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Stabilizing two-qubit entanglement with dynamically decoupled active feedback

Physical Review Applied 2024年第2期21卷 024022-024022页

作者： Sacha Greenfield Leigh Martin Felix Motzoi K. Birgitta Whaley Justin Dressel Eli M. Levenson-Falk Department of Physics and Astronomy University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science and Technology University of Southern California Los Angeles California 90089 USA Institute for Quantum Studies Chapman University Orange California 92866 USA Berkeley Center for Quantum Information and Computation Berkeley California 94720 USA Department of Physics University of California Berkeley California 94720 USA Department of Chemistry University of California Berkeley California 94720 USA Peter Grünberg Institute of Quantum Control (PGI-8) Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße Jülich 52428 Germany Schmid College of Science and Technology Chapman University Orange California 92866 USA Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90089 USA

We propose and analyze a protocol for stabilizing a maximally entangled state of two noninteracting qubits using active state-dependent feedback from a continuous two-qubit half-parity measurement in coordination with a concurrent, noncommuting dynamical decoupling drive. We demonstrate that such a drive can be simultaneous with the measurement and feedback, while also playing a key part in the feedback protocol itself. We show that robust stabilization with near-unit fidelity can be achieved even in the presence of realistic nonidealities, such as time delay in the feedback loop, imperfect state-tracking, inefficient measurements, dephasing from 1/f-distributed qubit-frequency noise, and relaxation. We mitigate feedback-delay error by introducing a forward-state-estimation strategy in the feedback controller that tracks the effects of control signals already in transit. More generally, the steady state is globally attractive without the need for ancillas, regardless of the error state, in contrast to most known feedback and error-correction schemes.

关键词： Measurement back action Optoelectronics Quantum control Quantum feedback Quantum information with solid state qubits Quantum measurements Superconducting qubits Microwave techniques

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Hardware Resource Reliability Analysis based on Deep Learning for Virtual Machine Deployment Optimization 9

Hardware Resource Reliability Analysis based on Deep Learnin...

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9th IEEE Global Conference on Consumer Electronics, GCCE 2020

作者： Huang, Chih-Hao Huang, Bo-Hong Wu, Tin-Yu National Ilan University. Master Program of E-Learning for Multimedia and Network Communications Yilan Taiwan National Ilan University Department of Computer Science and Information Engineering Yilan Taiwan

This paper proposes a new method based on the Apriori algorithm and deep learning technology to improve the flexibility of hardware deployment and to analyze hardware usage reliability for VM. This method can signific... 详细信息

ISBN: (纸本)9781728198026

关键词： Reliability analysis

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New observation on the key schedule of RECTANGLE

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science China(information sciences) 2019年第3期62卷 106-118页

作者： Hailun YAN Yiyuan LUO Mo CHEN Xuejia LAI Department of Computer Science and Engineering Shanghai Jiao Tong University School of Electronics and Information Shanghai Dianji University Westone Cryptologic Research Center

We evaluate the security of RECTANGLE from the perspective of actual key information(AKI).Insufficient AKI permits the attackers to deduce some subkey bits from some other subkey bits, thereby lowering the overall attack complexity or getting more attacked rounds. By considering the interaction between the key schedule's diffusion and the round function's diffusion, we find there exists AKI insufficiency in 4 consecutive rounds for RECTANGLE-80 and 6 consecutive rounds for RECTANGLE-128, although the master key bits achieve complete diffusion in 2 and 4 rounds, respectively. With such weakness of the key schedule, we give a generic meet-in-the-middle attack on 12-round reduced RECTANGLE-128 with only 8 known plaintexts. Moreover, we calculate AKI of variants of RECTANGLE as well as *** we find that both RECTANGLE-128 and PRESENT-128 with no key schedule involve more AKI than the original one. Based on this finding, we slightly modify the key schedule of *** with the original one, this new key schedule matches better with the round function in terms of maximizing AKI. Our work adds more insight to the design of block ciphers' key schedule.

关键词： lightweight block cipher RECTANGLE key schedule round function diffusion

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Design of a Secure Machine Learning-Based Malware Detection and Analysis Scheme

Design of a Secure Machine Learning-Based Malware Detection ...

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Communications, Computing, Cybersecurity, and Informatics (CCCI), International Conference on

作者： Pankaj Kumar Mohammad S. Obaidat Piyush Pandey Mohammad Wazid Ashok Kumar Das Devesh Pratap Singh Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India King Abdullah II School of Information Technology The University of Jordan Amman Jordan School of Computing SRM University Jordan University of Science and Technology Beijing Beijing China School of Computing SRM University Kattankulathur TN India Center for Security Theory Algorithmic Research International Institute of Information Technology Hyderabad India

Today's world is rapidly growing towards the advanced concept of technology. Technology nowadays is at its peak of development and has great advantages in all fields of work, whether it is in industrial automation, farming, weather forecasting, or disease detection and control. Despite its great advantages, it does have its negative side because a major population is using technology and general-purpose computing devices (i.e., desktops, laptops, smartphones, tablets, etc.). Therefore, most of their personal data is available online. The data is constantly in danger of getting stolen by malicious actors (i.e., hackers), who can find a way to get their hands on this data and then use it for their personal gain (i.e., money-making). The best-known technique to get this information is by using malware exploitation on the machine of the target. Malware is an executable or a binary that is malicious in nature. It may have various devastating effects, and in order to detect and make the system defensive, we need to propose a scheme to detect the malware in the system, analyze the particular file, and then mitigate it. In this paper, we propose a secure machine learning-based malware detection and analysis scheme (in short, SML-MDA). The conducted security analysis and formal security verification of the proposed SMLMDA prove its security against various types of potential attacks. The proposed SML-MDA is then compared with the other similar competing schemes, and its performance has been found to be better in terms of various performance metrics like accuracy and F1-score.

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Verifiable Privacy Preservation Scheme for Outsourcing Medi-cal Image to Cloud Through ROI Based Crypto-Watermarking

Verifiable Privacy Preservation Scheme for Outsourcing Medi-...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Chuan Zhou Yi Zhou XingHan An Yan Liu Min Wang XiangZhi Liu Tianjin University Tianjin China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China College of Environmental Science and Engineering Beijing Forestry University Beijing China Inspur Software Co. Ltd. Jinan China The Party School of cpc Jinan Municipal Committee Jinan China

A novel verifiable privacy preservation scheme for outsourcing medical image to cloud through ROI based crypto-watermarking is proposed in the paper. In the proposed scheme, data owner firstly carries out substitution of S-box for the region of interest (ROI) of medical image, and then separates the image into 4 most significant bits (MSBs) plane and 4 least significant bits (LSBs) plane images. Secondly, the hash value of ROI is embedded into the two separated bit plane images using reversible watermarking algorithm. Lastly, some selected hash values are transformed into the initial parameters of chaotic maps, and the two sharing secrets, which are produced through chaos based encryption algorithm, are finally outsourced to two different cloud servers. For authorized users, they can get shares from different cloud servers, and then can losslessly recover the original medical image through a series of decryption operations and extraction of watermarking. Furthermore, the users can verify whether the original image is completely reconstructed or not, they even can locate the tampered parts inside ROI if anyone of the sharing secrets is damaged. Some experiments analyses and comparisons are given to show the security and effectiveness of proposed scheme.

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An Improvement of Coherent Semantic Attention for Image Inpainting 1

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Wang, Weiqing Gu, Enming Fang, Wei School of Computer & Software Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Jiangsu Key Laboratory of Computer Information Process Technology SooChow University Suzhou China

ISBN: (数字)9789811580833

ISBN: (纸本)9789811580826

In some image restoration algorithms of the past, they often do not consider the continuity between pixels, and the internal features of the hole region. The mapping to the image semantically does not take into account the continuity of the feature, resulting in the color of the fault. Or the deformation of the edge contour of the image. In some of the current popular algorithms and models, we can clearly see the color faults and artificial repair traces from their repair results. These discontinuities are mainly because these methods ignore the semantic relevance and feature continuity of the hole region. Therefore, if we want to get a better image repair effect. We have to improve on semantic relevance and feature continuity. We validated the effectiveness of our proposed method in image restoration tasks on the CelebA and Places2 datasets, and our results yielded a better visual experience in some images than existing methods. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Semantics

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PDCapsNet: A Convolution Neural network Method for COVID-19 Detection from Chest X-Ray Images

PDCapsNet: A Convolution Neural Network Method for COVID-19 ...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Zhihao Liang Huijuan Lu Cunqian You Wenjie Zhu Li Xie Yudong Yao Key Laboratory of Electromagnetic Wave Information Technology and Metrology College of Information Engineering China Jiliang University Hangzhou Zhejiang Province China College of Information Engineering China Jiliang University Drore Information and Technology Co. LTD Hangzhou Zhejiang Province China College of Information Engineering China Jiliang University Modern Educational Technology Center Hangzhou City Zhejiang Province China College of Biomedical Engineering and Instrumentation Science Zhejiang University Institute of Advanced Digital Technologies and Instrumentation Hangzhou Zhejiang Province China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken New Jersey USA

The COVID-19 epidemic started in 2019 and it is still important to develop intelligent and efficient diagnostic methods for the detection of COVID-19. In the task of COVID-19 X-ray image recognition, conventional deep neural networks encounter challenges in effectively discerning distinctive features within pneumonia images, resulting in limited generalization capability when confronted with novel samples. To address this issue, we introduce a novel model in this paper, denoted as PDCapsNet. The model effectively exploits Pyconv's multi-scale fusion technique and leverages the capsule network's capacity to encapsulate diverse attributes of specific entities within COVID-19 X-ray images, resulting in enhancements in both accuracy and generalization. Experimental results show that the proposed model outperforms other previous related works.

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Compressed gate characterization for quantum devices with time-correlated noise

arXiv

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arXiv 2023年

作者： Gullans, M.J. Caranti, M. Mills, A.R. Petta, J.R. Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Department of Physics Princeton University PrincetonNJ08544 United States Department of Physics and Astronomy University of California - Los Angeles Los AngelesCA90095 United States Center for Quantum Science and Engineering University of California - Los Angeles Los AngelesCA90095 United States

As quantum devices make steady progress towards intermediate scale and fault-tolerant quantum computing, it is essential to develop rigorous and efficient measurement protocols that account for known sources of noise. Most existing quantum characterization protocols such as gate set tomography and randomized benchmarking assume the noise acting on the qubits is Markovian. However, this assumption is often not valid, as for the case of 1/f charge noise or hyperfine nuclear spin noise. Here, we present a general framework for quantum process tomography (QPT) in the presence of time-correlated noise. We further introduce fidelity benchmarks that quantify the relative strength of different sources of Markovian and non-Markovian noise. As an application of our method, we perform a comparative theoretical and experimental analysis of silicon spin qubits. We first develop a detailed noise model that accounts for the dominant sources of noise and validate the model against experimental data. Applying our framework for time-correlated QPT, we find that the number of independent parameters needed to characterize one and two-qubit gates can be compressed by 10x and 100x, respectively, when compared to the fully generic case. These compressions reduce the amount of tomographic measurements needed in experiment, while also significantly speeding up numerical simulations of noisy quantum circuit dynamics compared to time-dependent Hamiltonian simulation. Using this compressed noise model, we find good agreement between our theoretically predicted process fidelities and two qubit interleaved randomized benchmarking fidelities of 99.8% measured in recent experiments on silicon spin qubits. More broadly, our formalism can be directly extended to develop efficient and scalable tuning protocols for high-fidelity control of large-arrays of quantum devices with non-Markovian noise. © 2023, CC BY.

关键词： Benchmarking

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Optimizing Stabilizer Parities for Improved Logical Qubit Memories

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Physical Review Letters 2021年第24期127卷 240501-240501页

作者： Dripto M. Debroy Laird Egan Crystal Noel Andrew Risinger Daiwei Zhu Debopriyo Biswas Marko Cetina Chris Monroe Kenneth R. Brown Department of Physics Duke University Durham North Carolina 27708 USA Joint Quantum Institute Center for Quantum Information and Computer Science and Departments of Physics and Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA IonQ College Park Maryland 20740 USA

We study variants of Shor’s code that are adept at handling single-axis correlated idling errors, which are commonly observed in many quantum systems. By using the repetition code structure of the Shor’s code basis states, we calculate the logical channel applied to the encoded information when subjected to coherent and correlated single qubit idling errors, followed by stabilizer measurement. Changing the signs of the stabilizer generators allows us to change how the coherent errors interfere, leading to a quantum error-correcting code which performs as well as a classical repetition code of equivalent distance against these errors. We demonstrate a factor of 3.78±1.20 improvement of the logical T2* in a distance-3 logical qubit implemented on a trapped-ion quantum computer. Even-distance versions of our Shor-code variants are decoherence-free subspaces and fully robust to identical and independent coherent idling noise.

关键词： Quantum error correction Quantum information with trapped ions Quantum memories Trapped ions

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A Graph Convolution Neural network Based Method for Insider Threat Detection

A Graph Convolution Neural Network Based Method for Insider ...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Kexiong Fei Jiang Zhou Lin Su Weiping Wang Yong Chen Fan Zhang School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Computer Science Texas Tech University Lubbock USA Information Technology Center China Mobile Beijing China

In this research, we propose Log2Graph, a new insider threat detection method based on graph convolution neural network (GCN). This method first retrieves the corresponding logs and features from log files through feature extraction. Specifically, we use an auxiliary feature of anomaly index to describe relationship between entities, such as users and hosts, instead of establish complex connections between them. Second, these logs and features are augmented through a combination of oversampling and downsampling, to prepare for the next-stage supervised learning process. Third, we use three elaborated rules to construct the graph of each user by connecting the logs according to chronological and logical relationship. At last, the graph convolution neural network constructed is used to detect insider threats. Our validation and evaluation results confirm that Log2Graph can greatly improve the performance of detecting insider threats compared against baseline and existing methods.

关键词： Knowledge engineering Convolution Neural networks Supervised learning Feature extraction Time measurement Data models

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