower the platform models. Otherside, the OpenPOWER ecosystems have stepped into our life to fulfill the thirst of exploiting the last drop of a performance benefit from our invested system. It has Power8 compliant processor with a larger cache, big fat instructions and data path accompanied by the popular coherent accelerator. In this paper, we have proposed a container-based HPC ecosystem established using OpenPOWER machine. The performance of the designed and developed ecosystem is evaluated stressing on different subcomponents of the system such as processor, memory, IO, and interconnect. Finally, the results are compared with the performance of the equivalent environments made of virtual machines and BareMetal hardware. © Springer International Publishing AG 2017.

关键词： Ecosystems

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Experimental Verification and Analysis of Dynamic Loop Scheduling in Scientific Applications

Experimental Verification and Analysis of Dynamic Loop Sched...

引用

International Symposium on Parallel and Distributed computing

作者： Ali Mohammed Ahmed Eleliemy Florina M. Ciorba Franziska Kasielke Ioana Banicescu Department of Mathematics and Computer Science University of Basel Switzerland Center for Information Services and High Performance Computing Technische Universität Dresden Germany Department of Computer Science and Engineering Mississippi State University USA

Scientific applications are often irregular and characterized by large computationally-intensive parallel loops. Dynamic loop scheduling (DLS) techniques improve the performance of computationally-intensive scientific applications via load balancing of their execution on high-performance computing (HPC) systems. Identifying the most suitable choices of data distribution strategies, system sizes, and DLS techniques which improve the performance of a given application, requires intensive assessment and a large number of exploratory native experiments (using real applications on real systems), which may not always be feasible or practical due to associated time and costs. In such cases, simulative experiments are more appropriate for studying the performance of applications. This motivates the question of 'How realistic are the simulations of executions of scientific applications using DLS on HPC platforms?' In the present work, a methodology is devised to answer this question. It involves the experimental verification and analysis of the performance of DLS in scientific applications. The proposed methodology is employed for a computer vision application executing using four DLS techniques on two different HPC platforms, both via native and simulative experiments. The evaluation and analysis of the native and simulative results indicate that the accuracy of the simulative experiments is strongly influenced by the approach used to extract the computational effort of the application (FLOP-or time-based), the choice of application model representation into simulation (data or task parallel) and the available HPC subsystem models in the simulator (multi-core CPUs, memory hierarchy and network topology). The minimum and the maximum percent errors achieved between the native and the simulative experiments are 0.95% and 8.03%, respectively.

关键词： Dynamic scheduling Analytical models Task analysis Computational modeling Power capacitors Processor scheduling

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Experimental verification and analysis of dynamic loop scheduling in scientific applications

arXiv

引用

arXiv 2018年

作者： Mohammed, Ali Eleliemy, Ahmed Ciorba, Florina M. Kasielke, Franziska Banicescu, Ioana Department of Mathematics and Computer Science University of Basel Switzerland Center for Information Services and High Performance Computing Technische Universität Dresden Germany Department of Computer Science and Engineering Mississippi State University United States

Scientific applications are often irregular and characterized by large computationally-intensive parallel loops. Dynamic loop scheduling (DLS) techniques can be used to improve the performance of computationally-intensive scientific applications via load balancing of their execution on high-performance computing (HPC) systems. Identifying the most suitable choices of data distribution strategies, system sizes, and DLS techniques which improve the performance of a given application, requires intensive assessment and a large number of exploratory native experiments (using real applications on real systems), which may not always be feasible or practical due to associated time and costs. In such cases, to avoid the execution of a large volume of exploratory native experiments, simulative experiments which are faster and less costly are more appropriate for studying the performance of applications for the purpose of optimizing it. This motivates the question of ‘How realistic are the simulations of executions of scientific applications using DLS on HPC platforms?’ In the present work, a methodology is devised to answer this question It involves the experimental verification and analysis of the performance of DLS in scientific applications. The proposed methodology is employed for a computer vision application executing using four DLS techniques on two different HPC platforms, both via native and simulative experiments. Moreover, the evaluation and the analysis of the native and simulative results indicate that the accuracy of the simulative experiments is strongly influenced by the values obtained by the chosen approach used to extract the computational effort of the application (FLOP- or time-based), the choice of application model representation into simulation (data or task parallel), and the choice of HPC subsystem models available in the simulator (multi-core CPUs, memory hierarchy, and network topology). Further insights into the native performance on two HPC platf

关键词： Scheduling

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From FAIR to CURE: Guidelines for Computational Models of Biological Systems

arXiv

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arXiv 2025年

作者： Sauro, Herbert M. Agmon, Eran Blinov, Michael L. Gennari, John H. Hellerstein, Joe Heydarabadipour, Adel Hunter, Peter Jardine, Bartholomew E. May, Elebeoba Nickerson, David P. Smith, Lucian P. Bader, Gary D. Bergmann, Frank Boyle, Patrick M. Dräger, Andreas Faeder, James R. Feng, Song Freire, Juliana Fröhlich, Fabian Glazier, James A. Gorochowski, Thomas E. Helikar, Tomas Hoops, Stefan Imoukhuede, Princess Keating, Sarah M. Konig, Matthias Laubenbacher, Reinhard Loew, Leslie M. Lopez, Carlos F. Lytton, William W. McCulloch, Andrew Mendes, Pedro Myers, Chris J. Myers, Jerry G. Mulugeta, Lealem Niarakis, Anna van Niekerk, David D. Olivier, Brett G. Patrie, Alexander A. Quardokus, Ellen M. Radde, Nicole Rohwer, Johann M. Sahle, Sven Schaff, James C. Sego, T.J. Shin, Janis Snoep, Jacky L. Vadigepalli, Rajanikanth Wiley, H. Steve Waltemath, Dagmar Moraru, Ion Department of Bioengineering University of Washington SeattleWA98195-5061 United States Intelligent Systems Engineering and Biocomplexity Institute Indiana University Street BloomingtonIN47408 United States Center for Cardiovascular Biology University of Washington SeattleWA98195-5061 United States eScience Institute University of Washington SeattleWA98195-5061 United States Institute for Stem Cell and Regenerative Medicine University of Washington SeattleWA98195-5061 United States School of Biological Sciences University of Bristol 24 Tyndall Avenue BristolBS8 1TQ United Kingdom Departments of Physiology & Pharmacology Neurology Downstate Health Science University BrooklynNY11203 United States Department of Neurology Kings County Hospital BrooklynNY11203 United States NASA-John H. Glenn Research Center MS 110-3 21000 Brookpark Road ClevelandOH44135 United States Center for Cell Analysis and Modeling UConn Health 263 Farmington Avenue FarmingtonCT06030-6406 United States Amsterdam Institute for Life and Environment Vrije Universiteit Amsterdam De Boelelaan 1108 Amsterdam1081 HZ Netherlands Department of Pathology and Genomic Medicine Thomas Jefferson University 1020 Locust St PhiladelphiaPA19107 United States Biological Sciences Division Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Auckland Bioengineering Institute University of Auckland Auckland1010 New Zealand Center of Integrative Biology University of Toulouse III-Paul Sabatier 165 Rue Marianne Grunberg-Manago Toulouse31400 France Lifeware Group Inria Building Alan Turing 1 Rue Honoré d’Estienne d’Orves Palaiseau91120 France Multiscale Modeling Group Altos Labs Redwood CityCA94065 United States partner site Tübingen Tübingen Germany Ottfried-Müller-Str. 37 Tübingen72076 Germany 06120 Germany Department of Computer Science Center for Data Science New York University New YorkNY11201 United States Department of Bi

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility;the clarity of model descriptions and annotations for understandability;adherence to standards and open science practices for reproducibility;and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible. © 2025, CC BY.

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Noise subtraction from KAGRA O3GK data using Independent Component Analysis

arXiv

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arXiv 2022年

作者： Abe, H. Akutsu, T. Ando, M. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Bajpai, R. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, D. Chen, K. Chen, Y. Chiang, C.-Y. Chu, Y.-K. Eguchi, S. Eisenmann, M. Enomoto, Y. Flaminio, R. Fong, H.K. Fujii, Y. Fujikawa, Y. Fujimoto, Y. Fukunaga, I. Gao, D. Ge, G.-G. Ha, S. Hadiputrawan, I.P.W. Haino, S. Han, W.-B. Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hirata, N. Hirose, C. Ho, T.-C. Hsieh, B.-H. Hsieh, H.-F. Hsiung, C. Huang, H.-Y. Huang, P. Huang, Y.-C. Huang, Y.-J. Hui, D.C.Y. Ide, S. Inayoshi, K. Inoue, Y. Ito, K. Itoh, Y. Jeon, C. Jin, H.-B. Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanda, N. Kato, T. Kawaguchi, K. Kim, C. Kim, J. Kim, J.C. Kim, Y.-M. Kimura, N. Kiyota, T. Kobayashi, Y. Kohri, K. Kokeyama, K. Kong, A.K.H. Koyama, N. Kozakai, C. Kume, J. Kuromiya, Y. Kuroyanagi, S. Kwak, K. Lee, E. Lee, H.W. Lee, R. Leonardi, M. Li, K.L. Li, P. Lin, L.C.-C. Lin, C.-Y. Lin, E.T. Lin, F.-K. Lin, F.-L. Lin, H.L. Liu, G.C. Luo, L.-W. Ma'arif, M. Majorana, E. Michimura, Y. Mio, N. Miyakawa, O. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Morisue, N. Moriwaki, Y. Nagano, K. Nakamura, K. Nakano, H. Nakano, M. Nakayama, Y. Narikawa, T. Naticchioni, L. Nguyen Quynh, L. Ni, W.-T. Nishimoto, T. Nishizawa, A. Nozaki, S. Obayashi, Y. Ogaki, W. Oh, J.J. Oh, K. Ohashi, M. Ohashi, T. Ohkawa, M. Ohta, H. Okutani, Y. Oohara, K. Oshino, S. Otabe, S. Pan, K.-C. Parisi, A. Park, J. Peña Arellano, F.E. Saha, S. Saito, Y. Sakai, K. Sawada, T. Sekiguchi, Y. Shao, L. Shikano, Y. Shimizu, H. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Song, I. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takano, S. Takeda, H. Takeda, M. Tanaka, K. Tanaka, T. Tanaka, T. Tanioka, S. Taruya, A. Graduate School of Science Tokyo Institute of Technology Meguro-ku Tokyo152-8551 Japan Mitaka City Tokyo181-8588 Japan Mitaka City Tokyo181-8588 Japan Department of Physics The University of Tokyo Bunkyo-ku Tokyo113-0033 Japan The University of Tokyo Bunkyo-ku Tokyo113-0033 Japan Earthquake Research Institute The University of Tokyo Bunkyo-ku Tokyo113-0032 Japan Department of Mathematics and Physics Gravitational Wave Science Project Hirosaki University Aomori Hirosaki City036-8561 Japan Kamioka-cho Hida City Gifu506-1205 Japan Mitaka City Tokyo181-8588 Japan Yuseong-gu Daejeon34141 Korea Republic of National Institute for Mathematical Sciences Yuseong-gu Daejeon34047 Korea Republic of Ibaraki Tsukuba City305-0801 Japan Department of Astronomy Beijing Normal University Beijing100875 China Department of Applied Physics Fukuoka University Jonan Fukuoka Fukuoka City814-0180 Japan Department of Physics Tamkang University Danshui Dist. New Taipei City25137 Taiwan Department of Physics Institute of Astronomy National Tsing Hua University Hsinchu30013 Taiwan Department of Physics Center for High Energy and High Field Physics National Central University Zhongli District Taoyuan City32001 Taiwan Department of Physics National Tsing Hua University Hsinchu30013 Taiwan Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Université Savoie Mont Blanc CNRS IN2P3 AnnecyF-74941 France Department of Astronomy The University of Tokyo Mitaka City Tokyo181-8588 Japan Faculty of Engineering Niigata University Nishi-ku Niigata Niigata City950-2181 Japan Department of Physics Graduate School of Science Osaka City University Sumiyoshi-ku Osaka City Osaka558-8585 Japan Chinese Academy of Sciences Xiao Hong Shan Wuhan430071 China Ulju-gun Ulsan44919 Korea Republic of Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China KAGRA Observatory The University of Tok

In April 2020, KAGRA conducted its first science observation in combination with the GEO 600 detector (O3GK) for two weeks. According to the noise budget estimation, suspension control noise in the low frequency band and acoustic noise in the middle frequency band are identified as the dominant contribution. In this study, we show that such noise can be reduced in offline data analysis by utilizing a method called Independent Component Analysis (ICA). Here the ICA model is extended from the one studied in iKAGRA data analysis by incorporating frequency dependence while linearity and stationarity of the couplings are still assumed. By using optimal witness sensors, those two dominant contributions are mitigated in the real observational data. We also analyze the stability of the transfer functions for whole two weeks data in order to investigate how the current subtraction method can be practically used in gravitational wave search. Copyright © 2022, The Authors. All rights reserved.

关键词： Data handling

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General-Purpose Quantum Circuit Simulator with Projected Entangled-Pair States and the Quantum Supremacy Frontier

引用

Physical Review Letters 2019年第19期123卷 190501-190501页

作者： Chu Guo Yong Liu Min Xiong Shichuan Xue Xiang Fu Anqi Huang Xiaogang Qiang Ping Xu Junhua Liu Shenggen Zheng He-Liang Huang Mingtang Deng Dario Poletti Wan-Su Bao Junjie Wu Henan Key Laboratory of Quantum Information and Cryptography IEU Zhengzhou 450001 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 China Information Systems Technology and Design Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Quantum Intelligence Lab (QI-Lab) Supremacy Future Technologies (SFT) Guangzhou 511340 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen 518055 China Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230026 China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui 230026 China Science and Math Cluster and EPD Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore

Recent advances on quantum computing hardware have pushed quantum computing to the verge of quantum supremacy. Here, we bring together many-body quantum physics and quantum computing by using a method for strongly interacting two-dimensional systems, the projected entangled-pair states, to realize an effective general-purpose simulator of quantum algorithms. The classical computing complexity of this simulator is directly related to the entanglement generation of the underlying quantum circuit rather than the number of qubits or gate operations. We apply our method to study random quantum circuits, which allows us to quantify precisely the memory usage and the time requirements of random quantum circuits. We demonstrate our method by computing one amplitude for a 7×7 lattice of qubits with depth (1+40+1) on the Tianhe-2 supercomputer.

关键词： Quantum benchmarking Quantum computation Quantum simulation Computational complexity Tensor network methods

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Machine learning and high-throughput robust design of P3HT-CNT composite thin films for high electrical conductivity

arXiv

引用

arXiv 2020年

作者： Bash, Daniil Yongqiang, Cai Chellappan, Vijila Liang, Wong Swee Yang, Xu Kumar, Pawan Da, Tan Jin Abutaha, Anas Cheng, Jayce Fun, Lim Yee Tian, Siyu Ren, Danny Zekun Mekki-Barrada, Flore Wong, Wai Kuan Kumar, Jatin Khan, Saif Qianxiao, Li Buonassisi, Tonio Hippalgaonkar, Kedar Institute of Materials Research & Engineering Agency for Science Technology and Research 2 Fusionopolis Way 138634 Singapore Department of Chemistry National University of Singapore 3 Science Drive 3 117543 Singapore Department of Mathematics National University of Singapore 21 Lower Kent Ridge Rd 119077 Singapore School of Mathematical Sciences Laboratory of Mathematics and Complex Systems MOE Beijing Normal University Beijing100875 China University Hall Tan Chin Tuan Wing Level 04 #04-02 21 Lower Kent Ridge Road 119077 Singapore Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Qatar Foundation Doha34110 Qatar Singapore-MIT Alliance for Research and Technology SMART 138602 Singapore Department of Chemical and Biomolecular Engineering National University of Singapore 117585 Singapore Institute of High Performance Computing 1 Fusionopolis Way #16-16 Connexis138632 Singapore Massachusetts Institute of Technology CambridgeMA02139 United States Department of Materials Science and Engineering Nanyang Technological University Singapore639798 Singapore

Combining high-throughput experiments with machine learning allows quick optimization of parameter spaces towards achieving target properties. In this study, we demonstrate that machine learning, combined with multi-labeled datasets, can additionally be used for scientific understanding and hypothesis testing. We introduce an automated flow system with high-throughput drop-casting for thin film preparation, followed by fast characterization of optical and electrical properties, with the capability to complete one cycle of learning of fully labeled ~160 samples in a single day. We combine regio-regular poly-3-hexylthiophene with various carbon nanotubes to achieve electrical conductivities as high as 1200 S/cm. Interestingly, a non-intuitive local optimum emerges when 10% of double-walled carbon nanotubes are added with long single wall carbon nanotubes, where the conductivity is seen to be as high as 700 S/cm, which we subsequently explain with high fidelity optical characterization. Employing dataset resampling strategies and graph-based regressions allows us to account for experimental cost and uncertainty estimation of correlated multi-outputs, and supports the proving of the hypothesis linking charge delocalization to electrical conductivity. We therefore present a robust machine-learning driven high-throughput experimental scheme that can be applied to optimize and understand properties of composites, or hybrid organic-inorganic materials. © 2020, CC-BY.

关键词： Thin films

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