检索结果-内蒙古大学图书馆

作者： Liu, Jiufen Gan, Junjun Wang, Junchao Xu, Che Luo, Xiangyang Zhengzhou Science and Technology Institute Zhengzhou450001 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

The extraction of embedded messages, i.e., extraction attacks are the ultimate purpose of steganalysis, with great practical significance to obtain covert communication content and covert communication forensics. For steganography using a stego key, the extraction attacks are equivalent to the stego key recovery. This paper mainly studies the methods of efficient stego key recovery for LSB steganography in JPEG domain. According to the distribution differences of extracting message bits generated by the correct and incorrect keys, stego key recovery is transformed into the hypothesis test of the message bits distribution extracted by the correct and incorrect keys. Firstly, by fitting the message bits distribution extracted by the correct key, a stego key recovery method based on nonparametric hypothesis test is proposed. Secondly, by utilizing the distribution differences between message bits extracted by the correct and incorrect keys, a stego key recovery method based on parametric hypothesis test is proposed. And then, formulas are given for calculating the sample size and threshold in the proposed stego key recovery model, on the basis of type I error and type II error. Experimental results show that the proposed method can successfully recover the stego key of OutGuess 0.13b, OutGuess0.2, JPEG domain random LSB matching steganography and random F3 steganography, the performance is superior to that of existing methods. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Extraction

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Binary semantic Similarity Comparison based on software gene

Binary semantic Similarity Comparison based on software gene

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2019 International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2019

作者： Sun, Wenjie Chen, Yihang Shan, Zheng Liu, Fudong Wang, Qiuna State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Materials Branch of State Grid Henan Electric Power Company Zhengzhou China

The similarity of binary code is widely used in code copyright protection, vulnerability mining, malicious code analysis and etc. In this paper, we proposed a method for measuring/evaluating the similarity of two binary files based on software genes. Some of Natural language processing methods were introduced into program semantics analysis, including word2vec and doc2vec models to generate assembly instruction embeddings and gene semantic embeddings. Then the longest common subsequence method was applied to evaluate the software similarity. Experiments show that our method can effectively evaluate the similarity of binary files. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Genes

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Feature selection method for image steganalysis based on weighted inner-inter class distance and dispersion criterion 19

Feature selection method for image steganalysis based on wei...

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： Ma, Yuanyuan Luo, Xiangyang Li, Zhenyu Zhang, Yi Bors, Adrian G. State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Department of Computer Science University of York York United Kingdom

ISBN: (纸本)9781450371582

In order to improve the detection of hidden information in signals, additional features are considered as inputs for steganalysers. This research study proposes a feature selection method based on Weighted Inner-Inter class Distance and Dispersion (W2ID) criterion in order to reduce the steganalytic feature dimensionality. The definition of W2ID criterion and an algorithm determining the weight for the W2ID criterion based on the frequency statistical weighting method are proposed. Then, the W2ID criterion is applied in the decision rough set a-positive domain reduction, producing the W2ID-based feature selection method. Experimental results show that the proposed method can reduce the dimension of the feature space and memory requirements of Gabor Filter Residuals (GFR) feature while maintaining or improving the detection accuracy. © 2019 Association for computing Machinery.

关键词： Feature Selection

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LHAASO-KM2A detector simulation using Geant4

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Radiation Detection Technology and Methods 2024年第3期8卷 1437-1447页

作者： Zhen Cao F.Aharonian Q.An Axikegu Y.X.Bai Y.W.Bao D.Bastieri X.J.Bi Y.J.Bi J.T.Cai Q.Cao W.Y.Cao Zhe Cao J.Chang J.F.Chang A.M.Chen E.S.Chen Liang Chen Lin Chen Long Chen M.J.Chen M.L.Chen Q.H.Chen S.H.Chen S.Z.Chen T.L.Chen Y.Chen N.Cheng Y.D.Cheng M.Y.Cui S.W.Cui X.H.Cui Y.D.Cui B.Z.Dai H.L.Dai Z.G.Dai Danzengluobu X.Q.Dong K.K.Duan J.H.Fan Y.Z.Fan J.Fang K.Fang C.F.Feng L.Feng S.H.Feng X.T.Feng Y.L.Feng S.Gabici B.Gao C.D.Gao L.Q.Gao Q.Gao W.Gao W.K.Gao M.M.Ge L.S.Geng G.Giacinti G.H.Gong Q.B.Gou M.H.Gu F.L.Guo X.L.Guo Y.Q.Guo Y.Y.Guo Y.A.Han H.H.He H.N.He J.Y.He X.B.He Y.He Y.K.Hor B.W.Hou C.Hou X.Hou H.B.Hu Q.Hu S.C.Hu D.H.Huang T.Q.Huang W.J.Huang X.T.Huang X.Y.Huang Y.Huang Z.C.Huang X.L.Ji H.Y.Jia K.Jia K.Jiang X.W.Jiang Z.J.Jiang M.Jin M.M.Kang T.Ke D.Kuleshov K.Kurinov B.B.Li Cheng Li Cong Li D.Li F.Li H.B.Li H.C.Li H.Y.Li J.Li Jian Li Jie Li K.Li W.L.Li W.L.Li X.R.Li Xin Li Y.Z.Li Zhe Li Zhuo Li E.W.Liang Y.F.Liang S.J.Lin B.Liu C.Liu D.Liu H.Liu H.D.Liu J.Liu J.L.Liu J.Y.Liu M.Y.Liu R.Y.Liu S.M.Liu W.Liu Y.Liu Y.N.Liu R.Lu Q.Luo H.K.Lv B.Q.Ma L.L.Ma X.H.Ma J.R.Mao Z.Min W.Mitthumsiri H.J.Mu Y.C.Nan A.Neronov Z.W.Ou B.Y.Pang P.Pattarakijwanich Z.Y.Pei M.Y.Qi Y.Q.Qi B.Q.Qiao J.J.Qin D.Ruffolo A.Sáiz D.Semikoz C.Y.Shao L.Shao O.Shchegolev X.D.Sheng F.W.Shu H.C.Song Yu.V.Stenkin V.Stepanov Y.Su Q.N.Sun X.N.Sun Z.B.Sun P.H.T.Tam Q.W.Tang Z.B.Tang W.W.Tian C.Wang C.B.Wang G.W.Wang H.G.Wang H.H.Wang J.C.Wang K.Wang L.P.Wang L.Y.Wang P.H.Wang R.Wang W.Wang X.G.Wang X.Y.Wang Y.Wang Y.D.Wang Y.J.Wang Z.H.Wang Z.X.Wang Zhen Wang Zheng Wang D.M.Wei J.J.Wei Y.J.Wei T.Wen C.Y.Wu H.R.Wu S.Wu X.F.Wu Y.S.Wu S.Q.Xi J.Xia J.J.Xia G.M.Xiang D.X.Xiao G.Xiao G.G.Xin Y.L.Xin Y.Xing Z.Xiong D.L.Xu R.F.Xu R.X.Xu W.L.Xu L.Xue D.H.Yan J.Z.Yan T.Yan C.W.Yang F.Yang F.F.Yang H.W.Yang J.Y.Yang L.L.Yang M.J.Yang R.Z.Yang S.B.Yang Y.H.Yao Z.G.Yao Y.M.Ye L.Q.Yin N.Yin X.H.You Z.Y.You Y.H.Yu Q.Yuan H.Yue H.D.Zeng T.X.Zeng W.Zeng M.Zha B.B.Zhang F.Zhang H.M.Zhang H.Y.Zhang J.L.Zhang L.X.Zhang Li Zhang P.F.Zhang P.P.Zhang R.Zhang S.B.Zh Key Laboratory of Particle Astrophysics&Experimental Physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of SciencesBeijing100049China University of Chinese Academy of Sciences Beijing100049China TIANFU Cosmic Ray Research Center ChengduSichuanChina Dublin Institute for Advanced Studies 31 Fitzwilliam Place2 DublinIreland Max-Planck-Institute for Nuclear Physics 69029Heidelberg103980Germany State Key Laboratory of Particle Detection and Electronics BeijingChina University of Science and Technology of China Hefei230026AnhuiChina School of Physical Science and Technology&School of Information Science and Technology Southwest Jiaotong UniversityChengdu610031SichuanChina School of Astronomy and Space Science Nanjing UniversityNanjing210023JiangsuChina Center for Astrophysics Guangzhou UniversityGuangzhou510006GuangdongChina Hebei Normal University Shijiazhuang050024HebeiChina Key Laboratory of Dark Matter and Space Astronomy&Key Laboratory of Radio Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023JiangsuChina Tsung-Dao Lee Institute&School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China Key Laboratory of Cosmic Rays(Tibet University) Ministry of EducationLhasa 850000TibetChina National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Physics and Astronomy(Zhuhai)&School of Physics(Guangzhou)&Sino-French Institute of Nuclear Engineering and Technology(Zhuhai) Sun Yat-sen UniversityGuangzhouZhuhai 510275519000GuangdongChina School of Physics and Astronomy Yunnan UniversityKunming 650091YunnanChina Institute of Frontier and Interdisciplinary Science Shandong UniversityQingdao 266237ShandongChina APC UniversitéParis CitéCNRS/IN2P3CEA/IRFUObservatoire de ParisParis 11975205France Department of Engineering Phys

KM2A is one of the main sub-arrays of LHAASO,working on gamma ray astronomy and cosmic ray physics at energies above 10 *** simulation is the important foundation for estimating detector performance and data *** is a big challenge to simulate the KM2A detector in the framework of Geant4 due to the need to track numerous photons from a large number of detector units(>6000)with large altitude difference(30)and huge coverage(1.3).In this paper,the design of the KM2A simulation code G4KM2A based on Geant4 is *** process of G4KM2A is optimized mainly in memory consumption to avoid memory *** simplifications are used to significantly speed up the execution of *** running time is reduced by at least 30 times compared to full detector *** particle distributions and the core/angle resolution comparison between simulation and experimental data of the full KM2A array are also presented,which show good agreement.

关键词： LHAASO KM2A Simulation GEANT4

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Non-aligned double JPEG compression detection based on refined Markov features in QDCT domain

Non-aligned double JPEG compression detection based on refin...

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作者： Wang, Jinwei Huang, Wei Luo, Xiangyang Shi, Yun-Qing Jha, Sunil Kr. School of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu China Nanjing University of Information Science and Technology Nanjing China Shanxi Key Laboratory of Network and System Security Xidian University Shanxi China State Key Laboratory of Mathematical Engineering and Advanced Computing ZhengzhouHenan China New Jersey Institute of Technology NewarkNJ United States

Due to the widespread use of the JPEG format, non-aligned double JPEG (NA-DJPEG) compression is very common in image tampering. Therefore, non-aligned double JPEG compression detection has attracted significant attention in digital forensics in recent years. In most of the previous detection algorithms, grayscale images are used directly, or color images are first converted into grayscale images and then processed. However, it is worth noting that most tampered images are color images. To make full use of the color information in images, a detection algorithm, which uses color images directly, is put forward in this paper. The algorithm based on refined Markov in quaternion discrete cosine transform (QDCT) domain is proposed for NA-DJPEG compression detection. Firstly, color information of a given JPEG image is extracted from blocked images to construct quaternion, and then block image QDCT coefficient matrices, including amplitude and three angles (ψ, , and θ) can be obtained. Secondly, the refined Markov features are generated from the transition probability matrix in the corresponding refinement process. Our proposed refinement method not only reduces redundant features but also makes the acquired features more efficient in detection. Therefore, the refined Markov features can not only capture the intra-block correlation between block QDCT coefficients but also improve computing efficiency in real-time. Finally, support vector machine (SVM) method is employed for NA-DJPEG compression detection. The experiment results demonstrate that the proposed algorithm not only make use of color information of images, but also can achieve better detection performance with small size images (i.e., 64 × 64) outperforming state-of-the-art detection methods tested on the same dataset. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Discrete cosine transforms

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A knowledge acquisition automatizing framework from literature exemplified by Na + activation energy prediction of NASICON solid-state electrolyte

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Energy Storage Materials 2025年 80卷

作者： Yue Liu Dahui Liu Zhengwei Yang Xianyuan Ge Wenxuan Yao Jie Wu Maxim Avdeev Siqi Shi State Key Laboratory of Materials for Advanced Nuclear Energy & School of Computer Engineering and Science Shanghai University Shanghai 200444 China Shanghai Engineering Research Center of Intelligent Computing System Shanghai 200444 China Australian Nuclear Science and Technology Organisation Sydney 2232 Australia School of Chemistry The University of Sydney Sydney 2006 Australia State Key Laboratory of Materials for Advanced Nuclear Energy & School of Materials Science and Engineering Shanghai University Shanghai 200444 China Materials Genome Institute Shanghai University Shanghai 200444 China

Materials science literature contains vast amount of structure-activity relationship knowledge crucial for materials discovery and design. However, automatic extraction of domain knowledge from literature remains challenging due to its unstructured and heterogeneous format. Herein, we propose a framework for automating knowledge acquisition, which involves a materials entity-aware relational extraction model (MatRE) to mine triples, an approach to construct a knowledge graph (KG) for the detection of associations among triples, as well as inference and representation of structure-activity relationships in a machine learning (ML)-compatible format. We demonstrate its application in predicting sodium ion activation energy for the NASICON solid-state electrolyte (SSE) system. MatRE trained on a NASICON SSE dataset, achieves an F1-score of 0.80, and is used to extract 260,475 entity–relation triples from 1,808 scientific publications. Furthermore, embedding 24 knowledge bullets from the KG into data pre-processing and feature engineering stages improves the performance and interpretability of six common ML models by up to 25.7%. This work offers key insights into automatic knowledge acquisition from literature and heralds a new paradigm for AI-assisted materials genome engineering driven by both data and knowledge.

关键词：

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Accurate ECG Data Generation with a Simple Generative Adversarial Network 2

Accurate ECG Data Generation with a Simple Generative Advers...

引用

2nd International Conference on Artificial Intelligence and Computer Science, AICS 2020

作者： Wang, Haiyan Ge, Zhaoyang Wang, Zongmin Collaborative Innovation Centre for Internet Healthcare Zhengzhou University 75 Daxue Road Zhengzhou450052 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450003 China Simulation Experiment Centre Zhengzhou University of Aeronautics Zhengzhou450046 China School of Information Engineering Zhengzhou University Zhengzhou450001 China

Since ECG data is highly sensitive medical data, the acquisition of ECG data is highly restricted. However, with the increasing demand for ECG big data research, the improvement of computer computing capabilities, and the development of deep learning, the direction of ECG intelligent analysis is facing a serious lack of standard clinical data. In order to generate more precise ECG data, this paper proposes a GAN architecture for generating ECG heartbeat data. The network structure is simple and does not require any domain knowledge. In this paper, the MIT-BIH Arrhythmia database is selected, from which all left bundle branch block heartbeats are selected to form a training dataset. The training process shows that the proposed GAN structure is effective and accurate, and the generated results show that not only the generated simulated ECG heartbeat data is diverse, but also highly similar to the real data. © Published under licence by IOP Publishing Ltd.

关键词： Electrocardiograms

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Evidence for particle acceleration approaching PeV energies in the W51 complex

引用

Science Bulletin 2024年第18期69卷 2833-2841页

作者： LHAASO Collaboration Zhen Cao F.Aharonian Axikegu Y.X.Bai Y.W.Bao D.Bastieri X.J.Bi Y.J.Bi W.Bian A.V.Bukevich Q.Cao W.Y.Cao Zhe Cao J.Chang J.F.Chang A.M.Chen E.S.Chen H.X.Chen Liang Chen Lin Chen Long Chen M.J.Chen M.L.Chen Q.H.Chen S.Chen S.H.Chen S.Z.Chen T.L.Chen Y.Chen N.Cheng Y.D.Cheng M.Y.Cui S.W.Cui X.H.Cui Y.D.Cui B.Z.Dai H.L.Dai Z.G.Dai Danzengluobu X.Q.Dong K.K.Duan J.H.Fan Y.Z.Fan J.Fang J.H.Fang K.Fang C.F.Feng H.Feng L.Feng S.H.Feng X.T.Feng Y.Feng Y.L.Feng S.Gabici B.Gao C.D.Gao Q.Gao W.Gao W.K.Gao M.M.Ge L.S.Geng G.Giacinti G.H.Gong Q.B.Gou M.H.Gu F.L.Guo X.L.Guo Y.Q.Guo Y.Y.Guo Y.A.Han M.Hasan H.H.He H.N.He J.Y.He Y.He Y.K.Hor B.W.Hou C.Hou X.Hou H.B.Hu Q.Hu S.C.Hu D.H.Huang T.Q.Huang W.J.Huang X.T.Huang X.Y.Huang Y.Huang X.L.Ji H.Y.Jia K.Jia K.Jiang X.W.Jiang Z.J.Jiang M.Jin M.M.Kang I.Karpikov D.Kuleshov K.Kurinov B.B.Li C.M.Li Cheng Li Cong Li D.Li F.Li H.B.Li H.C.Li Jian Li Jie Li K.Li S.D.Li W.L.Li W.L.Li X.R.Li Xin Li Y.Z.Li Zhe Li Zhuo Li E.W.Liang Y.F.Liang S.J.Lin B.Liu C.Liu D.Liu D.B.Liu H.Liu H.D.Liu J.Liu J.L.Liu M.Y.Liu R.Y.Liu S.M.Liu W.Liu Y.Liu Y.N.Liu Q.Luo Y.Luo H.K.Lv B.Q.Ma L.L.Ma X.H.Ma J.R.Mao Z.Min W.Mitthumsiri H.J.Mu Y.C.Nan A.Neronov L.J.Ou P.Pattarakijwanich Z.Y.Pei J.C.Qi M.Y.Qi B.Q.Qiao J.J.Qin A.Raza D.Ruffolo A.Sáiz M.Saeed D.Semikoz L.Shao O.Shchegolev X.D.Sheng F.W.Shu H.C.Song Yu.V.Stenkin V.Stepanov Y.Su D.X.Sun Q.N.Sun X.N.Sun Z.B.Sun J.Takata P.H.T.Tam Q.W.Tang R.Tang Z.B.Tang W.W.Tian C.Wang C.B.Wang G.W.Wang H.G.Wang H.H.Wang J.C.Wang Kai Wang Kai Wang L.P.Wang L.Y.Wang P.H.Wang R.Wang W.Wang X.G.Wang X.Y.Wang Y.Wang Y.D.Wang Y.J.Wang Z.H.Wang Z.X.Wang Zhen Wang Zheng Wang D.M.Wei J.J.Wei Y.J.Wei T.Wen C.Y.Wu H.R.Wu Q.W.Wu S.Wu X.F.Wu Y.S.Wu S.Q.Xi J.Xia G.M.Xiang D.X.Xiao G.Xiao Y.L.Xin Y.Xing D.R.Xiong Z.Xiong D.L.Xu R.F.Xu R.X.Xu W.L.Xu L.Xue D.H.Yan J.Z.Yan T.Yan C.W.Yang C.Y.Yang F.Yang F.F.Yang L.L.Yang M.J.Yang R.Z.Yang W.X.Yang Y.H.Yao Z.G.Yao L.Q.Yin N.Yin X.H.You Z.Y.You Y.H.Yu Q.Yuan H.Yue H.D.Zeng T.X.Zeng W.Zeng M.Zha B.B.Zhang F.Zhang H.Zhang 不详 Key Laboratory of Particle Astrophysics&Experimental Physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China TIANFU Cosmic Ray Research Center ChengduChina Dublin Institute for Advanced Studies 31 Fitzwilliam Place2 DublinIreland Max-Planck-Institut for Nuclear Physics P.O.Box 103980Heidelberg 69029Germany School of Physical Science and Technology&School of Information Science and Technology Southwest Jiaotong UniversityChengdu 610031China School of Astronomy and Space Science Nanjing UniversityNanjing 210023China Center for Astrophysics Guangzhou UniversityGuangzhou 510006China Tsung-Dao Lee Institute&School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China Institute for Nuclear Research of Russian Academy of Sciences Moscow 117312Russia Hebei Normal University Shijiazhuang 050024China University of Science and Technology of China Hefei 230026China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy&Key Laboratory of Radio Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023China Research Center for Astronomical Computing Zhejiang LaboratoryHangzhou 311121China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China School of Physics and Astronomy Yunnan UniversityKunming 650091China Key Laboratory of Cosmic Rays(Tibet University) Ministry of EducationLhasa 850000China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Physics and Astronomy(Zhuhai)&School of Physics(Guangzhou)&Sino-French Institute of Nuclear Engineering and Technology(Zhuhai) Sun Yat-sen UniversityZhuhai 519000&Guangzhou 510275GuangdongChina Institute of Frontier and Interdisciplinary Science Shandong UniversityQingdao 266237China APC UniversitéParis Cité

Theγ-ray emission from the W51 complex is widely acknowledged to be attributed to the interaction between the cosmic rays(CRs)accelerated by the shock of supernova remnant(SNR)W51C and the dense molecular clouds in the adjacent star-forming region,***,the maximum acceleration capability of W51C for CRs remains *** on observations conducted with the Large High Altitude Air Shower Observatory(LHAASO),we report a significant detection ofγrays emanating from the W51 complex,with energies from 2 to 200 *** LHAASO measurements,for the first time,extend theγ-ray emission from the W51 complex beyond 100 TeV and reveal a significant spectrum bending at tens of *** combining the"π^(0)-decay bump"featured data from Fermi-LAT,the broadbandγ-ray spectrum of the W51 region can be well-characterized by a simple pp-collision *** observed spectral bending feature suggests an exponential cutoff at~400 TeV or a power-law break at~200 TeV in the CR proton spectrum,most likely providing the first evidence of SNRs serving as CR accelerators approaching the PeV ***,two young star clusters within W51B could also be theoretically viable to produce the most energeticγrays observed by *** findings strongly support the presence of extreme CR accelerators within the W51 complex and provide new insights into the origin of Galactic CRs.

关键词： UHE c-ray Cosmic rays SNR W51C Star clusters

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Nonreciprocal Single-Photon Band Structure

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Physical Review Letters 2022年第20期128卷 203602-203602页

作者： Jiang-Shan Tang Wei Nie Lei Tang Mingyuan Chen Xin Su Yanqing Lu Franco Nori keyu Xia College of Engineering and Applied Sciences National Laboratory of Solid State Microstructures and Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210023 China School of Physics Nanjing University Nanjing 210023 China RIKEN Quantum Computing Center RIKEN Cluster for Pioneering Research Wako-shi Saitama 351-0198 Japan Center for Joint Quantum Studies and Department of Physics School of Science Tianjin University Tianjin 300350 China School of Electronic Science and Engineering Nanjing University Nanjing 210023 China Physics Department The University of Michigan Ann Arbor Michigan 48109-1040 USA Jiangsu Key Laboratory of Artificial Functional Materials Nanjing University Nanjing 210023 China Key Laboratory of Intelligent Optical Sensing and Manipulation (Nanjing University) Ministry of Education Nanjing 210023 China

We study a single-photon band structure in a one-dimensional coupled-resonator optical waveguide that chirally couples to an array of two-level quantum emitters (QEs). The chiral interaction between the resonator mode and the QE can break the time-reversal symmetry without the magneto-optical effect and an external or synthetic magnetic field. As a result, nonreciprocal single-photon edge states, band gaps, and flat bands appear. By using such a chiral QE coupled-resonator optical waveguide system, including a finite number of unit cells and working in the nonreciprocal band gap, we achieve frequency-multiplexed single-photon circulators with high fidelity and low insertion loss. The chiral QE-light interaction can also protect one-way propagation of single photons against backscattering. Our work opens a new door for studying unconventional photonic band structures without electronic counterparts in condensed matter and exploring its applications in the quantum regime.

关键词： Cavity quantum electrodynamics Light propagation, transmission & absorption Quantum description of light-matter interaction Topological effects in photonic systems

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Factoring integers with sublinear resources on a superconducting quantum processor

arXiv

引用

arXiv 2022年

作者： Yan, Bao Tan, Ziqi Wei, Shijie Jiang, Haocong Wang, Weilong Wang, Hong Luo, Lan Duan, Qianheng Liu, Yiting Shi, Wenhao Fei, Yangyang Meng, Xiangdong Han, Yu Shan, Zheng Chen, Jiachen Zhu, Xuhao Zhang, Chuanyu Jin, Feitong Li, Hekang Song, Chao Wang, Zhen Ma, Zhi Wang, H. Long, Gui-Lu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China School of Physics ZJU-Hangzhou Global Scientific and Technological Innovation Center Interdisciplinary Center for Quantum Information Zhejiang Province Key Laboratory of Quantum Technology and Device Zhejiang University Hangzhou310000 China Beijing Academy of Quantum Information Sciences Beijing100193 China Institute of Information Technology Information Engineering University Zhengzhou450001 China Beijing National Research Center for Information Science and Technology School of Information Tsinghua University Beijing100084 China Frontier Science Center for Quantum Information Beijing100084 China

Shor’s algorithm has seriously challenged information security based on public key cryptosystems. However, to break the widely used RSA-2048 scheme, one needs millions of physical qubits, which is far beyond current technical capabilities. Here, we report a universal quantum algorithm for integer factorization by combining the classical lattice reduction with a quantum approximate optimization algorithm (QAOA). The number of qubits required is O(logN/loglogN), which is sublinear in the bit length of the integer N, making it the most qubit-saving factorization algorithm to date. We demonstrate the algorithm experimentally by factoring integers up to 48 bits with 10 superconducting qubits, the largest integer factored on a quantum device. We estimate that a quantum circuit with 372 physical qubits and a depth of thousands is necessary to challenge RSA-2048 using our algorithm. Our study shows great promise in expediting the application of current noisy quantum computers, and paves the way to factor large integers of realistic cryptographic significance. © 2022, CC BY-NC-ND.

关键词： Qubits

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