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arXiv 2020年

作者： Goswami, Swati Das, Asit K. Nandy, Subhas C. Department of Computer Science and Technology Indian Institute of Engineering Science and Technology Shibpur Howrah711103 India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata700108 India

A majority of real life networks are weighted and sparse. The present article aims at characterization of weighted networks based on sparsity, as a measure of inherent diversity, of different network parameters. It utilizes sparsity index defined on ordered degree sequence of simple networks and derives further properties of this index. The range of possible values of sparsity index of any connected network, with edge-count in specific intervals, is worked out analytically in terms of node-count;a pattern is uncovered in corresponding degree sequences to produce highest sparsities. Given the edge-weight frequency distribution of a network, we have formulated an expression of the sparsity index of edge-weights. Its properties are analyzed under different distributions of edge-weights. For example, the upper and lower bounds of sparsity index of edge-weights of a network, having all distinct edge-weights, is determined in terms of its node-count and edge density. The article highlights that this summary index with low computational cost, computed on different network parameters, is useful to reveal different structural and organizational aspects of networks for performing analysis. An application of this index has been demonstrated through overlapping community detection of networks. The results validated on artificial and real-world networks show its efficacy. Copyright © 2020, The Authors. All rights reserved.

关键词： Population dynamics

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Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

arXiv

引用

arXiv 2023年

作者： Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Manassa, Jason Pietryga, Jacob Qian, Yiwen Cho, Min Gee Rowell, Jonathan L. Zheng, Huihuo Robinson, Richard D. Gu, Junsi Kirilin, Alexey Rozeveld, Steve Ercius, Peter Fessler, Jeffrey A. Xu, Ting Scott, Mary Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann ArborMI United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Advanced Photon Source Facility Argonne National Laboratory LemontIL United States Department of Material Science and Engineering Northwestern University EvanstonIL United States Department of Materials Science and Engineering University of California at Berkeley BerkeleyCA United States Department of Chemistry and Chemical Biology Cornell University IthacaNY United States Argonne Leadership Computing Facility Argonne National Laboratory LemontIL United States Department of Material Science and Engineering Cornell University IthacaNY United States Kavli Institute at Cornell for Nanoscale Science Cornell University IthacaNY United States Dow Chemical Co. MidlandMI United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA United States Applied Physics Program University of Michigan Ann ArborMI United States

Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment completes. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one nanometer resolution in a Au-Fe3O4 metamaterial, Co3O4 - Mn3O4 core-shell nanocrystals, and ZnS-Cu0.64S0.36 nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX / EELS) signals. Now sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials. © 2023, CC BY.

关键词： Zinc sulfide

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Subspace variational quantum simulator

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Physical Review Research 2023年第2期5卷 023078-023078页

作者： Kentaro Heya Ken M. Nakanishi Kosuke Mitarai Zhiguang Yan Kun Zuo Yasunari Suzuki Takanori Sugiyama Shuhei Tamate Yutaka Tabuchi Keisuke Fujii Yasunobu Nakamura RIKEN Center for Quantum Computing (RQC) Wako Saitama 351-0198 Japan Institute for Physics of Intelligence The University of Tokyo Tokyo 113-0033 Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan Center for Quantum Information and Quantum Biology Osaka University Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino 180-8585 Japan Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Meguro-ku Tokyo 153-8904 Japan Department of Applied Physics Graduate School of Engineering The University of Tokyo Bunkyo-ku Tokyo 113-8656 Japan

Quantum simulation is one of the key applications of quantum computing, which accelerates research and development in the fields such as chemistry and material science. The recent development of noisy intermediate-scale quantum (NISQ) devices urges the exploration of applications without the necessity of quantum error correction. In this paper, we propose an efficient method to simulate quantum dynamics driven by a static Hamiltonian on NISQ devices, named subspace variational quantum simulator (SVQS). SVQS employs the subspace-search variational quantum eigensolver (SSVQE) [Phys. Rev. Res. 1, 033062 (2019)] to find a low-lying eigensubspace and extends it to simulate dynamics within the subspace with lower overhead compared to the existing schemes. We experimentally simulate the time-evolution operator in a low-lying eigensubspace of a hydrogen molecule. We also define the subspace process fidelity as a measure between two quantum processes in a subspace. The subspace time evolution mimicked by SVQS shows the subspace process fidelity of 0.896–0.989.

关键词： Quantum algorithms Quantum algorithms for chemical calculations Quantum computation Quantum control Quantum gates Quantum simulation Quantum tomography

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New metric improving Bayesian calibration of a multistage approach studying hadron and inclusive jet suppression

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Physical Review C 2024年第6期109卷 064903-064903页

作者： W. Fan G. Vujanovic S. A. Bass A. Angerami R. Arora S. Cao Y. Chen T. Dai L. Du R. Ehlers H. Elfner R. J. Fries C. Gale Y. He M. Heffernan U. Heinz B. V. Jacak P. M. Jacobs S. Jeon Y. Ji L. Kasper M. Kordell, II A. Kumar J. Latessa Y.-J. Lee R. Lemmon D. Liyanage A. Lopez M. Luzum A. Majumder S. Mak A. Mankolli C. Martin H. Mehryar T. Mengel J. Mulligan C. Nattrass J. Norman J.-F. Paquet C. Parker J. H. Putschke G. Roland B. Schenke L. Schwiebert A. Sengupta C. Shen C. Sirimanna D. Soeder R. A. Soltz I. Soudi M. Strickland Y. Tachibana J. Velkovska X.-N. Wang W. Zhao Department of Physics Duke University Durham North Carolina 27708 USA Department of Physics and Astronomy Department of Physics Lawrence Livermore National Laboratory Livermore California 94550 USA Research Computing Group University Technology Solutions The University of Texas at San Antonio San Antonio Texas 78249 USA Institute of Frontier and Interdisciplinary Science Shandong University Qingdao Shandong 266237 China Laboratory for Nuclear Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics McGill University Montréal QC H3A 2T8 Canada Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Physics Division Oak Ridge National Laboratory Oak Ridge Tennessee 37830 USA GSI Helmholtzzentrum für Schwerionenforschung 64291 Darmstadt Germany Institute for Theoretical Physics Goethe University 60438 Frankfurt am Main Germany Frankfurt Institute for Advanced Studies 60438 Frankfurt am Main Germany Cyclotron Institute Texas A&M University College Station Texas 77843 USA Department of Physics and Astronomy Texas A&M University College Station Texas 77843 USA Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou 510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China Department of Physics The Ohio State University Columbus Ohio 43210 USA Department of Physics University of California Berkeley California 94270 USA Nuclear Science Division Lawrence Berkeley National Laboratory Berkeley California 94270 USA Department of Statistical Science Duke University Durham North Carolina 27708 USA Department of Physics and Astronomy Vanderbilt University Nashville Tennessee 37235 USA Department of Computer Sci

We study parton energy-momentum exchange with the quark gluon plasma (QGP) within a multistage approach composed of in-medium Dokshitzer-Gribov-Lipatov-Altarelli-Parisi evolution at high virtuality, and (linearized) Boltzmann transport formalism at lower virtuality. This multistage simulation is then calibrated in comparison with high-pT charged hadrons, D mesons, and the inclusive jet nuclear modification factors, using Bayesian model-to-data comparison, to extract the virtuality-dependent transverse momentum broadening transport coefficient q̂. To facilitate this undertaking, we develop a quantitative metric for validating the Bayesian workflow, which is used to analyze the sensitivity of various model parameters to individual observables. The usefulness of this new metric in improving Bayesian model emulation is shown to be highly beneficial for future such analyses.

关键词： Jet quenching Jets & heavy flavor physics Quark-gluon plasma Relativistic heavy-ion collisions Bayesian methods

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Quantum linear algebra is all you need for Transformer architectures

arXiv

引用

arXiv 2024年

作者： Guo, Naixu Yu, Zhan Choi, Matthew Agrawal, Aman Nakaji, Kouhei Aspuru-Guzik, Alán Rebentrost, Patrick Centre for Quantum Technologies National University of Singapore 117543 Singapore Department of Computer Science University of Toronto TorontoONM5S 2E4 Canada Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Department of Mathematics National University of Singapore 119076 Singapore Department of Chemistry University of Toronto TorontoONM5G 1Z8 Canada 1-1-1 Umezono Ibaraki Tsukuba305-8568 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan Department of Materials Science & Engineering University of Toronto TorontoONM5S 3E4 Canada Department of Chemical Engineering & Applied Chemistry University of Toronto TorontoONM5S 3E5 Canada Canadian Institute for Advanced Research TorontoONM5G 1Z8 Canada School of Computing National University of Singapore 117417 Singapore

Generative machine learning methods such as large-language models are revolutionizing the creation of text and images. While these models are powerful they also harness a large amount of computational resources. The transformer is a key component in large language models that aims to generate a suitable completion of a given partial sequence. In this work, we investigate transformer architectures under the lens of fault-tolerant quantum computing. The input model is one where trained weight matrices are given as block encodings and we construct the query, key, and value matrices for the transformer. We show how to prepare a block encoding of the self-attention matrix, with a new subroutine for the row-wise application of the softmax function. In addition, we combine quantum subroutines to construct important building blocks in the transformer, the residual connection and layer normalization, and the feed-forward neural network. Our subroutines prepare an amplitude encoding of the transformer output, which can be measured to obtain a prediction. Based on common open-source large-language models, we provide insights into the behavior of important parameters determining the run time of the quantum algorithm. We discuss the potential and challenges for obtaining a quantum advantage. © 2024, CC BY.

关键词： Network architecture

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Optimal parameter configurations for sequential optimization of the variational quantum eigensolver

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Physical Review Research 2023年第4期5卷 043136-043136页

作者： Katsuhiro Endo Yuki Sato Rudy Raymond Kaito Wada Naoki Yamamoto Hiroshi C. Watanabe Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan Quantum Computing Center Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama 223-8522 Japan Toyota Central R & D Labs. Inc. Koraku Mori Building 10F 1-4-14 Koraku Bunkyo-ku Tokyo 112-0004 Japan IBM Quantum IBM Japan 19-21 Nihonbashi Hakozaki-cho Chuo-ku Tokyo 103-8510 Japan Department of Computer Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama 223-8522 Japan Department of Chemistry Graduate School of Science Kyushu University 744 Motooka Nishi-ku Fukuoka 819-0395 Japan

The variational quantum eigensolver (VQE) is a hybrid algorithm to find the minimum eigenvalue/vector of the given Hamiltonian by optimizing a parameterized quantum circuit (PQC) using a classical computer. Sequential optimization methods, which are often used in quantum circuit tensor networks, are popular for optimizing the parameterized gates of PQCs. In this paper, we focus on the case where the components to be optimized are single-qubit gates, in which the analytic optimization of a single-qubit gate is sequentially performed. The analytical solution is given by diagonalization of a matrix whose elements are computed from the expectation values of observables specified by a set of predetermined parameters, which we refer to as the parameter configurations. In this paper, we first show that the optimization accuracy significantly depends on the choice of parameter configurations owing to the statistical errors in the expectation values. We then identify a metric that quantifies the optimization accuracy of a parameter configuration for all possible statistical errors, named configuration overhead/cost or C-cost. We theoretically provide the lower bound of C-cost and show that, for the minimum size of parameter configurations, the lower bound is achieved if and only if the parameter configuration satisfies the so-called equiangular line condition. Finally, we provide numerical experiments demonstrating that the optimal parameter configuration exhibits the best result in several VQE problems. We hope that this general statistical methodology will enhance the efficacy of sequential optimization of PQCs for solving practical problems with near-term quantum devices.

关键词： Quantum algorithms & computation Quantum circuits Quantum computation Quantum information processing Quantum measurements Quantum parameter estimation

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TCGM: An Information-Theoretic Framework for Semi-supervised Multi-modality Learning 1

引用

16th European Conference on computer Vision, ECCV 2020

作者： Sun, Xinwei Xu, Yilun Cao, Peng Kong, Yuqing Hu, Lingjing Zhang, Shanghang Wang, Yizhou Microsoft Research-Asia Beijing China Center on Frontiers of Computing Studies Advanced Institute of Information Technology Department of Computer Science Peking University Beijing China Yanjing Medical College Capital Medical University Beijing China UC Berkeley Berkeley United States Deepwise AI Lab Beijing China

ISBN: (数字)9783030585808

ISBN: (纸本)9783030585792

Fusing data from multiple modalities provides more information to train machine learning systems. However, it is prohibitively expensive and time-consuming to label each modality with a large amount of data, which leads to a crucial problem of semi-supervised multi-modal learning. Existing methods suffer from either ineffective fusion across modalities or lack of theoretical guarantees under proper assumptions. In this paper, we propose a novel information-theoretic approach - namely, Total Correlation Gain Maximization (TCGM) – for semi-supervised multi-modal learning, which is endowed with promising properties: (i) it can utilize effectively the information across different modalities of unlabeled data points to facilitate training classifiers of each modality (ii) it has theoretical guarantee to identify Bayesian classifiers, i.e., the ground truth posteriors of all modalities. Specifically, by maximizing TC-induced loss (namely TC gain) over classifiers of all modalities, these classifiers can cooperatively discover the equivalent class of ground-truth classifiers;and identify the unique ones by leveraging limited percentage of labeled data. We apply our method to various tasks and achieve state-of-the-art results, including the news classification (Newsgroup dataset), emotion recognition (IEMOCAP and MOSI datasets), and disease prediction (Alzheimer’s Disease Neuroimaging Initiative dataset). © 2020, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Networked Integrated Sensing and Communications for 6G Wireless Systems

arXiv

引用

arXiv 2024年

作者： Li, Jiapeng Shao, Xiaodan Chen, Feng Wan, Shaohua Liu, Chang Wei, Zhiqiang Ng, Derrick Wing Kwan The College of Artificial Intelligence Southwest University Chongqing400715 China The Brain-Inspired Computing and Intelligent Control Key Laboratory Southwest University Chongqing400715 China The Key Laboratory of Luminescence Analysis and Molecular Sensing Southwest University Chongqing400715 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China The School of Mathematics and Statistics Xi'an Jiaotong University Xi'An710049 China The Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China The Department of Computer Science and Information Technology La Trobe University MelbourneVIC3150 Australia The School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia

Integrated sensing and communication (ISAC) is envisioned as a key pillar for enabling the upcoming sixth generation (6G) communication systems, requiring not only reliable communication functionalities but also highly accurate environmental sensing capabilities. In this paper, we design a novel networked ISAC framework to explore the collaboration among multiple users for environmental sensing. Specifically, multiple users can serve as powerful sensors, capturing back scattered signals from a target at various angles to facilitate reliable computational imaging. Centralized sensing approaches are extremely sensitive to the capability of the leader node because it requires the leader node to process the signals sent by all the users. To this end, we propose a two-step distributed cooperative sensing algorithm that allows low-dimensional intermediate estimate exchange among neighboring users, thus eliminating the reliance on the centralized leader node and improving the robustness of sensing. This way, multiple users can cooperatively sense a target by exploiting the block-wise environment sparsity and the interference cancellation technique. Furthermore, we analyze the mean square error of the proposed distributed algorithm as a networked sensing performance metric and propose a beamforming design for the proposed network ISAC scheme to maximize the networked sensing accuracy and communication performance subject to a transmit power constraint. Simulation results validate the effectiveness of the proposed algorithm compared with the state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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A 2-bit graphene encoder based on the plasmon-induced transparency effect and its sensing characteristics

引用

Results in Physics 2024年 59卷

作者： Zhu, Aijun Li, Zongteng Hou, Weigang Yang, Xinghua Cheng, Lei Hu, Cong Qiao, Fei Mahapatra, Rabi Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin 541004 China Shanxi Key Laboratory of Advanced Semiconductor Optoelectronic Devices and Integrated Systems Jincheng Shanxi 048000 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing 400000 China College of Science Beijing Forestry University Beijing 100083 China Jincheng Research Institute of Opto-mechatronics Industry Jincheng Shanxi 048000 China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips Guangxi Normal University Guilin 541004 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Department of Computer Science and Engineering Texas A&M University College Station 77843-3112 TX United States

A 2-bit encoder based on the plasmon-induced transparency (PIT) effect was proposed and investigated in the range from 2 to 7 THz. The Lorentz oscillation coupling model was utilized to confirm the simulation. By tuning the Fermi levels of cross1 and cross2, the 2-bit encoder was achieved. And the minimum MD was 94.73 %, the minimum ER was 12.77 dB, and the maximum IL was 0.32 dB. As a sensor, the structure had a sensitivity of up to 2.33 THz/RIU. In addition, due to the insensitivities to incidence angle and polarization angle, the encoder could be utilized to complex environments. Therefore, these results reveals that our work is significant in terahertz encoders, optical switches, sensors, slow light devices, and modulators. © 2024 The Author(s)

关键词： 2-bit encoder Metamaterial Plasmon-induced transparency (PIT) effect Plasmonic-graphene Terahertz (THz)

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Generalized Quantum Subspace Expansion

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Physical Review Letters 2022年第2期129卷 020502-020502页

作者： Nobuyuki Yoshioka Hideaki Hakoshima Yuichiro Matsuzaki Yuuki Tokunaga Yasunari Suzuki Suguru Endo Department of Applied Physics University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Theoretical Quantum Physics Laboratory RIKEN Cluster for Pioneering Research (CPR) Wako-shi Saitama 351-0198 Japan Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan Center for Quantum Information and Quantum Biology Osaka University 1-2 Machikaneyama Toyonaka 560-0043 Japan NEC-AIST Quantum Technology Cooperative Research Laboratory National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki 305-8568 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino 180-8585 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan

One of the major challenges for erroneous quantum computers is undoubtedly the control over the effect of noise. Considering the rapid growth of available quantum resources that are not fully fault tolerant, it is crucial to develop practical hardware-friendly quantum error mitigation (QEM) techniques to suppress unwanted errors. Here, we propose a novel generalized quantum subspace expansion method which can handle stochastic, coherent, and algorithmic errors in quantum computers. By fully exploiting the substantially extended subspace, we can efficiently mitigate the noise present in the spectra of a given Hamiltonian, without relying on any information of noise. The performance of our method is discussed under two highly practical setups: the quantum subspaces are mainly spanned by powers of the noisy state ρm and a set of error-boosted states, respectively. We numerically demonstrate in both situations that we can suppress errors by orders of magnitude, and show that our protocol inherits the advantages of previous error-agnostic QEM techniques as well as overcoming their drawbacks.

关键词： Quantum algorithms Quantum computation Quantum error correction Quantum information processing Variational approach

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