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arXiv 2022年

作者： Alam, M. Sohaib Belomestnykh, Sergey Bornman, Nicholas Cancelo, Gustavo Chao, Yu-Chiu Checchin, Mattia Dinh, Vinh San Grassellino, Anna Gustafson, Erik J. Harnik, Roni McRae, Corey Rae Harrington Huang, Ziwen Kapoor, Keshav Kim, Taeyoon Kowalkowski, James B. Kramer, Matthew J. Krasnikova, Yulia Kumar, Prem Kurkcuoglu, Doga Murat Lamm, Henry Lyon, Adam L. Milathianaki, Despina Murthy, Akshay Mutus, Josh Nekrashevich, Ivan Oh, JinSu Özgüler, A. Barış Perdue, Gabriel Nathan Reagor, Matthew Romanenko, Alexander Sauls, James A. Stefanazzi, Leandro Tubman, Norm M. Venturelli, Davide Wang, Changqing You, Xinyuan van Zanten, David M.T. Zhou, Lin Zhu, Shaojiang Zorzetti, Silvia NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Scientific Computing Division Fermi National Accelerator Laboratory BataviaIL60510 United States Northwestern-Fermilab Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL60208 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Theory Division Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO80305 United States National Institute of Standards and Technology Boulder CO80305 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Ames Laboratory U.S. Department of Energy AmesIA50011 United States Center for Photonic Communication and Computing ECE Department Northwestern University EvanstonIL60208 United States Rigetti Computing Inc. BerkeleyCA94710 United States NASA Ames Research Center Moffett FieldCA94035 United States

Quantum information science harnesses the principles of quantum mechanics to realize computational algorithms with complexities vastly intractable by current computer platforms. Typical applications range from quantum chemistry to optimization problems and also include simulations for high energy physics. The recent maturing of quantum hardware has triggered preliminary explorations by several institutions (including Fermilab) of quantum hardware capable of demonstrating quantum advantage in multiple domains, from quantum computing to communications, to sensing. The Superconducting Quantum Materials and Systems (SQMS) Center, led by Fermilab, is dedicated to providing breakthroughs in quantum computing and sensing, mediating quantum engineering and HEP based material science. The main goal of the Center is to deploy quantum systems with superior performance tailored to the algorithms used in high energy physics. In this Snowmass paper, we discuss the two most promising superconducting quantum architectures for HEP algorithms, i.e. three-level systems (qutrits) supported by transmon devices coupled to planar devices and multi-level systems (qudits with arbitrary N energy levels) supported by superconducting 3D cavities. For each architecture, we demonstrate exemplary HEP algorithms and identify the current challenges, ongoing work and future opportunities. Furthermore, we discuss the prospects and complexities of interconnecting the different architectures and individual computational nodes. Finally, we review several different strategies of error protection and correction and discuss their potential to improve the performance of the two architectures. This whitepaper seeks to reach out to the HEP community and drive progress in both HEP research and QIS hardware. Copyright © 2022, The Authors. All rights reserved.

关键词： High energy physics

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Analysis of Negative Capacitance MOSFETs Characteristic with Spacer

Analysis of Negative Capacitance MOSFETs Characteristic with...

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International Symposium on VLSI Technology, Systems and Applications

作者： Yu-Chun Lee Sekhar Reddy Kola Chieh-Yang Chen Min-Hui Chuang Yiming Li Parallel and Scientific Computing Laboratory Institute of Biomedical Engineering EECS International Graduate Program Institute of Communications Engineering Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan Center for mmWave Smart Radar System and Technologies National Chiao Tung University 1001 Ta-Hsueh Rd Hsinchu Taiwan

ISBN: (数字)9781728142326

ISBN: (纸本)9781728142333

In this work, our study comprises of design and investigation on negative capacitance (NC), metal-oxide-semiconductor (MOS) field effects transistors (MOSFETs) with spacer and source/drain (S/D) overlap engineering. The scope of the work is to boost the performance and high-energy efficiency of the studied NC-MOSFETs by using the ferro electric material (FE). The NC-MOSFETs with the spacer technology can achieve the admirable I on /I off ratio and subthreshold swing (SS), compared with planar MOSFETs. It makes device scaling possible by eliminating the short channel effect (SCE). We further estimated the effect of FE thickness and spacer, which are another critical parameter of obtaining better electrical characteristics and reducing SS.

关键词： Capacitance Iron Semiconductor device modeling MOSFET Integrated circuit modeling Mathematical model Ferroelectric materials

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OnsagerNet: Learning stable and interpretable dynamics using a generalized Onsager principle

arXiv

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arXiv 2020年

作者： Yu, Haijun Tian, Xinyuan Weinan, E. Li, Qianxiao NCMIS LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics The Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mathematics National University of Singapore Singapore119077 Singapore Institute of High Performance Computing A*STAR Singapore138632 Singapore

We propose a systematic method for learning stable and physically interpretable dynamical models using sampled trajectory data from physical processes based on a generalized Onsager principle. The learned dynamics are autonomous ordinary differential equations parameterized by neural networks that retain clear physical structure information, such as free energy, diffusion, conservative motion and external forces. For high dimensional problems with a low dimensional slow manifold, an autoencoder with metric preserving regularization is introduced to find the low dimensional generalized coordinates on which we learn the generalized Onsager dynamics. Our method exhibits clear advantages over existing methods on benchmark problems for learning ordinary differential equations. We further apply this method to study Rayleigh-Bénard convection and learn Lorenz-like low dimensional autonomous reduced order models that capture both qualitative and quantitative properties of the underlying dynamics. This forms a general approach to building reduced order models for forced dissipative *** Codes 76E30, 34D20, 68T05/07, 82C35 Copyright © 2020, The Authors. All rights reserved.

关键词： Ordinary differential equations

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Faster indicators of dengue fever case counts using Google and Twitter

arXiv

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arXiv 2021年

作者： Mizzi, Giovanni Preis, Tobias Bastos, Leonardo Soares da Costa Gomes, Marcelo Ferreira Codeço, Claudia Torres Moat, Helen Susannah Centre for Complexity Science University of Warwick Gibbet Hill Road CoventryCV4 7AL United Kingdom Data Science Lab Behavioural Science Warwick Business School University of Warwick Scarman Road CoventryCV4 7AL United Kingdom The Alan Turing Institute British Library 96 Euston Road LondonNW1 2DB United Kingdom Scientific Computing Program Oswaldo Cruz Foundation Av. Brazil 4365 - Manguinhos Rio de Janeiro Brazil

Dengue is a major threat to public health in Brazil, the world's sixth biggest country by population, with over 1.5 million cases recorded in 2019 alone. Official data on dengue case counts is delivered incrementally and, for many reasons, often subject to delays of weeks. In contrast, data on dengue-related Google searches and Twitter messages is available in full with no delay. Here, we describe a model which uses online data to deliver improved weekly estimates of dengue incidence in Rio de Janeiro. We address a key shortcoming of previous online data disease surveillance models by explicitly accounting for the incremental delivery of case count data, to ensure that our approach can be used in practice. We also draw on data from Google Trends and Twitter in tandem, and demonstrate that this leads to slightly better estimates than a model using only one of these data streams alone. Our results provide evidence that online data can be used to improve both the accuracy and precision of rapid estimates of disease incidence, even where the underlying case count data is subject to long and varied delays. © 2021, CC BY.

关键词： Social networking (online)

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On Cyclic Finite-State Approximation of Data-Driven Systems

arXiv

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arXiv 2019年

作者： Vides, Fredy Scientific Computing Innovation Center Department of Applied Mathematics Universidad Nacional Autónoma de Honduras UNAH Honduras Graduate Program in Structural Engineering Universidad Tecnológica Centroamericana UNITEC Honduras

In this document, some novel theoretical and computational techniques for constrained approximation of data-driven systems, are presented. The motivation for the development of these techniques came from structure-preserving matrix approximation problems that appear in the fields of system identification and model predictive control, for data-driven systems and processes. The research reported in this document is focused on finite-state approximation of data-driven systems. Some numerical implementations of the aforementioned techniques in the simulation and model predictive control of some generic data-driven systems, that are related to electrical signal transmission models, are outlined.93B28, 47N70 (primary) and 93C57, 93B40 (secondary) Copyright © 2019, The Authors. All rights reserved.

关键词： Singular value decomposition

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High-Performance Metal-Ferroeletric-Semiconductor Nanosheet Line Tunneling Field Effect Transistors with Strained SiGe

High-Performance Metal-Ferroeletric-Semiconductor Nanosheet ...

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International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)

作者： Narasimhulu Thoti Yiming Li Sekhar Reddy Kola Seiji Samukawa Parallel and Scientific Computing Laboratory 1001 Ta-Hsueh Rd. Hsinchu Taiwan EECS International Graduate Program 1001 Ta-Hsueh Rd. Hsinchu Taiwan Institute of Communications Engineering 1001 Ta-Hsueh Rd. Hsinchu Taiwan National Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu Taiwan Institute of Fluid Science Tohoku University 1001 Ta-Hsueh Rd. Sendai Japan

ISBN: (数字)9784863487635

ISBN: (纸本)9781728173542

Nanosheet line tunnel-field effect transistors (NLTFETs) are for the first time proposed by utilizing the advantages of ferroelectricity through HZO materials. Three ferroelectric line TFETs have been proposed and investigated. Among these, the metal-ferroelectric-semiconductor (MFS) structure has shown superior performance than the other two variants. The factors of electric field and electron barrier tunneling have been addressed to govern the performance of these structures. In addition, the effects of the ferroelectric (Hf 0.5 Zr 0.5 O 2 ) thickness (t FE ) and the dielectric constant have been discussed. The MFS NLTFETs can effectively utilize the advantages of ferroelectric than the other variants. High on-current of 175.6 μA/μm and low off-current of 38.4 aA/μm are achieved at t FE of 4 nm through proper utilization of gate-overlap on to the drain side. Furthermore, the proposed MFS structure successfully delivers low average and minimum subthreshold swings even at very thin t FE .

关键词： Performance evaluation TFETs Tunneling Nanoscale devices Semiconductor process modeling Electric fields Silicon germanium

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Correction to: Examining parent-of-origin effects on transcription and RNA methylation in mediating aggressive behavior in honey bees (Apis mellifera)

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BMC genomics 2024年第1期25卷 1229页

作者： Sean T Bresnahan Ellen Lee Lindsay Clark Rong Ma Michael Markey Juliana Rangel Christina M Grozinger Hongmei Li-Byarlay Huck Institutes of the Life Sciences Pennsylvania State University University Park USA. stb5321@psu.edu. Agricultural Research and Development Program Central State University Wilberforce USA. HPCBio University of Illinois at Urbana-Champaign Champaign USA. Research Scientific Computing Group Seattle Children's Research Institute Seattle USA. Huck Institutes of the Life Sciences Pennsylvania State University University Park USA. Department of Biochemistry and Molecular Biology Wright State University Dayton USA. Department of Entomology Texas A&M University College Station USA. Agricultural Research and Development Program Central State University Wilberforce USA. hli-byarlay@centralstate.edu. Department of Agricultural and Life Science Central State University Wilberforce USA. hli-byarlay@centralstate.edu.

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Predicting temperatures in Brazilian states capitals via Machine Learning

arXiv

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arXiv 2025年

作者： da Silva, Sidney T. Gabrick, Enrique C. de Moraes, Ana Luiza R. Viana, Ricardo L. Batista, Antonio M. Caldas, Iberê L. Kurths, Jürgen Federal University of Paraná PR Curitiba81531-980 Brazil Institute of Physics University of São Paulo SP São Paulo05508-090 Brazil Federal University of Paraná Department of Physics Interdisciplinary Center for Science Technology and Innovation Center for Modeling and Scientific Computing PR Curitiba81531-980 Brazil Graduate Program in Science State University of Ponta Grossa PR Ponta Grossa84030-900 Brazil Department of Mathematics and Statistics State University of Ponta Grossa PR Ponta Grossa84030-900 Brazil Potsdam Institute for Climate Impact Research Telegrafenberg A31 Potsdam14473 Germany Department of Physics Humboldt University Berlin Newtonstraße 15 Berlin12489 Germany

Climate change refers to substantial long-term variations in weather patterns. In this work, we employ a Machine Learning (ML) technique, the Random Forest (RF) algorithm, to forecast the monthly average temperature for Brazilian’s states capitals (27 cities) and the whole country, from January 1961 until December 2022. To forecast the temperature at k-month, we consider as features in RF: i) global emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) at k-month;ii) temperatures from the previous three months, i.e., (k − 1), (k − 2) and (k − 3)-month;iii) combination of i and ii. By investigating breakpoints in the times series, we discover that 24 cities and the gases present breakpoints in the 80’s and 90’s. After the breakpoints, we find an increase in the temperature and the gas emission. Thereafter, we separate the cities according to their geographical position and employ the RF algorithm to forecast the temperature from 2010-08 until 2022-12. Based on i, ii, and iii, we find that the three inputs result in a very precise forecast, with a normalized root mean squared error (NMRSE) less than 0.083 for the considered cases. From our simulations, the better forecasted region is Northeast through iii (NMRSE = 0.012). Furthermore, we also investigate the forecasting of anomalous temperature data by removing the annual component of each time series. In this case, the best forecasting is obtained with strategy i, with the best region being Northeast (NRMSE = 0.090). © 2025, CC BY.

关键词： Global warming

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Distributional property testing in a quantum world

arXiv

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arXiv 2019年

作者： Gilyén, András Liy, Tongyang QuSoft CWI University of Amsterdam Netherlands Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland U.S. Department of Energy Office of Science Office of Advanced Scientific Computing Research Quantum Algorithms Teams Program

A fundamental problem in statistics and learning theory is to test properties of distributions. We show that quantum computers can solve such problems with significant speed-ups. In particular, we give fast quantum algorithms for testing closeness between unknown distributions, testing independence between two distributions, and estimating the Shannon / von Neumann entropy of distributions. The distributions can be either classical or quantum, however our quantum algorithms require coherent quantum access to a process preparing the samples. Our results build on the recent technique of quantum singular value transformation, combined with more standard tricks such as divide-and-conquer. The presented approach is a natural fit for distributional property testing both in the classical and the quantum case, demonstrating the first speed-ups for testing properties of density operators that can be accessed coherently rather than only via sampling;for classical distributions our algorithms significantly improve the precision dependence of some earlier results. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum theory

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Quantum-inspired classical sublinear-time algorithm for solving low-rank semidefinite programming via sampling approaches

arXiv

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arXiv 2019年

作者： Chia, Nai-Hui Li, Tongyang Lin, Han-Hsuan Wang, Chunhao Department of Computer Science University of Texas Austin United States Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland U.S. Department of Energy Office of Science Office of Advanced Scientific Computing Research Quantum Algorithms Teams program

Semidefinite programming (SDP) is a central topic in mathematical optimization with extensive studies on its efficient solvers. Recently, quantum algorithms with superpolynomial speedups for solving SDPs have been proposed assuming access to its constraint matrices in quantum superposition. Mutually inspired by both classical and quantum SDP solvers, in this paper we present a sublinear classical algorithm for solving low-rank SDPs which is asymptotically as good as existing quantum algorithms. Specifically, given an SDP with m constraint matrices, each of dimension n and rank poly(log n), our algorithm gives a succinct description and any entry of the solution matrix in time O(m · poly(log n, 1/Ε)) given access to a sample-based low-overhead data structure of the constraint matrices, where Ε is the precision of the solution. In addition, we apply our algorithm to a quantum state learning task as an application. Technically, our approach aligns with both the SDP solvers based on the matrix multiplicative weight (MMW) framework and the recent studies of quantum-inspired machine learning algorithms. The cost of solving SDPs by MMW mainly comes from the exponentiation of Hermitian matrices, and we propose two new technical ingredients (compared to previous sample-based algorithms) for this task that may be of independent interest: • Weighted sampling: assuming sampling access to each individual constraint matrix A1,..., Aτ, we propose a procedure that gives a good approximation of A = A1 + ··· + Aτ. • Symmetric approximation: we propose a sampling procedure that gives low-rank spectral decomposition of a Hermitian matrix A. This improves upon previous sampling procedures that only give low-rank singular value decompositions, losing the signs of eigenvalues. Copyright © 2019, The Authors. All rights reserved.

关键词： Singular value decomposition

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