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arXiv 2024年

作者： Yang, Bowen Patel, Tarun Cheng, Meixin Pichugin, Kostyantyn Tian, Lin Sherlekar, Nachiket Yan, Shaohua Fu, Yang Tian, Shangjie Lei, Hechang Reimer, Michael E. Okamoto, Junichi Tsen, Adam W. Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Chemistry University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooN2L 3G1 Canada Department of Physics Beijing Key Laboratory of Optoelectronic Functional Materials & Micro-nano Devices Renmin University of China Beijing100872 China School of Materials Science and Engineering Anhui University Hefei230601 China Institute of Physics University of Freiburg Hermann-Herder-Str. 3 Freiburg79104 Germany EUCOR Centre for Quantum Science and Quantum Computing University of Freiburg Hermann-Herder-Str. 3 Freiburg79104 Germany

Various noncollinear spin textures and magnetic phases have been predicted in twisted two-dimensional CrI3 due to competing ferromagnetic (FM) and antiferromagnetic (AFM) interlayer exchange from moiré stacking — with potential spintronic applications even when the underlying material possesses a negligible Dzyaloshinskii–Moriya or dipole–dipole interaction. Recent measurements have shown evidence of coexisting FM and AFM layer order in small-twist-angle CrI3 bilayers and double bilayers. Yet, the nature of the magnetic textures remains unresolved and possibilities for their manipulation and electrical readout are unexplored. Here, we use tunneling magnetoresistance to investigate the collective spin states of twisted double-bilayer CrI3 under both out-of-plane and in-plane magnetic fields together with detailed micromagnetic simulations of domain dynamics based on magnetic circular dichroism. Our results capture hysteretic and anisotropic field evolutions of the magnetic states and we further uncover two distinct non-volatile spin textures (out-of-plane and in-plane domains) at ≈1° twist angle, with a different global tunneling resistance that can be switched by magnetic field. © 2024, CC BY-NC-ND.

关键词： Chromium compounds

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Editorial: special issue on “The security, privacy, and digital forensics of mobile networks and mobile cloud”

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Digital Communications and Networks 2019年第4期5卷 203-204页

作者： Lei Chen Guofei Gu Wenjia Li Rami J.Haddad the Department of Information Technology Allen E. Paulson College of Engineering and Computing at Georgia Southern University USA the Department of Computer Science & Engineering at Texas A&M University Computer Science at New York Institute of Technology (NYIT) since the Department of Electrical and Computer Allen E. Paulson College of Engineering and Computing at Georgia Southern University the Optical Networks and Smart Grid Applications (ONSmart) Laboratory GSU

To many people, smartphones and other mobile devices have been an indispensable part of their daily lives. With personal financial and medical information and job-related data processed on mobile devices,systems and networks as well as the connected mobile cloud, it is critically important that secure mobile operating systems, secure mobile applications, secure smartphone devices, secure cellular networks and secure mobile cloud all keep up with the ever-growing mobile usage.

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A Spectrum Resource Sharing Algorithm for IoT Networks based on Reinforcement Learning 16

A Spectrum Resource Sharing Algorithm for IoT Networks based...

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16th IEEE International Wireless Communications and Mobile computing Conference, IWCMC 2020

作者： Shi, Zhaoyuan Xie, Xianzhong Kadoch, Michel Cheriet, Mohamed Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China École de Technologie Supérieure University of Quebec Department of Electrical Engineering Montreal Canada

ISBN: (纸本)9781728131290

Internet of Things (IoT) has attracted tremendous interest since it can improve production efficiency and system intelligence significantly. However, with the explosive growth of various types of device and data flow, IoT suffers spectrum resource scarcity for wireless applications. In this paper, we propose a solution for spectrum resource sharing in the IoT network, with the objective to facilitate the limited spectrum sharing between different kinds of sensors. To overcome the challenges of unknown dynamic IoT environment, the deep Q-learning network (DQN) is adopted. BS acts as the single agent and centrally manages all spectrum resources. First, a new reward function is designed to drive the learning process, which takes into account the different communication requirements of various sensors. In addition, to improve the learning efficiency of DQN, we compress the action space. Finally, simulation results show that compared with other algorithms, the proposed algorithm can achieve good network performance. © 2020 IEEE.

关键词： Internet of things

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Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

arXiv

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arXiv 2022年

作者： Feng, Xin-Ru Li, Heng-Chao Wang, Rui Du, Qian Jia, Xiuping Plaza, Antonio School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS39762 United States School of Engineering and Information Technology University of New South Wales CanberraACT2612 Australia Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica University of Extremadura Cáceres10071 Spain

Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing. Taking the NMF model as a baseline, we show how to improve NMF by utilizing the main properties of HSIs (e.g., spectral, spatial, and structural information). We categorize three important development directions including constrained NMF, structured NMF, and generalized NMF. Furthermore, several experiments are conducted to illustrate the effectiveness of associated algorithms. Finally, we conclude the article with possible future directions with the purposes of providing guidelines and inspiration to promote the development of hyperspectral unmixing. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Digital noise spectroscopy with a quantum sensor

arXiv

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arXiv 2022年

作者： Wang, Guoqing Zhu, Yuan Li, Boning Li, Changhao Viola, Lorenza Cooper, Alexandre Cappellaro, Paola Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Nuclear Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics and Astronomy Dartmouth College 6127 Wilder Laboratory HanoverNH03755 United States Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada

We introduce and experimentally demonstrate a quantum sensing protocol to sample and reconstruct the auto-correlation of a noise process using a single-qubit sensor under digital control modulation. This Walsh noise spectroscopy method exploits simple sequences of spin-flip pulses to generate a complete basis of digital filters that directly sample the power spectrum of the target noise in the sequency domain - from which the auto-correlation function in the time domain, as well as the power spectrum in the frequency domain, can be reconstructed using linear transformations. Our method, which can also be seen as an implementation of frame-based noise spectroscopy, solves the fundamental difficulty in sampling continuous functions with digital filters by introducing a transformation that relates the arithmetic and logical time domains. In comparison to standard, frequency-based dynamical-decoupling noise spectroscopy protocols, the accuracy of our method is only limited by the sampling and discretization in the time space and can be easily improved, even under limited evolution time due to decoherence and hardware limitations. Finally, we experimentally reconstruct the auto-correlation function of the effective magnetic field produced by the nuclear-spin bath on the electronic spin of a single nitrogen-vacancy center in diamond, discuss practical limitations of the method, and avenues for further improving the reconstruction accuracy. Copyright © 2022, The Authors. All rights reserved.

关键词： Digital filters

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LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction

arXiv

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arXiv 2021年

作者： Lin, Yi Liu, Yanfei Chen, Hao Yang, Xin Ma, Kai Zheng, Yefeng Cheng, Kwang-Ting The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Shenzhen United Imaging Research Institute of Innovative Medical Equipment Real-Time Laboratory Shenzhen518048 China The Department of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Jarvis Research Center Tencent YouTu Lab Shenzhen518057 China

Radiation therapy treatment planning requires balancing the delivery of the target dose while sparing normal tissues, making it a complex process. To streamline the planning process and enhance its quality, there is a growing demand for knowledge-based planning (KBP). Ensemble learning has shown impressive power in various deep learning tasks, and it has great potential to improve the performance of KBP. However, the effectiveness of ensemble learning heavily depends on the diversity and individual accuracy of the base learners. Moreover, the complexity of model ensembles is a major concern, as it requires maintaining multiple models during inference, leading to increased computational cost and storage overhead. In this study, we propose a novel learning-based ensemble approach named LENAS, which integrates neural architecture search with knowledge distillation for 3D radiotherapy dose prediction. Our approach starts by exhaustively searching each block from an enormous architecture space to identify multiple architectures that exhibit promising performance and significant diversity. To mitigate the complexity introduced by the model ensemble, we adopt the teacher-student paradigm, leveraging the diverse outputs from multiple learned networks as supervisory signals to guide the training of the student network. Furthermore, to preserve high-level semantic information, we design a hybrid-loss to optimize the student network, enabling it to recover the knowledge embedded within the teacher networks. The proposed method has been evaluated on two public datasets, OpenKBP and AIMIS. Extensive experimental results demonstrate the effectiveness of our method and its superior performance to the state-of-the-art methods. Code: ***/hust-linyi/LENAS. © 2021, CC BY.

关键词： Students

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Quantum generative training using rényi divergences

arXiv

引用

arXiv 2021年

作者： Kieferová, Mária Marrero, Carlos Ortiz Wiebe, Nathan Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology Sydney Data Sciences & Analytics Group Department of Electrical & Computer Engineering Pacific Northwest National Laboratory North Carolina State University Department of Computer Science Department of Physics High Performance Computing Group University of Toronto University of Washington Pacific Northwest National Laboratory

Quantum neural networks (QNNs) are a framework for creating quantum algorithms that promises to combine the speedups of quantum computation with the widespread successes of machine learning. A major challenge in QNN development is a concentration of measure phenomenon known as a barren plateau that leads to exponentially small gradients for a range of QNNs models. In this work, we examine the assumptions that give rise to barren plateaus and show that an unbounded loss function can circumvent the existing no-go results. We propose a training algorithm that minimizes the maximal Rényi divergence of order two and present techniques for gradient computation. We compute the closed form of the gradients for Unitary QNNs and Quantum Boltzmann Machines and provide sufficient conditions for the absence of barren plateaus in these models. We demonstrate our approach in two use cases: thermal state learning and Hamiltonian learning. In our numerical experiments, we observed rapid convergence of our training loss function and frequently archived a 99% average fidelity in fewer than 100 epochs. © 2021, CC BY.

关键词： Quantum computers

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Finite-Size Analysis of Prepare-and-Measure and Decoy-State Quantum Key Distribution via Entropy Accumulation

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PRX Quantum 2025年第2期6卷 020342-020342页

作者： Lars Kamin Amir Arqand Ian George Norbert Lütkenhaus Ernest Y.-Z. Tan Institute for Quantum Computing and Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA

An important goal in quantum key distribution (QKD) is the task of providing a finite-size security proof without the assumption of collective attacks. For prepare-and-measure QKD, one approach for obtaining such proofs is the generalized entropy-accumulation theorem (GEAT) but thus far it has only been applied to study a small selection of protocols. In this work, we present techniques for applying the GEAT in finite-size analysis of generic prepare-and-measure protocols, with a focus on decoy-state protocols. In particular, we present an improved approach for computing entropy bounds for decoy-state protocols, which has the dual benefits of providing tighter bounds than previous approaches (even asymptotically) and being compatible with methods for computing min-trade-off functions in the GEAT. Furthermore, we develop methods to incorporate some improvements to the finite-size terms in the GEAT and implement techniques to automatically optimize the min-trade-off function. Our approach also addresses some numerical-stability challenges specific to prepare-and-measure protocols, which have not been addressed in previous works.

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Scalable flux controllers using adiabatic superconductor logic for quantum processors

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Physical Review Research 2023年第1期5卷 013145-013145页

作者： Naoki Takeuchi Taiki Yamae Wenhui Luo Fuminori Hirayama Tsuyoshi Yamamoto Nobuyuki Yoshikawa Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki 305-8568 Japan NEC-AIST Quantum Technology Cooperative Research Laboratory National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki 305-8568 Japan Department of Electrical and Computer Engineering Yokohama National University Yokohama Kanagawa 240-8501 Japan Research Fellow of Japan Society for the Promotion of Science Chiyoda Tokyo 102-0083 Japan Secure System Platform Research Laboratories NEC Corporation Kawasaki Kanagawa 211-0011 Japan Institute of Advanced Sciences Yokohama National University Yokohama Kanagawa 240-8501 Japan

Quantum processors have the potential to accelerate specific computing tasks but are difficult to scale up due to engineering limitations, such as the number of available cables and cooling power for dilution refrigerators for quantum bits (qubits). Hence, it is very important to develop scalable, energy-efficient interface circuits that can control many qubits via a few control lines inside a dilution refrigerator. One of the most important interface circuits is the flux controller (FC), which generates arbitrary dc flux bias to adjust the characteristics of component devices such as qubits. In this paper, we propose and demonstrate FCs using an energy-efficient superconductor logic family, adiabatic quantum-flux-parametron (AQFP) logic. We develop two types of FCs: the AQFP FC and the AQFP/single-flux-quantum (AQFP/SFQ) FC. Both FCs require only a few control lines and have extremely small power dissipation, thus exhibiting high scalability. Furthermore, the AQFP/SFQ FC can control flux bias using ballistic SFQ transmission, which is crucial for integration with qubits. As a proof of concept, we demonstrate AQFP and AQFP/SFQ FCs at 4.2 K, fabricated by the AIST high-speed standard process. Our results indicate that AQFP logic is highly suitable for use as qubit interface circuits for very large-scale quantum processors, especially from the viewpoint of the control line count, power dissipation, and amount of supply currents.

关键词： engineering Devices for digital logic, storage & processing Josephson junctions SQUID Superconducting devices

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Intrusion detection using machine learning techniques: An experimental comparison

arXiv

引用

arXiv 2021年

作者： Tait, Kathryn-Ann Khan, Jan Sher Alqahtani, Fehaid Shah, Awais Aziz Khan, Fadia Ali Rehman, Mujeeb Ur Boulila, Wadii Ahmad, Jawad School of Computing Edinburgh Napier University United Kingdom Department of Electrical and Electronics University of Gaziantep Gaziantep27310 Turkey Department of Computer Science King Fahad Naval Academy Al Jubail Saudi Arabia Politecnico di Bari Italy Department of Electrical Engineering Riphah International University Islamabad Pakistan RIADI Laboratory University of Manouba Manouba2010 Tunisia College of Computer Science and Engineering Taibah University Medina42353 Saudi Arabia

Due to an exponential increase in the number of cyber-attacks, the need for improved Intrusion Detection Systems (IDS) is apparent than ever. In this regard, Machine Learning (ML) techniques are playing a pivotal role in the early classification of the attacks in case of intrusion detection within the system. However, due to the large number of algorithms available, the selection of the right method is a challenging task. To resolve this issue, this paper analyses some of the current state of the art intrusion detection methods and discusses their pros and cons. Further, a review of different ML methods is carried out with four methods showing to be the most suitable one for classifying attacks. Several algorithms are selected and investigated to evaluate the performance of IDS. These IDS classifies binary and multiclass attacks in terms of detecting whether or not the traffic has been considered as benign or an attack. The experimental results demonstrate that binary classification has greater consistency in their accuracy results which ranged from 0.9938 to 0.9977, while multiclass ranges from 0.9294 to 0.9983. However, it has been also observed that multiclass provides the best results with the algorithm k-Nearest neighbor giving an accuracy score of 0.9983 while the binary classification highest score is 0.9977 from Random Forest. The experimental results demonstrate that multiclass classification produces better performance in terms of intrusion detection by specifically differentiating between the attacks and allowing a more targeted response to an attack. © 2021, CC BY.

关键词： Intrusion detection

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