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Towards the manipulation of topological states of matter： a perspective from electron transport

science Bulletin 2018年第9期63卷 580-594页

作者： Cheng Zhang Hai-Zhou Lu Shun-Qing Shen Yong P. Chen Faxian Xi State Key Laboratory of Surface Physics and Department of Physics Fudan University Shanghai 200433 China Collaborative Innovation Center of Advanced Microstructures Nanjing 210093 China Institute for Quantum Science and Engineering and Department of Physics South University of Science and Technology of China Shenzhen 518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Shenzhen 518055 China Department of Physics The University of Hong Kong. Hong Kong China Department of Physics and Astronomy Purdue University West Lafayette 47907 USA Birck Nanotechnology Center Purdue University West Lafayette 47907 USA School of Electrical and Computer Engineering Purdue University West Lafayette 47907 USA Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China

The introduction of topological invariants, ranging from insulators to metals, has provided new insights into the traditional classification of electronic states in condensed matter physics. A sudden change in the topological invariant at the boundaw of a topological nontrivial system leads to the formation of exotic surface states that are dramatically different from its bulk. In recent years, significant advancements in the exploration of the physical properties of these topological systems and regarding device research related to spintronics and quantum computation have been made. Here, we review the progress of the characterization and manipulation of topological phases from the electron transport perspective and also the intriguing chiral/Majorana states that stem from them. We then discuss the future directions of research into these topological states and their potential applications.

关键词： Topological insulator Dirac semimetal Weyl semimetal Quantum transport

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AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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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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Common Limitations of Image Processing Metrics: A Picture Story

arXiv

引用

arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

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Quantum Games and Interactive Tools for Quantum Technologies Outreach and Education

arXiv

引用

arXiv 2022年

作者： Seskir, Zeki C. Migdal, Piotr Weidner, Carrie Anupam, Aditya Case, Nicky Davis, Noah Decaroli, Chiara Ercan, İlke Foti, Caterina Gora, Pawel Jankiewicz, Klementyna La Cour, Brian R. Malo, Jorge Yago Maniscalco, Sabrina Naeemi, Azad Nita, Laurentiu Parvin, Nassim Scafirimuto, Fabio Sherson, Jacob F. Surer, Elif Wootton, James Yeh, Lia Zabello, Olga Chiofalo, Marilù Karlsruhe Institute of Technology ITAS Karlstraße 11 Karlsruhe76133 Germany Quantum Flytrap ul. Ceglowska 29 Warsaw01-809 Poland Quantum Engineering Technology Laboratories H.H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Georgia Institute of Technology School of Literature Media and Communication 686 Cherry St NW AtlantaGA30313 United States The University of Texas at Austin Center for Quantum Research Applied Research Laboratories 10000 Burnet Rd AustinTX78758 United States National Quantum Computing Center Oxfordshire DidcotOX11 0GD United Kingdom TU Delft Department of Microelectronics Mekelweg 4 Delft2628 CD Netherlands QPlayLearn Aalto University InstituteQ - The Finnish Quantum Institute Department of Applied Physics AaltoFI-00076 11000 Finland Quantum AI Foundation Sanocka 9/103 Warsaw02-110 Poland University of Pisa Department of Physics Largo Bruno Pontecorvo 3 PisaI-56126 Italy University of Helsinki InstituteQ - The Finnish Quantum Institute Department of Physics Helsinki Finland Georgia Institute of Technology School of Electrical and Computer Engineering 777 Atlantic Drive NW AtlantaGA30332 United States Quarks Interactive Strada Bailor 93a Miercurea-Ciuc Harghita4500 Romania IBM Quantum IBM Research - Zurich Säumerstrasse 4 Rüschlikon8803 Switzerland ScienceAtHome Fuglesangs Allé 4 Aarhus8210 Denmark Aarhus University Institute for Physics and Astronomy Ny Munkegade 120 Aarhus8000 Denmark Middle East Technical University Graduate School of Informatics Department of Modeling and Simulation Üniversiteler Mah. Dumlupınar Blv. No:1 Ankara06800 Turkey University of Oxford Department of Computer Science Wolfson Building Parks Road OxfordOX1 3QD United Kingdom IBM Quantum IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States Offenburg University of Applied Sciences Department of Business and Industrial Eng

In this article, we provide an extensive overview of a wide range of quantum games and interactive tools that have been employed by the community in recent years. The paper presents selected tools, as described by their developers. The list includes Hello Quantum, Hello Qiskit, Particle in a Box, Psi and Delta, QPlayLearn, Virtual Lab by Quantum Flytrap, Quantum Odyssey, scienceAtHome, and The Virtual Quantum Optics laboratory. Additionally, we present events for quantum game development: hackathons, game jams, and semester projects. Furthermore, we discuss the Quantum Technologies Education for Everyone (QUTE4E) pilot project, which illustrates an effective integration of these interactive tools with quantum outreach and education activities. Finally, we aim at providing guidelines for incorporating quantum games and interactive tools in pedagogic materials to make quantum technologies more accessible for a wider population. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum optics

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A generic transformation to enable optimal repair in MDS codes for distributed storage systems

A generic transformation to enable optimal repair in MDS cod...

引用

作者： Li, Jie Tang, Xiaohu Tian, Chao Information Security and National Computing Grid Laboratory Southwest Jiaotong University Chengdu610031 China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan430062 China Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Department of Electrical and Computer Engineering Texas A and M University College StationTX77843 United States

We propose a generic transformation that can convert any nonbinary (n=k{+}r,k)maximum distance separable (MDS) code into another (n,k) MDS code over the same field such that: 1) some arbitrarily chosen r nodes have the optimal repair bandwidth and the optimal rebuilding access;2) for the remaining k nodes, the normalized repair bandwidth and the normalized rebuilding access (over the file size) are preserved;and 3) the sub-packetization level is increased only by a factor of r. Two immediate applications of this generic transformation are then presented. The first application is that we can transform any nonbinary MDS code with the optimal repair bandwidth or the optimal rebuilding access for the systematic nodes only, into a new MDS code which possesses the corresponding repair optimality for all nodes. The second application is that by applying the transformation multiple times, any nonbinary (n,k) scalar MDS code can be converted into an (n,k) MDS code with the optimal repair bandwidth and the optimal rebuilding access for all nodes, or only a subset of nodes, whose sub-packetization level is also optimal. © 1963-2012 IEEE.

关键词： Repair

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BRIDGE: Benchmarking Large Language Models for Understanding real-world Clinical Practice Text

arXiv

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arXiv 2025年

作者： Wu, Jiageng Gu, Bowen Zhou, Ren Xie, Kevin Snyder, Doug Jiang, Yixing Carducci, Valentina Wyss, Richard Desai, Rishi J. Alsentzer, Emily Celi, Leo Anthony Rodman, Adam Schneeweiss, Sebastian Chen, Jonathan H. Romero-Brufau, Santiago Lin, Kueiyu Joshua Yang, Jie Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Siebel School of Computing and Data Science The Grainger College of Engineering University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Otorhinolaryngology – Head & Neck Surgery Mayo Clinic RochesterMN United States Department of Biostatistics Harvard T.H. Chan School of Public Health Harvard University BostonMA United States Department of Biomedical Data Science Stanford University Palo AltoCA United States Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA United States Division of General Internal Medicine Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States Stanford Center for Biomedical Informatics Research Stanford University StanfordCA United States Division of Hospital Medicine Stanford University StanfordCA United States Stanford Clinical Excellence Research Center Stanford University StanfordCA United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University MA United States Broad Institute of MIT and Harvard CambridgeMA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

Large language models (LLMs) hold great promise for medical applications and are evolving rapidly, with new models being released at an accelerated pace. However, current evaluations of LLMs in clinical contexts remain limited. Most existing benchmarks rely on medical exam-style questions or PubMed-derived text, failing to capture the complexity of real-world electronic health record (EHR) data. Others focus narrowly on specific application scenarios, limiting their generalizability across broader clinical use. To address this gap, we present BRIDGE, a comprehensive multilingual benchmark comprising 87 tasks sourced from real-world clinical data sources across nine languages. We systematically evaluated 52 state-of-the-art LLMs (including DeepSeek-R1, GPT-4o, Gemini, and Llama 4) under various inference strategies. With a total of 13,572 experiments, our results reveal substantial performance variation across model sizes, languages, natural language processing tasks, and clinical specialties. Notably, we demonstrate that open-source LLMs can achieve performance comparable to proprietary models, while medically fine-tuned LLMs based on older architectures often underperform versus updated general-purpose models. The BRIDGE and its corresponding leaderboard serve as a foundational resource and a unique reference for the development and evaluation of new LLMs in real-world clinical text understanding. © 2025, CC BY-NC-SA.

关键词： Electronic health record

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Disentangling the sources of ionizing radiation in superconducting qubits

arXiv

引用

arXiv 2022年

作者： Cardani, L. Colantoni, I. Cruciani, A. De Dominicis, F. D’Imperio, G. Laubenstein, M. Mariani, A. Pagnanini, L. Pirro, S. Tomei, C. Casali, N. Ferroni, F. Frolov, D. Gironi, L. Grassellino, A. Junker, M. Kopas, C. Lachman, E. McRae, C.R.H. Mutus, J. Nastasi, M. Pappas, D.P. Pilipenko, R. Sisti, M. Pettinacci, V. Romanenko, A. Van Zanten, D. Vignati, M. Withrow, J.D. Zhelev, N.Z. INFN Sezione di Roma P.le Aldo Moro 2 RomeI-00185 Italy Consiglio Nazionale delle Ricerche Istituto di Nanotecnologia c/o Dip. Fisica Sapienza Università di Roma Rome00185 Italy Gran Sasso Science Institute L’Aquila67100 Italy INFN Laboratori Nazionali del Gran Sasso AQ AssergiI-67100 Italy Department of Physics and Engineering Physics Astronomy Queen’s University Kingston KingstonONK7L 3N6 Canada Fermi National Accelerator Laboratory BataviaIL60510 United States Dipartimento di Fisica Università di Milano-Bicocca MilanoI-20126 Italy INFN Sezione di Milano-Bicocca MilanoI-20126 Italy Rigetti Computing 2919 Seventh Street BerkeleyCA94710 United States National Institute of Standards and Technology BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO80309 United States Dipartimento di Fisica Sapienza Università di Roma P.le Aldo Moro 2 RomeI-00185 Italy Physics Department University of Florida United States Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL60208 United States

Radioactivity was recently discovered as a source of decoherence and correlated errors for the real-world implementation of superconducting quantum processors. In this work, we measure levels of radioactivity present in a typical laboratory environment (from muons, neutrons, and γ-rays emitted by naturally occurring radioactive isotopes) and in the most commonly used materials for the assembly and operation of state-of-the-art superconducting qubits. We present a GEANT-4 based simulation to predict the rate of impacts and the amount of energy released in a qubit chip from each of the mentioned sources. We finally propose mitigation strategies for the operation of next-generation qubits in a radiopure environment. Copyright © 2022, The Authors. All rights reserved.

关键词： Radioactivity

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A Quantum Secret Sharing Scheme Using Orbital Angular Momentum onto Multiple Spin States Based on Fibonacci Compression Encoding

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Communications in Theoretical Physics 2018年第10期70卷 384-390页

作者： Hong Lai Ming-Xing Luo Yong-Jian Xu Josef Pieprzyk Jun Zhang Lei Pan Mehmet A.Orgun School of Computer and Information Science and Centre for Research and Innovation in Software Engineering（RISE） Southwest UniversityChongqing 400715China Information Security and National Computing Grid Laboratory School of Information Science and TechnologySouthwest Jiaotong UniversityChengdu 610031China Data61 Commonwealth Scientific and Industrial Research OrganisationSydneyAustralia Institute of Computer Science Polish Academy of SciencesWarsaw01-248Poland School of Software and Electrical Engineering Swinburne University of TechnologyHawthornVIC3122Australia School of Information Technology Deakin UniversityGeelongVIC3220Australia Department of Computing Macquarie UniversitySydneyNSW 2109Australia Faculty of Information Technology Macao University of Science and TechnologyAvenida Wai LongTaipa999078Macao

Since the use of a quantum channel is very expensive for transmitting large messages, it is vital to develop an effective quantum compression encoding scheme that is easy to implement. Given that, with the single-photon spin-orbit entanglement, we propose a quantum secret sharing scheme using orbital angular momentum onto multiple spin states based on Fibonacci compression encoding. In our proposed scheme, we can represent the frequency of any secret message which is typically collection of bits encodings of text or integers as a bitstring using the base Fibonacci sequence, which is encoded multiple spin states for secret shares transmitted to participants. We demonstrate that Fibonacci compression encoding carries excellent properties that enable us to achieve more robust quantum secret sharing schemes with fewer number of photons.

关键词： spin-orbit entanglement multiple spin states Fibonacci compression encoding quantum secretsharing

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Quantum Simulation for High Energy Physics

arXiv

引用

arXiv 2022年

作者： Bauer, Christian W. Davoudi, Zohreh Balantekin, A. Baha Bhattacharya, Tanmoy Carena, Marcela de Jong, Wibe A. Draper, Patrick El-Khadra, Aida Gemelke, Nate Hanada, Masanori Kharzeev, Dmitri Lamm, Henry Li, Ying-Ying Liu, Junyu Lukin, Mikhail Meurice, Yannick Monroe, Christopher Nachman, Benjamin Pagano, Guido Preskill, John Rinaldi, Enrico Roggero, Alessandro Santiago, David I. Savage, Martin J. Siddiqi, Irfan Siopsis, George Van Zanten, David Wiebe, Nathan Yamauchi, Yukari Yeter-Aydeniz, Kübra Zorzetti, Silvia Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States Department of Physics University of Wisconsin MadisonWI53706 United States T-2 Los Alamos National Laboratory Los AlamosNM87545 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Enrico Fermi Institute University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Physics University of Chicago ChicagoIL60637 United States Department of Physics Illinois Center for the Advanced Study of the Universe and Illinois Quantum Information Science and Technology Center University of Illinois UrbanaIL61801 United States QuEra Computing Inc BostonMA02135 United States Department of Mathematics University of Surrey Guildford Surrey GU2 7XH United Kingdom Center for Nuclear Theory Department of Physics and Astronomy Stony Brook University NY11794-3800 United States Department of Physics Brookhaven National Laboratory UptonNY11973 United States Pritzker School of Molecular Engineering Chicago Quantum Exchange Kadanoff Center for Theoretical Physics University of Chicago IL60637 United States QBraid Co. Harper Court 5235 ChicagoIL60615 United States Department of Physics Harvard University CambridgeMA02138 United States Department of Physics and Astronomy University of Iowa Iowa CityIA52242 United States Duke Quantum Center Duke University DurhamNC27701 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Physics Duke University DurhamNC27708 United States IonQ Inc. College ParkMD20740 United States Physics and Astronomy Department Rice University HoustonTX77005 United States Institute for Quantum Information and Matter Californi

It is for the first time that quantum simulation for High Energy Physics (HEP) is studied in the U.S. decadal particle-physics community planning, and in fact until recently, this was not considered a mainstream topic in the community. This fact speaks of a remarkable rate of growth of this subfield over the past few years, stimulated by the impressive advancements in Quantum Information sciences (QIS) and associated technologies over the past decade, and the significant investment in this area by the government and private sectors in the U.S. and other countries. High-energy physicists have quickly identified problems of importance to our understanding of nature at the most fundamental level, from tiniest distances to cosmological extents, that are intractable with classical computers but may benefit from quantum advantage. They have initiated, and continue to carry out, a vigorous program in theory, algorithm, and hardware co-design for simulations of relevance to the HEP mission. This community whitepaper is an attempt to bring this exciting and yet challenging area of research to the spotlight, and to elaborate on what the promises, requirements, challenges, and potential solutions are over the next decade and beyond. © 2022, CC BY.

关键词： High energy physics

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