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Proximal quantum control of spin and spin ensemble with localized control field from skyrmions

Physical Review Applied 2024年第6期22卷 064077-064077页

作者： Md Fahim F. Chowdhury Mohamad Niknam Md Mahadi Rajib Louis-S. Bouchard Jayasimha Atulasimha Department of Mechanical and Nuclear Engineering Virginia Commonwealth University Richmond VA 23284 USA Department of Chemistry and Biochemistry University of California Los Angeles 607 Charles E. Young Drive East Los Angeles CA 90095-1059 USA Center for Quantum Science and Engineering University of California Los Angeles Los Angeles CA 90095 USA Current affiliation: Department of Physics and Astronomy University of California Los Angeles Los Angeles CA 90095 USA Department of Electrical and Computer Engineering Virginia Commonwealth university Richmond VA 23284 USA Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA 94720-1770 USA

Selective control of individual spin qubits is needed for scalable quantum computing based on spin states. Achieving high-fidelity in both single-qubit and two-qubit gates, essential components of universal quantum computers, necessitates highly localized control fields. These fields must be capable of addressing specific spin qubits while minimizing gate errors and crosstalk in adjacent qubits. Overcoming the challenge of generating a localized radio-frequency magnetic field, in the absence of elementary magnetic monopoles, we introduce a technique that combines divergent and convergent nanoscale magnetic skyrmions. This approach produces a precise control field that manipulates spin qubits with high fidelity. We propose the use of 2D skyrmions, which are much easier to fabricate than 3D hedgehog structures. The latter are considered emergent magnetic monopoles, but their fabrication is challenging. In contrast, 2D skyrmions can be created with use of standard semiconductor-foundry processes and offer effective magnetic field confinement in two dimensions. Our comparative analysis of the density-matrix evolution and gate fidelities in scenarios involving proximal skyrmions and nanomagnets indicates potential gate fidelities surpassing 99.95% for π/2 gates and 99.90% for π gates. The average field induced at the nearest neighboring qubit by skyrmions is 0.0332 mT along the x axis (200 nm), while it is 0.507 mT for the nanomagnet case. Notably, the skyrmion configuration generates a significantly lower field on neighboring spin qubits, i.e., 15 times smaller field or approximately 23.5 dB compared to nanomagnets that produce the same field at the target qubit, making it a more-suitable candidate for scalable quantum control architectures by reducing disturbances in adjacent qubits.

关键词： Nanocrystals

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IEEE Transactions on Instrumentation and Measurement 2024年 1-1页

作者： Wu, Jin Wang, Chaoqun Li, Chong Jiang, Yi Zhang, Chengxi Huang, Yulong Qi, Yuhua Xue, Bohuan Jiao, Jianhao Fan, Rui Zhang, Wei Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China School of Control Science and Engineering Shandong University Jinan China Department of Automation and Measurement Ocean University of China Qingdao China Department of Electrical Engineering City University of Hong Kong Hong Kong China School of Internet of Things Jiangnan University Wuxi China Department of Automation Harbin Engineering University Harbin China School of Systems Science and Engineering Sun Yat-Sen University China College of Electronics and Information Engineering Tongji University Shanghai China

Similarity transformation problems are important in robotic instrumentation and computer vision based measurements since in many cases the information of visually observed scene scale is unknown and must be restored for accurate 3-dimensional reconstruction. In existing solvers, the scale is often considered as a scalar, i.e., isotropic, which may be invalid for anisotropic-scale setups. This paper exploits some mathematical coincidences that will lead to efficient solution to these problems. Possible further applications also include hand-eye calibration and structure-from-motion. We revisit pose estimation problems within the framework of similarity transformation, the one that considers scale-stretching, rotation and translation simultaneously. Two major problems are taken into account, i.e., the scale-stretching point-cloud registration and perspective-n-points (PnP). It has been found out that these two problems are quite similar. Moreover, we solve the anisotropic-scale registration problem that is important and is a remaining unsolved one in previous literatures. To compute the globally optimal solution of these non-convex problems, algebraic solution is obtained to compute all local minima using computationally efficient methods. The designed algorithm is deployed for robotic-arm pose estimation. We also extend the algorithm for solving the problem of robust magnetometer calibration. Visual pose experiments verify the superiority of the proposed method compared with representatives, including P3P, Lambda-Twist P3P and EPnP, which can be reproduced by repository in https://***/zarathustr/APnP. IEEE

关键词： Calibration

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Assessment of 1H-MRS spectra via multiset canonical correlation analysis and empirical mode decomposition

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computers in Biology and Medicine 2025年 187卷 109806-109806页

作者： Ahdi, Alieh Ensworth, Alex Schulz, Bretta Russell MacMillan, Erin Mirian, Maryam S. Vahabie, Abdol-Hossein Laule, Cornelia Nygaard, Haakon B. Setaredan, Seyed Kamaledin McKeown, Martin J. School of Electrical and Computer Engineering University of Tehran Tehran Iran Physics & Astronomy University of British Columbia Vancouver Canada UBC MRI Research Centre Faculty of Medicine University of British Columbia Vancouver Canada Pacific Parkinson's Research Centre University of British Columbia Vancouver Canada School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran University of British Columbia Vancouver Canada Division of Neurology Department of Medicine Faculty of Medicine University of British Columbia Vancouver Canada

Proton magnetic resonance spectroscopy (1H-MRS) is a valuable non-invasive method for quantifying brain metabolites. Most MRS spectra are pre-processed by taking the mean of several single-acquisition transients, ultimately resulting in peaks associated with biologically significant molecules. However, simply taking the mean of the transients is prone to errors due to outliers and the non-independence of sequential data acquisitions. Furthermore, this approach cannot identify whether peaks corresponding to different metabolites tend to co-vary across acquisitions. Averaging also assumes a single underlying true signal that is variably corrupted with noise. In this study, we propose a novel analytic approach that models each transient as a combination of deterministic components. This method allows for the extraction of distinct orthogonal components of the MRS spectrum that variably contribute to each transient signal. First, complex Empirical Mode Decomposition (EMD) is applied to extract intrinsic mode functions from the free induction decay (FID) signals. Subsequently, Multiset Canonical Correlation Analysis (MCCA) is employed to obtain the linear combination of intrinsic mode functions from each FID signal that produce the most consistent temporal waveforms across all transients. We applied this method to time-domain 1H-MRS data from 21 Alzheimer's disease (AD) subjects. Using the MCCA method, three significant orthogonal components were extracted. Regression analyses revealed that each of these components significantly contributed to the transient signals. To interpret the isolated spectra contained in the MCCA components, we utilized the LCModel program. The first component was qualitatively similar to the grand mean spectrum but demonstrated a dramatic 40.7 % increase in signal-to-noise ratio (SNR;p 1H-MRS spectra consist of a combination of deterministic components. By isolating these components, the signal-to-noise ratio (SNR) of the spectra is enhanced, an

关键词： Neurodegenerative diseases

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Edge Intelligence in Softwarized 6G: Deep Learning-enabled Network Traffic Predictions

Edge Intelligence in Softwarized 6G: Deep Learning-enabled N...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Zeb, Shah Rathore, Muhammad Ahmad Mahmood, Aamir Hassan, Syed Ali Kim, Jongwon Gidlund, Mikael Pakistan School of Electrical Engineering and Computer Science Gwangju61005 Korea Republic of Mid Sweden University Department of Information Systems and Technology Sweden

ISBN: (纸本)9781665423908

The 6G vision is envisaged to enable agile network expansion and rapid deployment of new on-demand microservices (e.g., visibility services for data traffic management, mobile edge computing services) closer to the network's edge IoT devices. However, providing one of the critical features of network visibility services, i.e., data flow prediction in the network, is challenging at the edge devices within a dynamic cloud-native environment as the traffic flow characteristics are random and sporadic. To provide the AI-native services for the 6G vision, we propose a novel edge-native framework to provide an intelligent prognosis technique for data traffic management in this paper. The prognosis model uses long short-term memory (LSTM)-based encoder-decoder deep learning, which we train on real time-series multivariate data records collected from the edge μ-boxes of a selected testbed network. Our result accurately predicts the statistical characteristics of data traffic and verifies the trained model against the ground truth observations. Moreover, we validate our novel framework with two performance metrics for each feature of the multivariate data. © 2021 IEEE.

关键词： Forecasting

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Fetal lung growth predicts the risk for early-life respiratory infections and childhood asthma

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World Journal of Pediatrics 2024年第5期20卷 481-495页

作者： Dimitra E.Zazara Olympia Giannou Steven Schepanski Mirja Pagenkemper Anastasios D.Giannou Maike Pincus Ioannis Belios Stefan Bonn Ania C.Muntau Kurt Hecher Anke Diemert Petra Clara Arck Division for Experimental Feto-Maternal Medicine Department of Obstetrics and Fetal MedicineUniversity Medical Center Hamburg-Eppendorf(UKE)Martinistraße 5220251HamburgGermany University Children’s Hospital UKEHamburgGermany Computer Engineering and Informatics Department Polytechnic SchoolUniversity of PatrasPatrasGreece Institute of Developmental Neurophysiology Center for Molecular Neurobiology Hamburg(ZMNH)UKEHamburgGermany Department of Obstetrics and Fetal Medicine UKEHamburgGermany Department of General Visceral and Thoracic SurgeryUKEHamburgGermany Section of Molecular Immunology and Gastroenterology Ⅰ.Department of MedicineUKEHamburgGermany Pediatrics and Pediatric Pneumology Practice BerlinGermany Institute of Medical Systems Biology ZMNHUKEHamburgGermany Hamburg Center for Translational Immunology UKEHamburgGermany

Background Early-life respiratory infections and asthma are major health burdens during *** predicting an increased risk for early-life respiratory diseases are ***,we identified the predictive value of ultrasound-monitored fetal lung growth for the risk of early-life respiratory infections and *** Fetal lung size was serially assessed at standardized time points by transabdominal ultrasound in pregnant women participating in a pregnancy *** between fetal lung growth and respiratory infections in infancy or early-onset asthma at five years were ***-learning models relying on extreme gradient boosting regressor or classifier algorithms were developed to predict respiratory infection or asthma risk based on fetal lung *** model development and validation,study participants were randomly divided into a training and a testing group,respectively,by the employed *** Enhanced fetal lung growth throughout pregnancy predicted a lower early-life respiratory infection *** sex was associated with a higher risk for respiratory infections in *** lung growth could also predict the risk of asthma at five years of *** designed three machine-learning models to predict the risk and number of infections in infancy as well as the risk of early-onset *** models’R2 values were 0.92,0.90 and 0.93,respectively,underscoring a high accuracy and agreement between the actual and predicted *** variables included known risk factors and novel predictors,such as ultrasound-monitored fetal lung *** Sonographic monitoring of fetal lung growth allows to predict the risk for early-life respiratory infections and asthma.

关键词： Asthma Child risk Early-life respiratory infections Prenatal lung development Sexual dimorphism

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Data-driven Optimal Cooperative Output Regulation of Unknown Linear Discrete-time Multi-agent systems

Data-driven Optimal Cooperative Output Regulation of Unknown...

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Chinese Control and Decision Conference, CCDC

作者： Dong Liang Yi Dong Chaoli Wang Engang Tian Department of Control Science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

The cooperative output regulation problem has been extensively studied on the basis of the distributed observer approach. However, the majority of the existing research assumes that the dynamics is known previously. To remove this condition, the cooperative output regulation problem is further solved via the data-driven framework where the dynamics of the plant is unknown. First, a data-driven distributed observer is established to estimate the state of the leader with unknown dynamics subject to external inputs. Second, the unknown regulator equations are solved using the iterative recurrent neural network approach. Third, the state-based data-driven distributed control law is synthesized to solve the problem. The optimal gains are derived by solving convex optimization problems using input and state data. Finally, a numerical example is presented to verify the feasibility of the proposed framework.

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Application of Modified MPPT Algorithms: A Comparative Study between Different Types of Solar Cells

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Applied Solar Energy (English translation of Geliotekhnika) 2020年第5期56卷 309-323页

作者： Khodair, D. Salem, M.S. Shaker, A. El Munim, H. E. Abd Abouelatta, M. Department of Electronics and Electrical Communications Faculty of Engineering Ain Shams University Cairo11517 Egypt Department of Engineering Physics Faculty of Engineering and Technology Future University Cairo Egypt Department of Computer Engineering Computer Science and Engineering College University of Ha’il Ha’il Saudi Arabia Department of Electrical Communication and Electronics Systems Engineering Faculty of Engineering Modern Science and Arts University Cairo Egypt Department of Engineering Physics and Mathematics Faculty of Engineering Ain Shams University Cairo11517 Egypt Department of Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo11517 Egypt

Abstract: Due to the broad needs for energy as market demands, searching on how to improve the efficiency of the PV systems is an essential concern for researchers to worry about. So, it is crucial to force the PV system to work at its peak power point in order to get the maximum available power from the photovoltaic panel. This paper presents a comprehensive comparison between four Maximum Power Point Tracking (MPPT) Algorithms;Perturb and Observe (P&O), Incremental Conductance (INC), Modified Variable Step Size Perturb and Observe (M-VSS-P&O) and Modified Variable Step Size Incremental Conductance (M-VSS-INC) by using of the DC-DC boost converter for three different kinds of solar cells. These cells are polycrystalline KC200GT cell, monocrystalline shell SQ85 cell and thin film shell ST40 cell. Simulations have been performed using MATLAB-SIMULINK for the three types of solar cells to investigate their performance under both standard test conditions (STC) and slowly varying and sudden changes in solar irradiance. The study has considered the response time, output power efficiency and steady-state-oscillations. The simulation results of the modified algorithms show an improvement in the cell performance in steady state conditions, tracking time and boost converter efficiency as well as an enhancement in the dynamic response in tracking the maximum power point (MPP) in varying climatic conditions over conventional algorithms. © 2020, Allerton Press, Inc.

关键词： Thin films

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MULTISCALE PHYSICS-INFORMED NEURAL NETWORKS FOR THE INVERSE DESIGN OF HYPERUNIFORM OPTICAL MATERIALS

arXiv

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arXiv 2024年

作者： Riganti, Roberto Zhu, Yilin Cai, Wei Torquato, Salvatore Negro, Luca Dal Physics Department Boston University Boston United States Division of Materials Science & Engineering Boston University Boston United States Department of Mathematics Southern Methodist University Dallas United States Department of Chemistry Department of Physics Princeton Materials Institute Princeton University Princeton United States Department of Electrical & Computer Engineering Division of Materials Science & Engineering Physics Department Photonics Center Boston University Boston United States

In this article, we employ multiscale physics-informed neural networks (MscalePINNs) for the inverse design of finite-size photonic materials with stealthy hyperuniform (SHU) disordered geometries. Specifically, we show that MscalePINNs can capture the fast spatial variations of complex fields scattered by arrays of dielectric nanocylinders arranged according to isotropic SHU point patterns, thus enabling a systematic methodology to inversely retrieve their effective dielectric profiles. Our approach extends the recently developed high-frequency homogenization theory of hyperuniform media and retrieves more general permittivity profiles for applications-relevant finite-size SHU systems, unveiling unique features related to their isotropic nature. In particular, we numerically corroborate the existence of a transparency region beyond the long-wavelength approximation, enabling effective and isotropic homogenization even without disorder-averaging, in contrast to the case of uncorrelated Poisson random patterns. The flexible multiscale network approach introduced here enables the efficient inverse design of more general effective media and finite-size optical metamaterials with isotropic electromagnetic responses beyond the limitations of traditional homogenization theories. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks

arXiv

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arXiv 2024年

作者： Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui In Kim, Dong Letaief, Khaled B. School of Computer Science and Engineering Nanyang Technological University Singapore the School of Automation Guangdong University of Technology China the Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore the Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Hong Kong

The advancement of generative artificial intelligence (GAI) has driven revolutionary applications like ChatGPT. The widespread of these applications relies on the mixture of experts (MoE), which contains multiple experts and selectively engages them for each task to lower operation costs while maintaining performance. Despite MoE, GAI faces challenges in resource consumption when deployed on user devices. This paper proposes mobile edge networks supported MoE-based GAI. We first review the MoE from traditional AI and GAI perspectives, including structure, principles, and applications. We then propose a framework that transfers subtasks to devices in mobile edge networks, aiding GAI model operation on user devices. We discuss challenges in this process and introduce a deep reinforcement learning based algorithm to select edge devices for subtask execution. Experimental results will show that our framework not only facilitates GAI’s deployment on resource-limited devices but also generates higher-quality content compared to methods without edge network support. © 2024, CC BY.

关键词： Deep learning

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Facile integration of giant exchange bias in Fe5GeTe2/oxide heterostructures by atomic layer deposition

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Physical Review Materials 2023年第1期7卷 014008-014008页

作者： Jierui Liang Shanchuan Liang Ti Xie Andrew F. May Thomas Ersevim Qinqin Wang Hyobin Ahn Changgu Lee Xixiang Zhang Jian-Ping Wang Michael A. McGuire Min Ouyang Cheng Gong Department of Electrical and Computer Engineering and Quantum Technology Center University of Maryland College Park Maryland 20742 USA Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Physics University of Maryland College Park Maryland 20742 USA SKKU Advanced Institute of Nanotechnology Sungkyunkwan University Suwon Gyeonggido 16419 Republic of Korea Department of Mechanical Engineering Sungkyunkwan University Suwon Gyeonggido 16419 Republic of Korea King Abdullah University of Science and Technology (KAUST) Physical Sciences and Engineering Division (PSE) Thuwal Saudi Arabia Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota 55455 USA

Exchange bias arises from the interfacial exchange coupling in ferromagnet-antiferromagnet bilayers and manifests as a horizontal shift of the magnetic hysteresis loop, constituting a critical component underpinning a broad range of magnetoresistive logic and memory devices. The facile implementation of exchange bias in van der Waals (vdW) magnets would be a key step towards practical devices for emerging vdW spintronics. Here, we report an easy approach to establishing strong exchange bias in the vdW magnet Fe5GeTe2 by a single-step process—atomic layer deposition (ALD) of oxides on Fe5GeTe2. We successfully created exchange bias of 300–1500 Oe in Fe5GeTe2/Al2O3, Fe5GeTe2/ZnO, and Fe5GeTe2/V2O5 heterostructures, at 130 K. Control experiments showed that increasing the oxidant pulse duration in each ALD cycle or utilizing the stronger oxidant O3 can enhance the exchange bias strength, revealing the key role of the ALD oxidants. Our systematic work elucidates the essential role of ALD-enabled oxidization of Fe5GeTe2 in the formation of exchange bias, and establishes ALD of oxides as a facile, controllable, and generally effective approach to creating giant exchange bias in vdW magnets, representing an integral advance towards practical vdW spintronic devices.

关键词： Exchange bias Magnetism Ferromagnets Van der Waals systems

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