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Coherent H ∞ control for Markovian jump linear quantum systems

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IFAC-PapersOnLine 2020年第2期53卷 269-274页

作者： Yanan Liu Daoyi Dong Ian R. Petersen Qing Gao Steven X. Ding Hidehiro Yonezawa School of Engineering and Information Technology University of New South Wales Canberra ACT 2600 Australia Center for Quantum Computation and Communication Technology Australian Research Council Canberra ACT 2600 Australia Institute for Automatic Control and Complex Systems (AKS) University of Duisburg-Essen. 47057 Duisburg Germany Research School of Electrical Energy and Materials Engineering The Australian National University Canberra ACT 2601 Australia School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing and also with Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

The purpose of this paper is to design a coherent feedback controller for a Markovian jump linear quantum system suffering from a fault signal. The control objective is to bound the effect of the disturbance input on the output for the time-varying quantum system. We prove the relation between the H ∞ control problem, the dissipation properties, and the solutions of Riccati differential equations, by which the H ∞ controller of the Markovian jump linear quantum system is given by the solutions of Linear Matrix Inequalities (LMIs).

关键词： Coherent quantum feedback H∞ control fault-tolerant control Markovian jump linear quantum systems

Polygenic risk score and lung adenocarcinoma risk among never-smokers by EGFR mutation status-a brief report

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Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 2024年第4期20卷 521-530页

作者： Batel Blechter Chao Agnes Hsiung Xiaoyu Wang Haoyu Zhang Wei Jie Seow Jianxin Shi Nilanjan Chatterjee Hee Nam Kim Maria Pik Wong Yun-Chul Hong Jason Yy Wong Juncheng Dai H Dean Hosgood Zhaoming Wang I-Shou Chang Jiyeon Choi Jiucun Wang Minsun Song Wei Hu Wei Zheng Jin Hee Kim Baosen Zhou Demetrius Albanes Min-Ho Shin Lap Ping Chung She-Juan An Hong Zheng Yasushi Yatabe Xu-Chao Zhang Young Tae Kim Xiao-Ou Shu Young-Chul Kim Roel C H Vermeulen Bryan A Bassig Jiang Chang James Chung Man Ho Bu-Tian Ji Michiaki Kubo Yataro Daigo Yukihide Momozawa Yoichiro Kamatani Takayuki Honda Hideo Kunitoh Shun-Ichi Watanabe Yohei Miyagi Haruhiko Nakayama Shingo Matsumoto Masahiro Tsuboi Koichi Goto Zhihua Yin Atsushi Takahashi Akiteru Goto Yoshihiro Minamiya Kimihiro Shimizu Kazumi Tanaka Tangchun Wu Fusheng Wei Jian Su Yeul Hong Kim In-Jae Oh Victor Ho Fun Lee Wu-Chou Su Yuh-Min Chen Gee-Chen Chang Kuan-Yu Chen Ming-Shyan Huang Hsien-Chih Lin Adeline Seow Jae Yong Park Sun-Seog Kweon Chien-Jen Chen Yu-Tang Gao Chen Wu Biyun Qian Daru Lu Jianjun Liu Hyo-Sung Jeon Chin-Fu Hsiao Jae Sook Sung Ying-Huang Tsai Yoo Jin Jung Huan Guo Zhibin Hu Tzu-Yu Chen Laurie Burdett Meredith Yeager Amy Hutchinson Sonja I Berndt Wei Wu Junwen Wang Jin Eun Choi Kyong Hwa Park Sook Whan Sung Li Liu Chang Hyun Kang Chung-Hsing Chen Jun Xu Peng Guan Wen Tan Chih-Liang Wang Alan Dart Loon Sihoe Ying Chen Yi Young Choi Jun Suk Kim Ho-Il Yoon Qiuyin Cai In Kyu Park Ping Xu Qincheng He Chih-Yi Chen Junjie Wu Wei-Yen Lim Kun-Chieh Chen John K C Chan Jihua Li Hongyan Chen Chong-Jen Yu Li Jin Joseph F Fraumeni Jie Liu Maria Teresa Landi Taiki Yamaji Yang Yang Belynda Hicks Kathleen Wyatt Shengchao A Li Hongxia Ma Bao Song Zhehai Wang Sensen Cheng Xuelian Li Yangwu Ren Motoki Iwasaki Junjie Zhu Gening Jiang Ke Fei Guoping Wu Li-Hsin Chien Fang-Yu Tsai Jinming Yu Victoria L Stevens Pan-Chyr Yang Dongxin Lin Kexin Chen Yi-Long Wu Keitaro Matsuo Nathaniel Rothman Kouya Shiraishi Hongbing Shen Stephen J Chanock Takashi Kohno Qing Lan Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA. Institute of Population Health Sciences National Health Research Institutes Zhunan Taiwan. Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA Saw Swee Hock School of Public Health National University of Singapore and National University Health System Singapore Singapore Department of Medicine Yong Loo Lin School of Medicine National University of Singapore and National University Health System Singapore Singapore. Department of Oncology School of Medicine Johns Hopkins University Baltimore MD USA Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Baltimore MD USA. Department of Preventive Medicine Chonnam National University Medical School Gwangju Republic of Korea. Department of Pathology Queen Mary Hospital Hong Kong Hong Kong. Department of Preventive Medicine Seoul National University College of Medicine Seoul Republic of Korea. Epidemiology and Community Health Branch National Heart Lung and Blood Institute Bethesda MD USA. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Department of Epidemiology and Population Health Albert Einstein College of Medicine New York USA. Department of Computational Biology St. Jude Children's Research Hospital Memphis TN USA. National Institute of Cancer Research National Health Research Institutes Zhunan Taiwan. Ministry of Education Key Laboratory of Contemporary Anthropology School of Life Sciences Fudan University Shanghai China State Key Laboratory of Genetic Engineering School of Life Sciences Fudan University Shanghai China. Department of Statistics & Research Institute of Natural Sciences Sookmyung Women's University Seoul 04310 Korea. Division of Epidemiology Department of Medicine Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center Nashville TN USA. Department of Environmental Health

We assessed the association between a genome-wide polygenic risk score (PRS) developed for lung adenocarcinoma (LUAD) risk and mutation on the epidermal growth factor receptor (EGFR) gene in 998 East Asian never-smoking female LUAD cases (518 EGFR-positive; 480 EGFR-negative) and 4,544 never-smoking controls using case-case and multinomial regression analyses. We found that the PRS was more strongly associated with EGFR-positive LUAD compared to EGFR-negative LUAD, where the association between the fourth quartile of the PRS and EGFR-positive LUAD (OR=8.63, 95% CI:5.67, 13.14) was significantly higher than the association between the fourth quartile of the PRS with EGFR-negative LUAD (OR=3.50, 95% CI: 2.44, 5.00) (p-heterogeneity=3.66x10). Our findings suggest that germline genetic susceptibility may be differentially associated with LUAD in never-smoking female East Asian patients depending on the cancer's mutation status, which may have important public health and clinical implications.

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

Publisher Correction: Compact angle-resolved metasurface spectrometer

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Nature materials 2025年第4期24卷 650页

作者： Guiyi Cai Yanhao Li Yao Zhang Xiong Jiang Yimu Chen Geyang Qu Xudong Zhang Shumin Xiao Jiecai Han Shaohua Yu Yuri Kivshar Qinghai Song Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Harbin Institute of Technology Shenzhen People's Republic of China. Pengcheng Laboratory Shenzhen People's Republic of China. Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Harbin Institute of Technology Shenzhen People's Republic of China. shumin.xiao@***. Pengcheng Laboratory Shenzhen People's Republic of China. shumin.xiao@***. National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of Technology Harbin People's Republic of China. shumin.xiao@***. National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of Technology Harbin People's Republic of China. Pengcheng Laboratory Shenzhen People's Republic of China. yush@***. Nonlinear Physics Center Research School of Physics Australian National University Canberra Australian Capital Territory Australia. yuri.kivshar@anu.edu.au. Qingdao Innovation and Development Center Harbin Engineering University Qingdao People's Republic of China. yuri.kivshar@anu.edu.au. Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Harbin Institute of Technology Shenzhen People's Republic of China. qinghai.song@***. Pengcheng Laboratory Shenzhen People's Republic of China. qinghai.song@***. Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan People's Republic of China. qinghai.song@***.

On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds

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arXiv 2023年

作者： Wang, Cheng-Xiang You, Xiaohu Gao, Xiqi Zhu, Xiuming Li, Zixin Zhang, Chuan Wang, Haiming Huang, Yongming Chen, Yunfei Haas, Harald Thompson, John S. Larsson, Erik G. Di Renzo, Marco Tong, Wen Zhu, Peiying Shen, Xuemin Poor, H. Vincent Hanzo, Lajos The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211111 China The School of Information Science and Engineering The State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China The Pervasive Communication Research Center Purple Mountain Laboratories Nanjing211111 China The School of Engineering The University of Warwick CoventryCV4 7AL United Kingdom The LiFi Research and Development Center Department of Electronic and Electrical Engineering University of Strathclyde GlasgowG1 1XQ United Kingdom The Institute for Digital Communications School of Engineering University of Edinburgh EdinburghEH9 3JL United Kingdom Linköping University Linköping581 83 Sweden Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-sur-Yvette91192 France The Wireless Advanced System and Competency Centre HUAWEI Technologies Co. Ltd. OttawaONK2K 3J1 Canada HUAWEI Technologies Canada Co. Ltd. OttawaONK2K 3J1 Canada The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed. © 2023, CC BY.

关键词： Network architecture

Fault-tolerant coherent H∞ control for linear quantum systems

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arXiv 2020年

作者： Liu, Yanan Dong, Daoyi Petersen, Ian R. Gao, Qing Ding, Steven X. Yokoyama, Shota Yonezawa, Hidehiro School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia Center for Quantum Computation and Communication Technology Australian Research Council CanberraACT2600 Australia University of Duisburg-Essen Duisburg47057 Germany Research School of Electrical Energy and Materials Engineering Australian National University CanberraACT2601 Australia School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Robustness and reliability are two key requirements for developing practical quantum control systems. The purpose of this paper is to design a coherent feedback controller for a class of linear quantum systems suffering from Markovian jumping faults so that the closed-loop quantum system has both fault tolerance and H∞ disturbance attenuation performance. This paper first extends the physical realization conditions from the time-invariant case to the time-varying case for linear stochastic quantum systems. By relating the fault tolerant H∞ control problem to the dissipation properties and the solutions of Riccati differential equations, an H∞ controller for the quantum system is then designed by solving a set of linear matrix inequalities (LMIs). In particular, an algorithm is employed to introduce additional noises and to construct the corresponding input matrices to ensure the physical realizability of the quantum controller. For real applications of the developed fault-tolerant control strategy, we present a linear quantum system example from quantum optics, where the amplitude of the pumping field randomly jumps among different values. It is demonstrated that a quantum H∞ controller can be designed and implemented using some basic optical components to achieve the desired control goal. Copyright © 2020, The Authors. All rights reserved.

关键词： Controllers

On-demand generation of traveling cat states using a parametric oscillator

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Physical Review A 2019年第2期99卷 023838-023838页

作者： Hayato Goto Zhirong Lin Tsuyoshi Yamamoto Yasunobu Nakamura Frontier Research Laboratory Corporate Research & Development Center Toshiba Corporation 1 Komukai Toshiba-cho Saiwai-ku Kawasaki-shi 212-8582 Japan RIKEN Center for Emergent Matter Science (CEMS) Wako Saitama 351-0198 Japan System Platform Research Laboratories NEC Corporation Tsukuba Ibaraki 305-8501 Japan Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Meguro-ku Tokyo 153-8904 Japan

We theoretically propose a method for on-demand generation of traveling Schrödinger cat states, namely, quantum superpositions of distinct coherent states of traveling fields. This method is based on deterministic generation of intracavity cat states using a Kerr-nonlinear parametric oscillator (KPO) via quantum adiabatic evolution. We show that the cat states generated inside a KPO can be released into an output mode by dynamically controlling the parametric pump amplitude. We further show that the quality of the traveling cat states can be improved by using a shortcut-to-adiabaticity technique.

关键词： Nonlinear optics Optical parametric oscillators & amplifiers Quantum computation Quantum information processing Quantum optics Quantum state engineering Quantum states of light Superconducting quantum optics

Overcoming the disconnect between energy system and climate modelling

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arXiv 2022年

作者： Craig, Michael T. Wohland, Jan Stoop, Laurens P. Kies, Alexander Pickering, Bryn Bloomfield, Hannah C. Browell, Jethro De Felice, Matteo Dent, Chris J. Deroubaix, Adrien Frischmuth, Felix Gonzalez, Paula L.M. Grochowicz, Aleksander Gruber, Katharina Härtel, Philipp Kittel, Martin Kotzur, Leander Labuhn, Inga Lundquist, Julie K. Pflugradt, Noah van der Wiel, Karin Zeyringer, Marianne Brayshaw, David J. School for Environment and Sustainability University of Michigan United States Institute for Environmental Decisions ETH Zurich Switzerland Helmholtz-Zentrum Hereon Germany Information and Computing Science Copernicus Institute of Sustainable Development Utrecht University Netherlands TenneT TSO B.V. Arnhem Netherlands Royal Netherlands Meteorological Institute Netherlands Frankfurt Institute for Advanced Studies Germany Department of Electrical and Computer Engineering Aarhus University Denmark ETH Zürich Switzerland School of Geographical Sciences University of Bristol United Kingdom Department of Meteorology University of Reading United Kingdom University of Glasgow United Kingdom European Commission Joint Research Centre Netherlands School of Mathematics University of Edinburg United Kingdom The Alan Turing Institute United Kingdom Max Planck Institute Germany Fraunhofer Institute for Energy Economics and Energy System Technology Germany Department of Mathematics University of Oslo Norway University of Natural Resources and Life Sciences Austria German Institute for Economic Research Germany Institute of Energy and Climate Research Techno-economic Systems Analysis Germany University of Bremen Institute of Geography Germany Dept. of Atmospheric and Oceanic Science University of Colorado Boulder United States National Renewable Energy Laboratory United States Renewable and Sustainable Energy Institute United States Forschungszentrum Jülich Germany Department of Technology Systems University of Oslo Norway

Energy system models underpin decisions by energy system planners and operators. Energy system modelling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modelling has started to form that aims to better integrate energy system models with weather and climate models. Here, we evaluate the disconnects between the energy system and climate modelling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modelling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision making. © 2022, CC BY-NC-ND.

关键词： Decision making

swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor

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arXiv 2019年

作者： Li, Mingzhen Liu, Changxi Liao, Jianjin Zheng, Xuegui Yang, Hailong Sun, Rujun Xu, Jun Gan, Lin Yang, Guangwen Luan, Zhongzhi Qian, Depei School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment Beijing China National University of Singapore Singapore Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the existing deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning workloads. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient codes for deep learning workloads on Sunway. The experiment results show that the codes generated by swTVM achieves 1.79× on average compared to the state-of-the-art deep learning framework on Sunway, across six representative benchmarks. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in mind. We believe this work will encourage more people to embrace the power of deep learning and Sunway many-core processor. Copyright © 2019, The Authors. All rights reserved.

关键词： Program compilers