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A proof-of-majority consensus protocol for blockchain-enabled collaboration infrastructure of 5G network slice brokers 2

A proof-of-majority consensus protocol for blockchain-enable...

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2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure, BSCI 2020, Co-located with AsiaCCS 2020

作者： Lin, Wenmin Xu, Xiaolong Qi, Lianyong Zhang, Xuyun Dou, Wanchun Khosravi, Mohammad R. Institute of VR and Intelligent Systems Alibaba Business School Hangzhou Normal University Hangzhou China Nanjing University of Information and Technology Nanjing China Qufu Normal University Rizhao China Department of Computing Macquarie University Sydney Australia State Key Laboratory of Novel Software Department of Computer Science and Technology Nanjing University Nanjing China Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Department of Computer Engineering Persian Gulf University Bushehr Iran

ISBN: (纸本)9781450376105

With network slicing technology, 5G changes the classical mobile network business model from a network-operator-oriented business to a more open system with multiple stakeholders. In this context, a network slice broker plays the role of agency between network resource providers and vertical service providers, to lease and provide on-demand network slice services. In practice, a single network slice broker normally owns limited types of network slices. And a collaboration infrastructure could be built up to enable network slice sharing among multiple network slice brokers, so as to improve user experience, and enhance the resource utilization rate. However, a major challenge with such an infrastructure is how to support fair and secure slice trading process among multiple un-trusted network slice providers. Blockchain technology, on the other side, brings in high opportunities to implement a fair and secure trading system without a trusted third-party. In view of this observation, this paper proposes a design of blockchain-enabled collaboration infrastructure for network slice brokers, where a group of registered brokers can trade their network slices in a fair and secure manner. To enhance the accountability of slice trading records, transactions are verified and sorted by each broker node, which are further packed into blocks and appended to a shared ledger maintained by each member in the collaboration network. More specifically, we design a Proof-of-Majority (PoM) consensus protocol to sort slice trading transactions before packing transaction blocks of the shared ledger. Furthermore, an optimization for the basic PoM protocol is discussed, with dependency resolving strategy to improve system performance. Experiments are conducted to verify the feasibility of our proposal. © 2020 ACM.

关键词： 5G mobile communication systems

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Transition Metal-Vacancy Point Defects in Zinc Oxide as Deep-Level Spin Qubits

arXiv

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arXiv 2025年

作者： Zhang, Shimin Park, Taejoon Perez, Erik Li, Kejun Wang, Xingyi Wang, Yanyong Vega Bazantes, Jorge D. Zhang, Ruiqi Sun, Jianwei Fu, Kai-Mei C. Seo, Hosung Ping, Yuan Department of Materials Science and Engineering University of Wisconsin-Madison 53706 United States SKKU Advanced Institute of Nanotechnology Sungkyunkwan University Gyeonggi Suwon16419 Korea Republic of Department of Energy Systems Research Department of Physics Ajou University Gyeonggi Suwon16499 Korea Republic of Department of Physics University of California Santa CruzCA95064 United States Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States School of Science & Engineering Tulane University 6823 St Charles Ave New OrleansLA70118 United States Department of Physics University of Washington SeattleWA98195 United States Physical Sciences Division Pacific Northwest National Laboratory RichlandWA99352 United States Department of Quantum Information Engineering Sungkyunkwan University Gyeonggi Suwon16419 Korea Republic of Center for Quantum Information Korea Institute of Science and Technology Seoul02792 Korea Republic of Department of Physics University of Wisconsin-Madison 53706 United States Department of Chemistry University of Wisconsin-Madison 53706 United States

Wide band gap oxides are promising host materials for spin defect qubits, offering unique advantages such as a dilute nuclear spin environment. Zinc oxide (ZnO), in particular, can achieve exceptional high purity, which enables long spin coherence time. In this work, we theoretically search for deep-level point defects in ZnO with optimal physical properties for optically-addressable spin qubits. Using first-principles calculations, we predict the Molybdenum-vacancy complex defect (MoZnvO)2+ in ZnO to own promising spin and optical properties, including spin-triplet ground state, optical transition in the visible to near-infrared range with high quantum yield, allowed intersystem crossings with a sizable optically-detected magnetic resonance contrast, and long spin T2 and T∗2. Notably, we find the Huang-Rhys factor of the defect to be around 5, which is significantly smaller than the typical range of 10-30 for most known defects in ZnO. Furthermore, we compare the spin decoherence driven by the nuclear spin bath and paramagnetic impurity baths. We find that the paramagnetic impurities are very effective in causing spin decoherence even with very low concentrations, implying that they can likely dominate the spin decoherence in ZnO even after isotopic purification. Using the computed excited-state energies and kinetic rates as inputs, we predict the ODMR contrast and propose a new protocol for spin qubit initialization and readout, which could be generalized to other systems with forbidden axial intersystem crossings. © 2025, CC BY.

关键词： Zinc oxide

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Predicting extreme events from data using deep machine learning: when and where

arXiv

引用

arXiv 2022年

作者： Jiang, Junjie Huang, Zi-Gang Grebogi, Celso Lai, Ying-Cheng The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Research Center for Brain-inspired Intelligence Xi’an Jiaotong University The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Shaanxi Xi’an China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences King’s College University of Aberdeen AB24 3UE United Kingdom Department of Physics Arizona State University TempeAZ85287 United States

We develop a deep convolutional neural network (DCNN) based framework for model-free prediction of the occurrence of extreme events both in time ("when") and in space ("where") in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the 2D complex Ginzburg-Landau equation and empirical wind speed data of the North Atlantic ocean to demonstrate and validate our machine-learning based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme event on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world. © 2022, CC BY.

关键词： Forecasting

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Robust multimodal Federated learning for non-IID multimodal data with incompleteness

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Future Generation computer systems 2025年

作者： Songcan Yu Kaiming Zhu Feiyuan Liang Junbo Wang Krishna Kant Ling Yin Department of Computer Science and Technology Sichuan Police College Luzhou 646000 PR China Police Integration Computing Key Laboratory of Sichuan Province Chengdu 610206 PR China School of Intelligent Systems Engineering Sun Yat-Sen University Shenzhen 518107 PR China Department of Computer and Information Sciences Temple University Philadelphia USA Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 PR China

Federated learning (FL) provides a privacy-aware distributed learning framework using private data belonging to multiple users (or clients). Often the data is generated continuously by the sensors deployed by different clients and is increasingly multimodal. However, the data is invariably imbalanced across different modalities, and certain modalities may even be missing from some clients for certain periods due to sensor failure or unavailability. Furthermore, the available data across clients may not be identically distributed (non-IID). Non-IID data is already known to be a problem with FL, and incomplete modalities could worsen the results even further. Effectively fusing multimodal data with incompleteness and training a high-accuracy global model is still a challenging problem. To tackle this problem, we propose a novel framework called MFedNI that extracts cross-modal temporal features and adaptively adjusts the weights of each modality to mitigate the effect of modal incompleteness. The prototype contrastive learning (PCL)-based feature alignment method further improves the consistency of feature representations across clients. We evaluated MFedNI using two multimodal datasets, HAR and DEAP with different levels of incompleteness. The results demonstrate that MFedNI outperforms other approaches and can train highly accurate models on datasets comprising non-IID multimodal data with incompleteness.

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Blackhole-Inspired Thermal Trapping with Graded Heat-Conduction Metadevices

arXiv

引用

arXiv 2022年

作者： Xu, Liujun Liu, Jinrong Jin, Peng Xu, Guoqiang Li, Jiaxin Ouyang, Xiaoping Li, Ying Qiu, Cheng-Wei Huang, Jiping Fudan University Shanghai200438 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Graduate School of China Academy of Engineering Physics Beijing100193 China School of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China School of Materials Science and Engineering Xiangtan University Xiangtan411105 China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining314400 China

Black holes are one of the most intriguing predictions of general relativity. So far, metadevices have enabled analogous black holes to trap light or sound in laboratory spacetime. However, trapping heat in a conductive ambient is still challenging because diffusive behaviors are directionless. Inspired by black holes, we construct graded heat-conduction metadevices to achieve thermal trapping, resorting to the imitated advection produced by graded thermal conductivities rather than the trivial solution of using insulation materials to confine thermal diffusion. We experimentally demonstrate thermal trapping for guiding hot spots to diffuse towards the center. Graded heat-conduction metadevices have advantages in energy-efficient thermal regulation because the imitated advection has a similar temperature field effect to the realistic advection that is usually driven by external energy sources. These results also provide insights into correlating transformation thermotics with other disciplines such as cosmology for emerging heat control schemes. Copyright © 2022, The Authors. All rights reserved.

关键词： Advection

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Roadmap on Neuromorphic Photonics

arXiv

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arXiv 2025年

作者： Brunner, Daniel Shastri, Bhavin J. Al-Qadasi, Mohammed A. Ballani, H. Barbay, Sylvain Biasi, Stefano Bienstman, Peter Bilodeau, Simon Bogaerts, Wim Böhm, Fabian Brennan, G. Buckley, Sonia Cai, Xinlun Strinati, Marcello Calvanese Canakci, B. Charbonnier, Benoit Chemnitz, Mario Chen, Yitong Cheung, Stanley Chiles, Jeff Choi, Suyeon Christodoulides, Demetrios N. Chrostowski, Lukas Chu, J. Clegg, J.H. Cletheroe, D. Conti, Claudio Dai, Qionghai Di Lauro, Luigi Diamantopoulos, Nikolaos-Panteleimon Dinc, Niyazi Ulas Ewaniuk, Jacob Fan, Shanhui Fang, Lu Franchi, Riccardo Freire, Pedro Gentilini, Silvia Gigan, Sylvain Giorgi, Gian Luca Gkantsidis, C. Gladrow, J. Goi, Elena Goldmann, M. Grabulosa, A. Gu, Min Guo, Xianxin Hejda, Matěj Horst, F. Hsieh, Jih-Liang Hu, Jianqi Hu, Juejun Huang, Chaoran Hurtado, Antonio Jaurigue, Lina Kalinin, K.P. Kamalian-Kopae, Morteza Kelly, D.J. Khajavikhan, Mercedeh Kremer, H. Laydevant, Jeremie Lederman, Joshua C. Lee, Jongheon Lenstra, Daan Li, Gordon H.Y. Li, Mo Li, Yuhang Lin, Xing Lin, Zhongjin Lis, Mieszko Lüdge, Kathy Lugnan, Alessio Lupo, Alessandro Lvovsky, A.I. Manuylovich, Egor Marandi, Alireza Marchesin, Federico Massar, Serge McCaughan, Adam N. McMahon, Peter L. Moralis-Pegios, Miltiadis Morandotti, Roberto Moser, Christophe Moss, David J. Mukherjee, Avilash Nikdast, Mahdi Offrein, B.J. Oguz, Ilker Oripov, Bakhrom O'Shea, G. Ozcan, Aydogan Parmigiani, F. Pasricha, Sudeep Pavanello, Fabio Pavesi, Lorenzo Peserico, Nicola Pickup, L. Pierangeli, Davide Pleros, Nikos Porte, Xavier Primavera, Bryce A. Prucnal, Paul Psaltis, Demetri Puts, Lukas Qiao, Fei Rahmani, B. Raineri, Fabrice Ríos Ocampo, Carlos A. Robertson, Joshua Romeira, Bruno Roques-Carmes, Charles Rotenberg, Nir Rowstron, A. Schoenhardt, Steffen Schwartz, Russell L.T. Shainline, Jeffrey M. Shekhar, Sudip Skalli, A. Sohoni, Mandar M. Sorger, Volker J. Soriano, Miguel C. Spall, James Stabile, Ripalta Stiller, Birgit Sunada, Satoshi Tefas, Anastasios Tossoun, Bassem Tsakyridis, Apostolos Turitsyn, Sergei K. Van der Sande, G Université Marie et Louis Pasteur CNRS UMR 6174 Institut FEMTO-ST Besançon25000 France Centre for Nanophotonics Department of Physics Engineering Physics & Astronomy Queen's University Canada Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Microsoft Research Cambridge United Kingdom Université Paris-Saclay CNRS Centre de Nanosciences et de Nanotechnologies France Nanoscience Laboratory Department of Physics University of Trento Italy Photonics Research Group Department of Information Technology Ghent University imec Belgium Princeton University NJ United States Hewlett Packard Labs Hewlett Packard Enterprise Böblingen Germany National Institute of Standards and Technology BoulderCO United States State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-sen University China Enrico Fermi Research Center Rome Italy Université Grenoble-Alpes CEA Leti Grenoble France Leibniz-Institute of Photonic Technology Jena Germany Institute of Applied Optics and Biophysics Jena Germany Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering North Carolina State University NC United States Department of Electrical Engineering Stanford University CA United States University of Southern California Los AngelesCA United States Department of Physics Sapienza University Rome Italy Institute for Complex Systems National Research Council Rome Italy Varennes Canada NTT Device Technology Labs NTT Corporation Kanagawa Atsugi Japan Institute of Electrical and Microengineering School of Engineering École Polytechnique Fédérale de Lausanne Switzerland Edward L. Ginzton Laboratory Stanford University StanfordCA United States Department of Electronic Engineering Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China Aston Univ

Neuromorphic photonics are processors inspired by the human brain and enabled by light (photons) instead of traditional electronics. Neuromorphic photonics and its associated concepts are experiencing a significant resurgence, building on foundational research from the 1980s and 1990s. This renewed momentum is driven by breakthroughs in photonic integration, nonlinear optics, and advanced materials, alongside the growing necessity of neuro-inspired computing in numerous applications of economic and societal relevance. The increasing demand for energy-efficient artificial intelligence (AI) solutions underscores the need for innovation and a cohesive vision to address key challenges, including scalability, energy efficiency, precision, and standardized performance benchmarks. Together, these efforts present an opportunity to establish a unique photonic advantage with practical, real-world applications. This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. The roadmap introduces various approaches to embedding the high-complexity transformations central to neuromorphic computing, focusing on frequency, delay, and spectral embeddings. This is followed by a discussion of architectures of photonic neural networks (PNNs) and an in-depth analysis of methods for implementing these architectures in photonic hardware. Dedicated sections delve into integrated photonic hardware, the realization of photonic weights and memories, and the optimization of training processes for photonic neuromorphic architectures. The roadmap concludes by exploring numerous potential applications, highlighting the challenges and advances necessary to transition neuromorphic photonic computing from a primarily academic pursuit to a technolog

关键词： Philosophical aspects

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Recent advances of transition radiation: fundamentals and applications

arXiv

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arXiv 2022年

作者： Chen, Ruoxi Gong, Zheng Chen, Jialin Zhang, Xinyan Zhu, Xingjian Chen, Hongsheng Lin, Xiao Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science & Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center The Electromagnetics Academy Zhejiang University Zhejiang University Haining314400 China Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa32000 Israel School of Physics Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Zhejiang University Jinhua321099 China Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing312000 China

Transition radiation is a fundamental process of light emission and occurs whenever a charged particle moves across an inhomogeneous region. One feature of transition radiation is that it can create light emission at arbitrary frequency under any particle velocity. Therefore, transition radiation is of significant importance to both fundamental science and practical applications. In this paper, we provide a brief historical review of transition radiation and its recent development. Moreover, we pay special attention to four typical applications of transition radiation, namely the detection of high-energy particles, coherent radiation sources, beam diagnosis, and excitation of surface waves. Finally, we give an outlook for the research tendency of transition radiation, especially its flexible manipulation by exploiting artificially-engineered materials and nanostructures, such as gain materials, metamaterials, spatial-temporal materials, meta-boundaries, and layered structures with a periodic or non-periodic stacking. © 2022, CC BY.

关键词： Light emission

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Emerging chiral optics from chiral interfaces

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Physical Review B 2021年第19期103卷 195405-195405页

作者： Xinyan Zhang Yuhan Zhong Tony Low Hongsheng Chen Xiao Lin Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota 55455 USA

Twisted atomic bilayers are emerging platforms for manipulating chiral light-matter interaction at the extreme nanoscale, due to their inherent magnetoelectric responses induced by the finite twist angle and quantum interlayer coupling between the atomic layers. Recent studies have reported the direct correspondence between twisted atomic bilayers and chiral metasurfaces, which features a chiral surface conductivity, in addition to the electric and magnetic surface conductivities. However, far-field chiral optics in light of these constitutive conductivities remains unexplored. Within the framework of the full Maxwell equations, we find that the chiral surface conductivity can be exploited to realize perfect polarization transformation between linearly polarized light. Remarkably, such an exotic chiral phenomenon can occur either for the reflected or transmitted light. Moreover, we reveal that all transmitted light through the judiciously designed chiral surface conductivity can always have the polarization different from the incident light, irrespective of the incident angle.

关键词： Electromagnetism Extraordinary optical transmission Light-matter interaction Refraction

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Configurable Phase Transitions in a Topological Thermal Material

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Physical Review Letters 2021年第10期127卷 105901-105901页

作者： Guoqiang Xu Ying Li Wei Li Shanhui Fan Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Republic of Singapore Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China GPL Photonics Laboratory State Key Laboratory of Applied Optics Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun 130033 China Department of Electrical Engineering and Ginzton Laboratory Stanford University Stanford California 94305 USA

Diffusive nature of thermal transportation fundamentally restricts topological characteristics due to the absence of a sufficient parametric space with complex dimensionalities. Here, we create an orthogonal advection space with two advective pairs to reveal the unexplored topological transitions in thermal material. We demonstrate four types of configurable thermal phases, including the nontrivial dynamic-equilibrium distribution, nonchiral steplike π-phase transition, and another two trivial profiles related to the anti-parity-time symmetry nature. Our findings provide a recipe for realizing a topologically robust thermal system under arbitrary perturbations.

关键词： Diffusion Heat transfer Topological insulators Topological phase transition Metamaterials Topological materials

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Reentrant quantum anomalous Hall effect in molecular beam epitaxy-grown MnBi2Te4 thin films

arXiv

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arXiv 2024年

作者： Li, Yuanzhao Bai, Yunhe Feng, Yang Luan, Jianli Gao, Zongwei Chen, Yang Tong, Yitian Liu, Ruixuan Chong, Su Kong Wang, Kang L. Zhou, Xiaodong Shen, Jian Zhang, Jinsong Wang, Yayu Chen, Chui-Zhen Xie, XinCheng Feng, Xiao He, Ke Xue, Qi-Kun Beijing Academy of Quantum Information Sciences Beijing100193 China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA90095 United States Department of Physics and Astronomy University of California Los Angeles Los AngelesCA90095 United States State Key Laboratory of Surface Physics Department of Physics Fudan University Shanghai200438 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200438 China Zhangjiang Fudan International Innovation Center Fudan University Shanghai201210 China Shanghai Research Center for Quantum Sciences Shanghai201315 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China School of Physical Science and Technology Soochow University Suzhou215006 China Institute for Advanced Study Soochow University Suzhou215006 China International Center for Quantum Materials School of Physics Peking University Beijing100871 China Southern University of Science and Technology Shenzhen518055 China Frontier Science Center for Quantum Information Beijing100084 China Hefei National Laboratory Hefei230088 China

In the realm of topological quantum physics, it is widely accepted that a global bandgap is necessary to achieve quantized Hall conductance, irrespective of its origin-magnetic field, exchange coupling or other mechanisms. A material exhibiting the quantum anomalous Hall (QAH) effect had been considered equivalent to a Chern insulator, a band insulator with non-zero Chern number. However, a Chern Anderson insulator (CAI), which arises in a conducting state with exchange-induced Berry curvature splitting and disorder-induced Anderson localization, can in principle exhibit the QAH effect. In this study, we investigate intrinsic magnetic topological insulator MnBi2Te4 thin films grown by molecular beam epitaxy (MBE). We observe a reentrant QAH effect when the Fermi energy enters the valance band and magnetic field equals zero, indicating the emergence of the CAI state. The discovery opens a new avenue for realizing the QAH effect and underscores the fundamental role of both Berry curvature and Anderson localization. © 2024, CC BY-NC-ND.

关键词： Molecular beam epitaxy

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