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arXiv 2024年

作者： Halimeh, Jad C. Hanada, Masanori Matsuura, Shunji Nori, Franco Rinaldi, Enrico Schäfer, Andreas Max Planck Institute of Quantum Optics Garching85748 Germany Ludwig Maximilian University of Munich Munich80333 Germany Munich80799 Germany School of Mathematical Sciences Queen Mary University of London Mile End Road LondonE1 4NS United Kingdom qBraid Co. Harper Court 5235 ChicagoIL60615 United States RIKEN Saitama Wako351-0198 Japan Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Physics University of Guelph ONN1G 1Y2 Canada Center for Mathematical Science and Advanced Technology Japan Agency for Marine-Earth Science and Technology Yokohama236-0001 Japan RIKEN Saitama Wako351-0198 Japan Theoretical Quantum Physics Laboratory Cluster of Pioneering Research RIKEN Wako Saitama351-0198 Japan Physics Department University of Michigan Ann ArborMI48109 United States Quantinuum K.K. Otemachi Financial City Grand Cube 3F 1-9-2 Otemachi Chiyoda-ku Tokyo Japan Institute of Theoretical Physics University of Regensburg Universitätsstrasse 31 RegensburgD-93053 Germany

We provide a universal framework for the quantum simulation of SU(N) Yang–Mills theories on fault-tolerant digital quantum computers adopting the orbifold lattice formulation. As warm-up examples, we also consider simple models, including scalar field theory and the Yang–Mills matrix model, to illustrate the universality of our formulation, which shows up in the fact that the truncated Hamiltonian can be expressed in the same simple form for any N, any dimension, and any lattice size, in stark contrast to the popular approach based on the Kogut–Susskind formulation. In all these cases, the truncated Hamiltonian can be programmed on a quantum computer using only standard tools well-established in the field of quantum computation. As a concrete application of this universal framework, we consider Hamiltonian time evolution by Suzuki–Trotter decomposition. This turns out to be a straightforward task due to the simplicity of the truncated Hamiltonian. We also provide a simple circuit structure that contains only CNOT and one-qubit gates, independent of the details of the theory investigated. © 2024, CC BY.

关键词： Quantum computers

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Author Correction: An aquatic-vision-inspired camera based on a monocentric lens and a silicon nanorod photodiode array

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Nature Electronics 2022年第7期5卷 478页

作者： Minsung Kim Changsoon Choi Mincheol Lee Kyoung Won Cho Hyojin Cho Dae-Hyeong Kim Gil Ju Lee Min Seok Kim Hyun Myung Kim Young Min Song Siyi Liu Nanshu Lu Moon Kee Choi Center for Nanoparticle Research Institute for Basic Science (IBS) Seoul Republic of Korea School of Chemical and Biological Engineering Institute of Chemical Processes Seoul National University Seoul Republic of Korea Department of Materials Science and Engineering Seoul National University Seoul Republic of Korea School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Republic of Korea Center for Mechanics of Solids Structures and Materials Department of Aerospace Engineering and Engineering Mechanics Department of Biomedical Engineering Texas Materials Institute University of Texas at Austin Austin TX USA School of Materials Science and Engineering Ulsan National Institute of Science and Technology (UNIST) Ulsan Republic of Korea

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High-fidelity and robust two-qubit gates for quantum-dot spin qubits in silicon

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Physical Review A 2019年第4期99卷 042310-042310页

作者： Chia-Hsien Huang Chih-Hwan Yang Chien-Chang Chen Andrew S. Dzurak Hsi-Sheng Goan Department of Physics and Center for Theoretical Physics National Taiwan University Taipei 10617 Taiwan Center for Quantum Science and Engineering National Taiwan University Taipei 10617 Taiwan Centre for Quantum Computation and Communication Technology School of Electrical Engineering and Telecommunications The University of New South Wales Sydney New South Wales 2052 Australia

A two-qubit controlled-not (cnot) gate, realized by a controlled-phase (C-phase) gate combined with single-qubit gates, has been experimentally implemented recently for quantum-dot spin qubits in isotopically enriched silicon, a promising solid-state system for practical quantum computation. In the experiments, the single-qubit gates have been demonstrated with fault-tolerant control fidelity, but the infidelity of the two-qubit C-phase gate is, primarily due to the electrical noise, still higher than the required error threshold for fault-tolerant quantum computation (FTQC). Here, by taking the realistic system parameters and the experimental constraints on the control pulses into account, we construct experimentally realizable high-fidelity cnot gates robust against electrical noise with the experimentally measured 1/f1.01 noise spectrum and also against the uncertainty in the interdot tunnel coupling amplitude. Our fine-tuned optimal cnot gate has about two orders of magnitude improvement in gate infidelity over the ideal C-phase gate constructed without considering any noise effect. Furthermore, within the same control framework, high-fidelity and robust single-qubit gates can also be constructed, paving the way for large-scale FTQC.

关键词： Quantum computation Quantum gates Quantum dots

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Observation of ultrastrong magnon-magnon coupling in YFeO3 using terahertz magnetospectroscopy

Observation of ultrastrong magnon-magnon coupling in YFeO3 u...

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CLEO: QELS_Fundamental Science, CLEO_QELS 2020

作者： Makihara, Takuma Timothy Noe, G. Li, Xinwei Hayashida, Kenji Peraca, Nicolas Marquez Tian, Kevin Ma, Xiaoxuan Jin, Zuanming Ren, Wei Ma, Guohong Cao, Shixun Katayama, Ikufumi Takeda, Jun Turchinovich, Dmitry Nojiri, Hiroyuki Bamba, Motoaki Kono, Junichiro Department of Physics and Astronomy Rice University HoustonTX United States Department of Electrical and Computer Engineering Rice University HoustonTX United States Department of Physics International Center of Quantum and Molecular Structures Materials Genome Institute Shanghai University Shanghai200444 China Department of Physics Graduate School of Engineering Science Yokohama National University Yokohama Japan Fakultät für Physik Universität Bielefeld Universitätsstr. 25 Bielefeld33615 Germany Institute for Materials Research Tohoku University Sendai Japan Department of Physics Kyoto University Sakyo-ku Kyoto606-8502 Japan PRESTO Japan Science and Technology Agency Saitama332-0012 Japan Department of Materials Science and NanoEngineering Rice University HoustonTX United States

We studied magnon-magnon ultrastrong coupling in YFeO3 using terahertz magnetospectroscopy in magnetic fields up to 30 T, which led to an extreme breakdown of the rotating-wave approximation where the counter-rotating... 详细信息

ISBN: (数字)9781943580767

ISBN: (纸本)9781943580767

关键词： Iron compounds

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Graph-time spectral analysis for atrial fibrillation

arXiv

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arXiv 2019年

作者： Sun, Miao Isufi, Elvin de Groot, Natasja M.S. Hendriks, Richard C. Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Department of Cardiology Erasmus University Medical Center Netherlands

Atrial fibrillation is a clinical arrhythmia with multifactorial mechanisms still unresolved. Time-frequency analysis of epicardial electrograms has been investigated to study atrial fibrillation. However, deeper understanding of atrial fibrillation can be achieved if the spatial dimension can be incorporated. Unfortunately, the physical models describing the spatial relations of atrial fibrillation signals are complex and non-linear;hence, the conventional signal processing techniques to study electrograms in the joint space, time, and frequency domain are less suitable. In this study, we wish to put forward a radically different approach to analyze atrial fibrillation with a higher-level model. This approach relies on graph signal processing to represent the spatial relations between epicardial electrograms and put forward a graph-time spectral analysis for atrial fibrillation. To capture the frequency content along both the time and graph domain, we proposed the joint graph and short-time Fourier transform. The latter allows us to analyze the spatial variability of the electrogram temporal frequencies. With this technique, we have found that the spatial variation of the atrial electrograms decreases during atrial fibrillation due to the reduction of the high temporal frequencies of the atrial waves. The proposed analysis further confirms that the ventricular activity is smoother over the atrial area compared with the atrial activity. Besides using the proposed graph-time analysis to conduct a first study on atrial fibrillation, we applied it to the cancellation of ventricular activity from atrial electrograms. Experimental results on simulated and real data further corroborate the findings in this atrial fibrillation study. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectrum analysis

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Reply to comment on "Thermal effects - an alternative mechanism for plasmon-assisted photocatalysis"

arXiv

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arXiv 2020年

作者： Dubi, Yonatan Un, Ieng Wai Sivan, Yonatan Department of Chemistry Ben-Gurion University Israel School of Electrical and Computer Engineering Ben-Gurion University of the Negev Israel Joan and Irwin Jacobs TIX Institute National Tsing Hua University Taiwan Ilse Katz Center for Nanoscale Science and Technology Ben-Gurion University Israel

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Location-Enabled IoT (LE-IoT): A survey of positioning techniques, error sources, and mitigation

arXiv

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arXiv 2020年

作者： Li, You Zhuang, Yuan Hu, Xin Gao, Zhouzheng Hu, Jia Chen, Long He, Zhe Pei, Ling Chen, Kejie Wang, Maosong Niu, Xiaoji Chen, Ruizhi Thompson, John Ghannouchi, Fadhel El-Sheimy, Naser Department of Geomatics Engineering University of Calgary State Key Laboratory of Surveying Mapping and Remote Sensing Wuhan University School of Electronic Engineering Beijing University of Posts and Telecommunications Department of Land Sciences China University of Geosciences Beijing China Department of Computer Science University of Exeter School of Data and Computer Science Sun Yat-sen University Shanghai Key Laboratory of Navigation and Location Based Services School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Earth and Space Sciences Southern University of Science and Technology College of Intelligence Science and Technology National University of Defense Technology NSS Research Center Wuhan University School of Engineering University of Edinburgh

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for massmarket localization applications. However, there are various error sources that have limited the localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review-localization data sources-localization algorithms-localization error sources and mitigation-localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors. Copyright © 2020, The Authors. All rights reserved.

关键词： Wide area networks

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The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

arXiv

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

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Erratum to "Predict sample's line positions of absorption peaks in terahertz band with the forced radiation intensity of molecular electric dipoles" [Opt. Commun. 458 (2020) 124848](S0030401819309782)(10.1016/***.2019.124848)

引用

Optics Communications 2020年 483卷

作者： Zhang, Zhongwei Zhu, Zhi Yuan, Minghui Minghui Li You, Guanjun Chen, Lin Zhu, Yiming Terahertz Technology Innovation Research Institute Shanghai Key Lab of Modern Optical System Terahertz Science Cooperative Innovation Center School of Optical-Electrical Computer Engineering University of Shanghai for Science and Technology No. 580 Jun Gong Road Shanghai200093 China Department of Optical Engineering School of Sciences Zhejiang A&F University Zhejiang ProvinceHangzhou311300 China

The publisher regrets that there is minor error in grant number. The updated acknowledgement is as below: Acknowledgments: This work is financially supported by theNatural Science Foundation of China [grant numbers61722111,11904231,61671302];Shanghai Pujiang Program, China [grant number17PJD028];the 111 Project [grant numberD18014];Shanghai Sailing Program [grant number19YF1434100];Zhejiang Agriculture and Forestry University Teaching Reform Project [grant number2013110150];Zhejiang Science and technology department Project [grant number2017C37015];the Shuguang Program [grant number18SG44]. The publisher would like to apologise for any inconvenience caused. © 2019 Elsevier B.V.

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Stabilizing deep tomographic reconstruction

arXiv

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arXiv 2020年

作者： Wu, Weiwen Hu, Dianlin Cong, Wenxiang Shan, Hongming Wang, Shaoyu Niu, Chuang Yan, Pingkun Yu, Hengyong Vardhanabhuti, Varut Wang, Ge Biomedical Imaging Center Center for Biotechnology and Interdisciplinary Studies Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY United States The Laboratory of Image Science and Technology Southeast University Nanjing China Institute of Science and Technology for Brain-inspired Intelligence Fudan University Shanghai China Department of Electrical & Computer Engineering University of Massachusetts Lowell LowellMA United States Department of Diagnostic Radiology Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong SAR Hong Kong

Tomographic image reconstruction with deep learning is an emerging field, but a recent landmark study reveals that several deep reconstruction networks are unstable for computed tomography (CT) and magnetic resonance imaging (MRI). Specifically, three kinds of instabilities were reported: (1) strong image artefacts from tiny perturbations, (2) small features missing in a deeply reconstructed image, and (3) decreased imaging performance with increased input data. On the other hand, compressed sensing (CS) inspired reconstruction methods do not suffer from these instabilities because of their built-in kernel awareness. For deep reconstruction to realize its full potential and become a mainstream approach for tomographic imaging, it is thus critically important to meet this challenge by stabilizing deep reconstruction networks. Here we propose an Analytic Compressed Iterative Deep (ACID) framework to address this challenge. ACID synergizes a deep reconstruction network trained on big data, kernel awareness from CS-inspired processing, and iterative refinement to minimize the data residual relative to real measurement. Our study demonstrates that the deep reconstruction using ACID is accurate and stable, and sheds light on the converging mechanism of the ACID iteration under a Bounded Relative Error Norm (BREN) condition. In particular, the study shows that ACID-based reconstruction is resilient against adversarial attacks, superior to classic sparsity-regularized reconstruction alone, and eliminates the three kinds of instabilities. We anticipate that this integrative data-driven approach will help promote development and translation of deep tomographic image reconstruction networks into clinical applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Compressed sensing

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