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arXiv 2021年

作者： Laucht, Arne Hohls, Frank Ubbelohde, Niels Fernando Gonzalez-Zalba, M. Reilly, David J. Stobbe, Søren Schröder, Tim Scarlino, Pasquale Koski, Jonne V. Dzurak, Andrew Yang, Chih-Hwan Yoneda, Jun Kuemmeth, Ferdinand Bluhm, Hendrik Pla, Jarryd Hill, Charles Salfi, Joe Oiwa, Akira Muhonen, Juha T. Verhagen, Ewold LaHaye, M.D. Kim, Hyun Ho Tsen, Adam W. Culcer, Dimitrie Geresdi, Attila Mol, Jan A. Mohan, Varun Jain, Prashant K. Baugh, Jonathan Centre for Quantum Computation and Communication Technology School of Electrical Engineering and Telecommunications UNSW SydneyNSW2052 Australia Physikalisch-Technische Bundesanstalt Braunschweig38116 Germany Quantum Motion Technologies Nexus Discovery Way LeedsLS2 3AA United Kingdom School of Physics University of Sydney SydneyNSW2006 Australia Microsoft Corporation Station Q Sydney University of Sydney SydneyNSW2006 Australia Department of Photonics Engineering DTU Fotonik Technical University of Denmark Building 343 Kgs. LyngbyDK-2800 Denmark Department of Physics Humboldt-Universität zu Berlin Berlin12489 Germany Department of Physics ETH Zürich ZürichCH-8093 Switzerland Niels Bohr Institute University of Copenhagen Copenhagen2100 Denmark JARA-FIT Institute for Quantum Information RWTH Aachen University and Forschungszentrum Jülich Aachen52074 Germany School of Physics University of Melbourne Melbourne Australia Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada The Institute of Scientific and Industrial Research Osaka University Ibaraki Osaka567-0047 Japan Center for Quantum Information and Quantum Biology Institute for open and Transdisciplinary Research Initiative Osaka University Osaka560-8531 Japan Center for Nanophotonics AMOLF Amsterdam1098 XG Netherlands School of Physics The University of New South Wales Sydney2052 Australia Australian Research Council Centre of Excellence in Future Low-Energy Electronics Technologies UNSW Node The University of New South Wales Sydney2052 Australia QuTech and Kavli Institute of Nanoscience Delft University of Technology Delft2600 GA Netherlands School of Physics and Astronomy Queen Mary University of London E1 4NS United Kingdom Department of Materials Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Chemistry University of Illinois at Urbana-Champaign Urbana

Quantum phenomena are typically observable at length and time scales smaller than those of our everyday experience, often involving individual particles or excitations. The past few decades have seen a revolution in the ability to structure matter at the nanoscale, and experiments at the single particle level have become commonplace. This has opened wide new avenues for exploring and harnessing quantum mechanical effects in condensed matter. These quantum phenomena, in turn, have the potential to revolutionize the way we communicate, compute and probe the nanoscale world. Here, we review developments in key areas of quantum research in light of the nanotechnologies that enable them, with a view to what the future holds. Materials and devices with nanoscale features are used for quantum metrology and sensing, as building blocks for quantum computing, and as sources and detectors for quantum communication. They enable explorations of quantum behaviour and unconventional states in nano- and opto-mechanical systems, low-dimensional systems, molecular devices, nano-plasmonics, quantum electrodynamics, scanning tunnelling microscopy, and more. This rapidly expanding intersection of nanotechnology and quantum science/technology is mutually beneficial to both fields, laying claim to some of the most exciting scientific leaps of the last decade, with more on the horizon. © 2021, CC BY.

关键词： Quantum computers

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Foundation models and intelligent decision-making: Progress, challenges, and perspectives

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Innovation 2025年第6期6卷 100948页

作者： Huang, Jincai Xu, Yongjun Wang, Qi Wang, Qi [Cheems] Liang, Xingxing Wang, Fei Zhang, Zhao Wei, Wei Zhang, Boxuan Huang, Libo Chang, Jingru Ma, Liantao Ma, Ting Liang, Yuxuan Zhang, Jie Guo, Jian Jiang, Xuhui Fan, Xinxin An, Zhulin Li, Tingting Li, Xuefei Shao, Zezhi Qian, Tangwen Sun, Tao Diao, Boyu Yang, Chuanguang Yu, Chenqing Wu, Yiqing Li, Mengxian Zhang, Haifeng Zeng, Yongcheng Zhang, Zhicheng Zhu, Zhengqiu Lv, Yiqin Li, Aming Chen, Xu An, Bo Xiao, Wei Bai, Chenguang Mao, Yuxing Yin, Zhigang Gui, Sheng Su, Wentao Zhu, Yinghao Gao, Junyi He, Xinyu Li, Yizhou Jin, Guangyin Ao, Xiang Zhai, Xuehao Tan, Haoran Yun, Lijun Shi, Hongquan Li, Jun Fan, Changjun Huang, Kuihua Harrison, Ewen Leung, Victor C.M. Qiu, Sihang Dong, Yanjie Zheng, Xiaolong Wang, Gang Zheng, Yu Wang, Yuanzhuo Guo, Jiafeng Wang, Lizhe Cheng, Xueqi Wang, Yaonan Yang, Shanlin Fu, Mengyin Fei, Aiguo Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Huazhong University of Science and Technology Wuhan430074 China School of Automation Beijing Institute of Technology Beijing100081 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing100081 China Guangzhou511453 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China IDEA Research International Digital Economy Academy Shenzhen518057 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Science National University of Defense Technology Changsha410073 China Center for Systems and Control College of Engineering Peking University Beijing100871 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China College of Systems Engineering National University of Defense Technology Changsha410073 China Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electrical Engineering Chongqing University Chongqing400044 China Department of Mathematics The University of Hong Kong Hong Kong SAR999077 China School of Food Science and Technology Dalian Polytechnic University Dalian116034 China Centre for Medical Informatics University of Edinburgh EdinburghEH16 4UX United Kingdom Department of Stomatology Peking Union Medical College Hospital Chinese Academ

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems. © 2025 The Authors

关键词： Reinforcement learning

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A review and discussion of decomposition-based hybrid models for wind energy forecasting applications

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Applied Energy 2019年 235卷 939-953页

作者： Zheng Qian Yan Pei Hamidreza Zareipour Niya Chen School of Instrumentation Science and Opto-electronics Engineering Beihang University Beijing China Key Laboratory of Precision Opto-Mechatronics Technology Ministry of Education Beihang University Beijing China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing 100192 China Department of Electrical and Computer Engineering University of Calgary Calgary Alberta Canada ABB Corporate Research Center Beijing China

With the continuous growth of wind power integration into the electrical grid, accurate wind power forecasting is an important component in management and operation of power systems. Given the challenging nature of wind power forecasting, various methods are presented in the literature to improve wind power forecasting accuracy. Among them, combining different techniques to construct hybrid models has been frequently reported in the literature. Decomposition-based models are a family of hybrid models that firstly decompose the wind speed/power time series into relatively more stationary subseries, and then build forecasting models for each subseries. In this paper, we present a comprehensive review of decomposition-based wind forecasting methods in order to explore their effectiveness. Decomposition-based hybrid forecasting models are classified into different groups based on the decomposition methods, such as, wavelet, empirical mode decomposition, seasonal adjust methods, variational mode decomposition, intrinsic time-scale decomposition, and bernaola galvan algorithm. We discuss decomposition methods in the context of alternative forecasting algorithms, and explore the challenges of each method. Comparative analysis of various decomposition-based models is also provided. We also explore current research activities and challenges, and identify potential directions for future research on this subject.

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Master stability functions for complete, intralayer, and interlayer synchronization in multiplex networks of coupled Rössler oscillators

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Physical Review E 2019年第1期99卷 012304-012304页

作者： Longkun Tang Xiaoqun Wu Jinhu Lü Jun-an Lu Raissa M. D'Souza Fujian Province University Key Laboratory of Computation Science School of Mathematical Science Huaqiao University Quanzhou 362021 China School of Mathematics and Statistics Wuhan University Wuhan 430072 China Department of Computer Science University of California Davis California 95616 USA School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

Synchronization phenomena are of broad interest across disciplines and increasingly of interest in a multiplex network setting. For the multiplex network of coupled Rössler oscillators, here we show how the master stability function, a celebrated framework for analyzing synchronization on a single network, can be extended to certain classes of multiplex networks with different intralayer and interlayer coupling functions. We derive three master stability equations that determine, respectively, the necessary regions of complete synchronization, intralayer synchronization, and interlayer synchronization. We calculate these three regions explicitly for the case of a two-layer network of Rössler oscillators and show that the overlap of the regions determines the type of synchronization achieved. In particular, if the interlayer or intralayer coupling function is such that the interlayer or intralayer synchronization region is empty, complete synchronization cannot be achieved regardless of the coupling strength. Furthermore, for any network structure, the occurrence of intralayer and interlayer synchronization depends mainly on the coupling functions of nodes within a layer and across layers, respectively. Our mathematical analysis requires that the intralayer and interlayer supra-Laplacians commute. But, we show this is only a sufficient, and not necessary, condition and that the results can be applied more generally.

关键词： Collective dynamics Synchronization Synchronization transition

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Simple security proofs for continuous variable quantum key distribution with intensity fluctuating sources

arXiv

引用

arXiv 2019年

作者： Li, Chenyang Qian, Li Lo, Hoi-Kwong Center for Quantum Information and Quantum Control Department of Electrical & Computer Engineering University of Toronto TorontoM5S 3G4 Canada Department of Physics The University of Hong Kong Hong Kong Department of Physics University of Toronto TorontoM5S 3G4 Canada

Despite tremendous theoretical and experimental progress in continuous variable (CV) quantum key distribution (QKD), the security has not been rigorously established for most current continuous variable quantum key distribution systems that have imperfections. Among these imperfections, intensity fluctuation is one of the principal problem affecting security. In this paper, we provide simple security proofs for continuous variable quantum key distribution systems with intensity fluctuating sources. Specifically, depending on device assumptions in the source, the imperfect systems are divided into two general cases for security proofs. In the most conservative case, we prove the security based on the tagging idea, which is a main technique for the security proof of discrete variable quantum key distribution. Our proofs are simple to implement without any hardware adjustment for current continuous variable quantum key distribution systems. Also, we show that our proofs are able to provide secure secret keys in the finite-size scenario. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum cryptography

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Almost Sure Attitude Consensus in Multispacecraft Systems With Stochastic Communication Links

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IFAC-PapersOnLine 2017年第1期50卷 9392-9397页

作者： Rezaee H. Abdollahi F. The Center of Excellence on Control and Robotics Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical and Computer Engineering Concordia University Montreal Quebec Canada

Leaderless attitude consensus problem over multispacecraft systems is studied. The base of convergence analysis of existing results devoted to attitude consensus control of multispacecraft systems is deterministic knowledge on networks communication links. The main contribution of the present study is to design a control strategy guaranteeing almost sure attitude consensus in a multispacecraft system when communication links stochastically fail over time. Moreover, it is assumed that there exist constraints on the spacecraft input torques. To achieve the mentioned objectives, we propose a magnitude-constrained consensus control law for the spacecraft; then, by invoking the generalized invariance principle and the super-martingales convergence theorem, almost sure convergence of quaternion-based consensus errors to zero is concluded. The results are verified via a simulation example. © 2017

关键词： Attitude consensus coordination generalized invariance principle spacecraft stochastic communication

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Permutation matrix representation quantum Monte Carlo

arXiv

引用

arXiv 2019年

作者： Gupta, Lalit Albash, Tameem Hen, Itay Department of Physics and Astronomy and Center for Quantum Information Science and Technology University of Southern California Los AngelesCA90089 United States Department of Electrical and Computer Engineering Center for Quantum Information and Control CQuIC University of New Mexico AlbuquerqueNM87131 United States Information Sciences Institute University of Southern California Marina del ReyCA90292 United States

We present a quantum Monte Carlo algorithm for the simulation of general quantum and classical many-body models within a single unifying framework. The algorithm builds on a power series expansion of the quantum partition function in its off-diagonal terms and is both parameter-free and Trotter error-free. In our approach, the quantum dimension consists of products of elements of a permutation group. As such, it allows for the study of a very wide variety of models on an equal footing. To demonstrate the utility of our technique, we use it to clarify the emergence of the sign problem in the simulations of non-stoquastic physical models. We also study the thermal properties of the transverse-field Ising model augmented with randomly chosen two-body transverse-field interactions. Copyright © 2019, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Social Energy:Mining Energy From the Society

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 466-482页

作者： Jun Jason Zhang David Wenzhong Gao Yingchen Zhang Xiao Wang Xiangyang Zhao Dongliang Duan Xiaoxiao Dai Jun Hao Fei-Yue Wang Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of Denver IEEE National Renewable Energy Laboratory Qingdao Academy of Intelligent Industries(QAII) QingdaoShandongChinaand SKL-MCCSCASIA School of Automation Science and Electrical Engineering Beihang University Department of Electrical and Computer Engineering College of Engineering and Applied ScienceUniversity of Wyoming State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences(SKL-MCCSCASIA) Research Center of Computational Experiments and Parallel Systems National University of Defense Technology

The inherent nature of energy,i.e.,physicality,sociality and informatization,implies the inevitable and intensive interaction between energy systems and social *** this perspective,we define "social energy" as a complex sociotechnical system of energy systems,social systems and the derived artificial virtual systems which characterize the intense intersystem and intra-system *** recent advancement in intelligent technology,including artificial intelligence and machine learning technologies,sensing and communication in Internet of Things technologies,and massive high performance computing and extreme-scale data analytics technologies,enables the possibility of substantial advancement in socio-technical system optimization,scheduling,control and *** this paper,we provide a discussion on the nature of energy,and then propose the concept and intention of social energy systems for electrical power.A general methodology of establishing and investigating social energy is proposed,which is based on the ACP approach,i.e., "artificial systems"(A), "computational experiments"(C) and "parallel execution"(P),and parallel system methodology.A case study on the University of Denver(DU) campus grid is provided and studied to demonstrate the social energy *** the concluding remarks,we discuss the technical pathway,in both social and nature sciences,to social energy,and our vision on its future.

关键词： ACP distribution locational marginal pricing intelligent operation in power systems parallel control parallel intelligence parallel management social energy socio-technical energy systems

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The status of KAGRA underground cryogenic gravitational wave telescope 15

The status of KAGRA underground cryogenic gravitational wave...

引用

15th International Conference on Topics in Astroparticle and Underground Physics, TAUP 2017

作者： Akutsu, T. Ando, M. Araya, A. Aritomi, N. Asada, H. Aso, Y. Atsuta, S. Awai, K. Barton, M.A. Cannon, K. Craig, K. Creus, W. Doi, K. Eda, K. Enomoto, Y. Flaminio, R. Fujii, Y. Fujimoto, M.-K. Furuhata, T. Haino, S. Hasegawa, K. Hashino, K. Hayama, K. Hirobayashi, S. Hirose, E. Hsieh, A.H. Inoue, Y. Ioka, K. Itoh, Y. Kaji, T. Kajita, T. Kakizaki, M. Kamiizumi, M. Kambara, S. Kanda, N. Kanemura, S. Kaneyama, M. Kang, G. Kasuya, J. Kataoka, Y. Kawai, N. Kawamura, S. Kim, C. Kim, H. Kim, J. Kim, Y. Kimura, N. Kinugawa, T. Kirii, S. Kitaoka, Y. Kojima, Y. Kokeyama, K. Komori, K. Kotake, K. Kumar, R. Lee, H. Lee, H. Liu, Y. Luca, N. Majorana, E. Mano, S. Marchio, M. Matsui, T. Matsushima, F. Michimura, Y. Miyakawa, O. Miyamoto, T. Miyamoto, A. Miyo, K. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Morozumi, T. Musha, M. Nagano, S. Nagano, K. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Nakao, K. Narikawa, T. Quynh, L.Nguyen Ni, W.-T. Ochi, T. Oh, J. Oh, S. Ohashi, M. Ohishi, N. Ohkawa, M. Okutomi, K. Oohara, K. Alleano, F.E.P. Pinto, I. Sago, N. Saijo, M. Saito, Y. Sakai, K. Sakai, Y. Sasaki, Y. Sasaki, M. Sato, S. Sato, T. Sekiguchi, Y. Seto, N. Shibata, M. Shimoda, T. Shinkai, H. Shoda, A. Somiya, K. Son, E. Suemasa, A. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takeda, H. Tanaka, H. Tanaka, K. Tanaka, T. Tatsumi, D. Tomaru, T. Tomura, T. Travasso, F. Tsubono, K. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueki, S. Ueno, K. Ushiba, T. Van Putten, M.H.P.M. Vocca, H. Wada, S. Wakamatsu, T. Yamada, T. Yamamoto, S. Yamamoto, T. Yamamoto, K. Yamamoto, A. Yokoyama, J. Yokozawa, T. Yoon, T.H. Yuzurihara, H. Zeidler, S. Zhu, Z.-H. National Astronomical Observatory of Japan Mitaka Tokyo181-8588 Japan Department of Physics University of Tokyo Bunkyo Tokyo113-0033 Japan Earthquake Research Institute University of Tokyo Bunkyo Tokyo113-0032 Japan Department of Advanced Physics Hirosaki University Aomori Hirosaki036-8561 Japan National Astronomical Observatory of Japan Hida Gifu506-1205 Japan Department of Physics Tokyo Institute of Technology Meguro Tokyo152-8551 Japan Institute for Cosmic Ray Research University of Tokyo Hida Gifu506-1205 Japan Research Center for the Early Universe University of Tokyo Bunkyo Tokyo113-0033 Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa277-8582 Japan Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Department of Physics University of Toyama Toyama Toyama930-8555 Japan Faculty of Engineering University of Toyama Toyama Toyama930-8555 Japan Yukawa Institute for Theoretical Physics Kyoto University Sakyo Kyoto606-8502 Japan Department of Physics Osaka City University Sumiyosi Osaka558-8585 Japan Department of Physics Osaka University Osaka Toyonaka560-0043 Japan Korea Institute of Science and Technology Information Yuseong Daejeon 34141 Korea Republic of Yuseong Daejeon34055 Korea Republic of National Institute for Mathematical Sciences Daejeon34047 Korea Republic of Department of Computer Simulation Inje University Gimhae Gyeongsangnam50834 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul151-742 Korea Republic of High Energy Accelerator Research Organization Ibaraki Tsukuba305-0801 Japan Department of Physical Science Hiroshima University Higashihiroshima Hiroshima903-0213 Japan Department of Applied Physics Fukuoka University Jonan Fukuoka814-0180 Japan Department of Advanced Materials Science University of Tokyo Chiba Kashiwa277-8582 Japan Istituto Nazionale di Fisica Nucleare Sapienza University Roma00185 Italy Department o

KAGRA is a 3-km interferometric gravitational wave telescope located in the Kamioka mine in Japan. It is the first km-class gravitational wave telescope constructed underground to reduce seismic noise, and the first km-class telescope to use cryogenic cooling of test masses to reduce thermal noise. The construction of the infrastructure to house the interferometer in the tunnel, and the initial phase operation of the interferometer with a simple 3-km Michelson configuration have been completed. The first cryogenic operation is expected in 2018, and the observing runs with a full interferometer are expected in 2020s. The basic interferometer configuration and the current status of KAGRA are described. © Published under licence by IOP Publishing Ltd.

关键词： Interferometers

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Cost Optimal control of Microgrids Having Solar Power and Energy Storage

Cost Optimal Control of Microgrids Having Solar Power and En...

引用

2018 IEEE/PES Transmission and Distribution Conference and Exposition, T and D 2018

作者： Gupta, Nikhil Francis, Griffin Ospina, Juan Newaz, Alvi Collins, Emmanuel G. Faruque, Omar Meeker, Rick Harper, Mario United States Center for Intelligent Systems Control and Robotics FAMU-FSU COE United States Department of Electrical and Computer Engineering FAMU-FSU COE United States Center for Advanced Power Systems FSU United States NHU Energy Inc. TallahasseeFL United States Department of Scientific Computing FSU United States

ISBN: (纸本)9781538655832

Solar power availability is intermittent and must be accompanied by an energy storage system (ESS) so that the effect of variability can be minimized. Hence, a strategy is needed to find the optimum combination of grid power, solar power, and power from the ESS so that the total cost of energy can be minimized. A solution to this problem is proposed here as Advanced Optimal Resource Allocation (AORA). This control scheme uses the prediction of Photovoltaic (PV) power availability, the real-time price of energy, and the levelized cost of energy for both PV and ESS to optimally combine the dispatch of power sources. Simulation results are presented for a 24 h prediction window and compared with a baseline power usage scheme that is based on a fixed charging and discharging schedule for the ESS. The comparison shows that AORA results in a substantial cost savings over the baseline scheme. © 2018 IEEE.

关键词： Forecasting

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