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Enhanced phase estimation with coherently boosted two-mode squeezed beams and its application to optical gyroscopes

Physical Review A 2020年第2期102卷 022614-022614页

作者： Xiao-Qi Xiao Elisha S. Matekole Jiankang Zhao Guihua Zeng Jonathan P. Dowling Hwang Lee Department of Communication Engineering Shanghai Dianji University Shanghai 200240 China Hearne Institute for Theoretical Physics and Department of Physics and Astronomy Louisiana State University Baton Rouge Louisiana 70803 USA State Key Laboratory of Advanced Optical Communication Systems and Networks Institute of Sensing and Navigation Department of Electronic Engineering Shanghai Jiaotong University Shanghai 200030 China National Institute of Information and Communications Technology Tokyo 184-8795 Japan NYU-ECNU Institute of Physics at NYU Shanghai Shanghai 200062 China CAS-Alibaba Quantum Computing Laboratory USTC Shanghai 201315 China

Quantum techniques, developed in recent decades, provide new approaches to achieving high-precision measurements beyond the classical bounds. In this paper, we theoretically demonstrate a metrology method for improving the sensitivity of the interferometric optical gyroscope, robust against the loss, by using coherent-light stimulated two-mode squeezed beams as the light source. The detection protocol is based on a simple intensity measurement, and the quantum noise is far below the shot-noise limit. The enhancement factors for different coherent light fields are analyzed in detail. Additionally, the influence of loss during the propagation in the optical path is studied, and the conditions for achieving sub-shot-noise measurement sensitivity are obtained. We also find that the phase sensitivity of the proposed gyroscope scheme becomes closer to the quantum Cramér-Rao bound with increasing of the photon number of the coherent beams.

关键词： Quantum metrology

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Simulation of Passenger Evacuation of High-speed Train Cars 9

Simulation of Passenger Evacuation of High-speed Train Cars

引用

9th International Conference on Fire Science and Fire Protection engineering, ICFSFPE 2019

作者： Huang, Shenshi Wei, Ruichao Shi, Kai Hu, Jinxing Lu, Shouxiang Lian, Liping Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Science Shenzhen China Smart City Research Institute of China Electronics Technology Group Corporation Shenzhen China State Key Laboratory of Fire Science University of Science and Technology of China Hefei China School of Urban Planning and Design Peking University Shenzhen China

ISBN: (纸本)9781728153223

This study is to evaluate the passenger evacuation of the high-speed train. Geometry was built up based on current Chinese train type CRH380B, and scenarios were designed according to the current emergency response plans. Simulation runs were performed by Pathfinder and uncertainty of evacuation time was obtained. It is observed that the passenger flow rates in train coaches are generally stable. Passenger transfer time and evacuation time are approximately proportional to passenger attendance rate. However, in some complicated scenarios, it is found that the process of passenger transfer affects the evacuation results a lot. Reasonable passenger transfer scale can maximize the effectiveness of emergency plans. This study provides an evaluation method on the topic of train car evacuation, and future work can be carried out to optimize the design of emergency response. © 2019 IEEE.

关键词： Railroad cars

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Quantum Deep Learning for Mutant COVID-19 Strain Prediction

arXiv

引用

arXiv 2022年

作者： Jin, Yu-Xin Hu, Jun-Jie Li, Qi Luo, Zhi-Cheng Zhang, Fang-Yan Tang, Hao Qian, Kun Jin, Xian-Min TuringQ Co. Ltd. Shanghai 200240 Singapore School Of Physics And Astronomy State Key Laboratory Of Advanced Optical Communication Systems And Networks Shanghai Jiao Tong University Shanghai200240 China CAS Center For Excellence Synergetic Innovation Center In Quantum Information And Quantum Physics University Of Science And Technology Of China Anhui Hefei230026 China School Of Mathematical Sciences Nankai University Tianjin300071 China Department Of Information And Intelligence Development Zhongshan Hospital Fudan University Shanghai200032 China Biomedical Engineering Imperial College London LondonSW7 2BX United Kingdom Biological Science Carnegie Mellon University PittsburghPA15213 United States

New COVID-19 epidemic strains like Delta and Omicron with increased transmissibility and pathogenicity emerge and spread across the whole world rapidly while causing high mortality during the pandemic period. Early prediction of possible variants (especially spike protein) of COVID-19 epidemic strains based on available mutated SARS-CoV-2 RNA sequences may lead to early prevention and treatment. Here, combining the advantage of quantum and quantum-inspired algorithms with the wide application of deep learning, we propose a development tool named DeepQuantum, and use this software to realize the goal of predicting spike protein variation structure of COVID-19 epidemic strains. In addition, this hybrid quantum-classical model for the first time achieves quantum-inspired blur convolution similar to classical depthwise convolution and also successfully applies quantum progressive training with quantum circuits, both of which guarantee that our model is the quantum counterpart of the famous style-based GAN. The results state that the fidelities of random generating spike protein variation structure are always beyond 96% for Delta, 94% for Omicron. The training loss curve is more stable and converges better with multiple loss functions compared with the corresponding classical algorithm. At last, evidences that quantum-inspired algorithms promote the classical deep learning and hybrid models effectively predict the mutant strains are strong. © 2022, CC BY.

关键词： Convolution

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A review-driven neural model for sequential recommendation

arXiv

引用

arXiv 2019年

作者： Li, Chenliang Niu, Xichuan Luo, Xiangyang Chen, Zhenzhong Quan, Cong School of Cyber Science and Engineering Wuhan University School of Remote Sensing and Information Engineering Wuhan University State Key Lab of Mathematical Engineering and Advanced Computing Zhengzhou School of Computer Science Wuhan University

Writing review for a purchased item is a unique channel to express a user's opinion in ECommerce. Recently, many deep learning based solutions have been proposed by exploiting user reviews for rating prediction. In contrast, there has been few attempt to enlist the semantic signals covered by user reviews for the task of collaborative filtering. In this paper, we propose a novel review-driven neural sequential recommendation model (named RNS) by considering users' intrinsic preference (long-term) and sequential patterns (short-term). In detail, RNS is devised to encode each user or item with the aspect-aware representations extracted from the reviews. Given a sequence of historical purchased items for a user, we devise a novel hierarchical attention over attention mechanism to capture sequential patterns at both unionlevel and individual-level. Extensive experiments on three real-world datasets of different domains demonstrate that RNS obtains significant performance improvement over uptodate state-of-the-art sequential recommendation models. Copyright © 2019, The Authors. All rights reserved.

关键词： Recommender systems

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The effects of different travel modes and travel destinations on COVID-19 transmission in global cities

引用

Science Bulletin 2022年第6期67卷 588-592,M0003页

作者： Rui Zhu Luc Anselin Michael Batty Mei-Po Kwan Min Chen Wei Luo Tao Cheng Che Kang Lim Paolo Santi Cheng Cheng Qiushi Gu Man Sing Wong Kai Zhang Guonian Lü Carlo Ratti Department of Land Surveying and Geo-Informatics the Hong Kong Polytechnic UniversityHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China Senseable City Laboratory Future Urban Mobility IRGSingapore-MIT Alliance for Research and TechnologySingapore 138602Singapore Center for Spatial Data Science Division of the Social SciencesUniversity of ChicagoChicago IL 60637USA School of Geography Science Nanjing Normal UniversityNanjing 210023China Centre for Advanced Spatial Analysis Faculty of the Built EnvironmentUniversity College LondonLondon WC1E 6BTUK Department of Geography and Resource Management the Chinese University of Hong KongHong KongChina Institute of Space and Earth Information Science the Chinese University of Hong KongHong KongChina Department of Human Geography and Spatial Planning Utrecht UniversityUtrecht 3584 CSNetherlands State Key Laboratory Cultivation Base of Geographical Environment Evolution Nanjing 210023China Department of Geography National University of SingaporeSingapore 117570Singapore SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College LondonLondon WC1E 6BTUK Department of Clinical Translational Research Singapore General HospitalSingapore 169856Singapore Division of Clinical Immunology and Transfusion Medicine Department of Laboratory MedicineKarolinska InstitutetStockholm 17177Sweden Senseable City Laboratory Department of Urban Studies and PlanningMassachusetts Institute of TechnologyCambridgeMA 02139USA Istituto di Informatica e Telematica del CNR Pisa 56124Italy Department of System Science Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Tourism Studies School of HumanitiesSoutheast UniversityNanjing 210096China

The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their ***,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].

关键词：多变量时间序列疫情传播出行方式谷歌公司交通出行病毒传播出行行为大城市

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PS-HOMO: A Protection Scheme for iOS SMS

引用

Journal of Physics: Conference Series 2020年第2期1486卷

作者： Guomiao Zhou Qianqiong Wu Ming Duan Qi Xi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan 450000 China Institute of Electrical Engineering and Automation Luoyang Institute of Science and Technology Luoyang Henan 471000 China

iOS Short Message Service (SMS) transmits messages in plaintext, which would lead to potential security threats of data. It is meaningful to design a safe and effective SMS. In this paper, all iOS Security Guides are referred to and all the cryptographic algorithms in iOS data protection are analyzed. Because there is no homomorphic encryption, a library of homomorphic encryption algorithms for iOS based on RSA and Paillier is developed. Using this library, a protection scheme PS-HOMO for protecting iOS SMS data is proposed and implemented. Under the premise of ensuring the data confidentiality, PS-HOMO also has the function of ciphertext operation due to the multiplicative homomorphism of RSA and additive homomorphism of Paillier. A comprehensive performance test was performed on PS-HOMO. The results show that the performance is in line with our expectations and will not have a major impact on the original system. The security of PS-HOMO is theoretically analyzed from man-in-the-middle attack, replay attack, and traffic analysis attack. Two application scenarios of the PS-HOMO, remote anonymous voting and privacy telemedicine service are envisaged. We hope PS-HOMO would play a significant role in privacy protection.

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Long-range propagation of magnon polaritons in a canted antiferromagnet

引用

Physical Review Applied 2025年第6期23卷 L061002-L061002页

作者： Hanchen Wang Jinlong Wang Kanglin Yu Lutong Sheng Yongjian Zhou Rundong Yuan Chensong Hua Weichao Yu Junfeng Hu Jilei Chen Cheng Song Jean-Philippe Ansermet Mehrdad Elyasi Gerrit E.W. Bauer Haiming Yu Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing 100191 China International Quantum Academy Shenzhen 518048 China Department of Materials ETH Zurich Zurich 8093 Switzerland Key Laboratory of Advanced Materials (MOE) School of Materials Science and Engineering Tsinghua University Beijing 100084 China Cavendish Laboratory Department of Physics University of Cambridge Cambridge CB3 0HE United Kingdom Institute for Nanoelectronic Devices and Quantum Computing State Key Laboratory of Surface Physics Fudan University Shanghai 200433 China Zhangjiang Fudan International Innovation Center Fudan University Shanghai 201210 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Institute of Physics École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland WPI Advanced Institute for Materials Research Tohoku University Sendai 980-8577 Japan Kavli Institute for Theoretical Sciences University of the Chinese Academy of Sciences Beijing 100190 China Institute for Materials Research Tohoku University Sendai 980-8577 Japan

We report strong coupling between magnons and cavity photons in a single-crystalline slab of α-Fe2O3 (hematite) with canted antiferromagnetic order at room temperature. The hybridized magnon polaritons propagate over many millimeters due to their high group velocities, as experimentally characterized using time-of-flight spectroscopy. The conventional dipolar and spin-orbit-interaction-induced chirality can explain an observed nonreciprocity. Our findings highlight the potential of antiferromagnetic magnon polaritons for on-chip spin-based information technologies.

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Stealthy Malware Detection Based on Deep Neural Network

引用

Journal of Physics: Conference Series 2020年第1期1437卷

作者： Shoupu Lu Qingbao Li Xinbing Zhu State Key laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan Province 450001 China Henan University of Economics and Law Zhengzhou Henan Province 450000 China

Network attacks using advanced local hiding technology have not only increased, but also become a serious threat. However, attacks using these technologies can not be detected through traffic detection, and some attacks imitate benign traffic to avoid detection. To solve these problems, a malware process detection method based on process behavior in possibly infected terminals is proposed. In this method, a deep neural network is introduced to classify malware processes. Firstly, the recurrent neural network is trained to extract the characteristics of process behavior. Secondly, training convolutional neural network is used to classify feature images generated by trained RNN features. The experiments results show that this method can effectively extract the features of malicious processes, and the AUC of ROC curve is 0.97 in the best case.

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Preparing a commercial quantum key distribution system for certification against implementation loopholes

arXiv

引用

arXiv 2023年

作者： Makarov, Vadim Abrikosov, Alexey Chaiwongkhot, Poompong Fedorov, Aleksey K. Huang, Anqi Kiktenko, Evgeny Petrov, Mikhail Ponosova, Anastasiya Ruzhitskaya, Daria Tayduganov, Andrey Trefilov, Daniil Zaitsev, Konstantin Russian Quantum Center Skolkovo Moscow121205 Russia Vigo Quantum Communication Center University of Vigo VigoE-36310 Spain NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Bangkok10110 Thailand QRate Skolkovo Moscow143026 Russia Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Steklov Mathematical Institute Russian Academy of Sciences Moscow119991 Russia atlanTTic Research Center University of Vigo VigoE-36310 Spain School of Telecommunication Engineering Department of Signal Theory and Communications University of Vigo VigoE-36310 Spain National Research University Higher School of Economics Moscow101000 Russia

A commercial quantum key distribution (QKD) system needs to be formally certified to enable its wide deployment. The certification should include the system’s robustness against known implementation loopholes and attacks that exploit them. Here we ready a fiber-optic QKD system for this procedure. The system has a prepare-and-measure scheme with decoy-state BB84 protocol, polarisation encoding, qubit source rate of 312.5 MHz, and is manufactured by QRate. We detail its hardware and post-processing. We analyse the hardware for known implementation loopholes, search for possible new loopholes, and discuss countermeasures. We then amend the system design to address the highest-risk loopholes identified. We also work out technical requirements on the certification lab and outline its possible structure. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum cryptography

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Block Ciphers Classification Based on Random Forest

引用

Journal of Physics: Conference Series 2019年第3期1168卷 032015-032015页

作者： Hu, Xinyi Zhao, Yaqun State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China State Key Laboratory of Cryptography and Science China

Discriminant analysis is an important analytical method in cryptanalysis. When only known ciphertext, the encryption algorithm for identifying and classifying is an important part of distinguishing analysis. In this paper, a random forest classifier is used to classify eight block ciphers of ECB mode and eight block ciphers of CBC mode. By designing a feature based on ciphertext recombination and location-specific, the results show that the designed features can effectively extract the information of ciphertext data. In ECB mode, 8 algorithms can be successfully classified with an accuracy of more than 87%. In CBC mode, it can also be classified with higher accuracy than random.

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