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Dephasing in Fluxonium Qubits from Coherent Quantum Phase Slips

PRX Quantum 2024年第3期5卷 030341页

作者： Mallika T. Randeria Thomas M. Hazard Agustin Di Paolo Kate Azar Max Hays Leon Ding Junyoung An Michael Gingras Bethany M. Niedzielski Hannah Stickler Jeffrey A. Grover Jonilyn L. Yoder Mollie E. Schwartz William D. Oliver Kyle Serniak Research Laboratory of Electronics Department of Physics Department of Electrical Engineering and Computer Science

Phase slips occur across all Josephson junctions (JJs) at a rate that increases with the impedance of the junction. In superconducting qubits composed of JJ-array superinductors—such as fluxonium—phase slips in the array can lead to decoherence. In particular, phase-slip processes at the individual array junctions can coherently interfere, each with an Aharonov-Casher phase that depends on the offset charges of the array islands. These coherent quantum phase slips (CQPS) perturbatively modify the qubit frequency, and therefore charge noise on the array islands will lead to dephasing. By varying the impedance of the array junctions, we design a set of fluxonium qubits in which the expected phase-slip rate within the JJ array changes by several orders of magnitude. We characterize the coherence times of these qubits and demonstrate that the scaling of CQPS-induced dephasing rates agrees with our theoretical model. Furthermore, we perform noise spectroscopy of two qubits in regimes dominated by either CQPS or flux noise. We find that the noise power spectrum associated with CQPS dephasing appears to be featureless at low frequencies and not 1/f. Numerical simulations indicate that this behavior is consistent with charge noise generated by charge-parity fluctuations within the array. Our findings broadly inform JJ-array-design trade-offs, relevant for the numerous superconducting-qubit designs employing JJ-array superinductors.

关键词： Noise Phase slips Superconductivity Josephson junctions Qubits Superconducting qubits Microwave techniques

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Optimal Coordination of Dual-Setting Directional Overcurrent Relays in Microgrids 4

Optimal Coordination of Dual-Setting Directional Overcurrent...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Foqha, Tareq Alsadi, Samer Refaat, Shady S. Arab American University Department of Electrical Engineering Jenin Palestine Palestine Technical University-Kadoorie Department of Electrical Engineering Tulkarm Palestine University of Hertfordshire School of Physics Engineering and Computer Science Hatfield United Kingdom

ISBN: (纸本)9798350306262

Microgrids are gaining popularity due to their improved reliability and resilience. However, their protection is challenging due to bidirectional current flow and varying fault current levels in different operating modes. This paper proposes the use of dual-setting directional overcurrent relays for microgrid protection. To demonstrate their effectiveness, a comparative analysis of dual-setting relays and conventional relays is conducted. The relay coordination problem is formulated as a nonlinear programming problem, and the settings of relays are optimally determined using genetic algorithm and an efficient hybrid optimization algorithm that combines the modified firefly algorithm and genetic algorithm to minimize the overall relay operating time for primary relays. The proposed scheme is implemented on the distribution section of the IEEE-14 bus system, demonstrating a reduction in total system operating time with dual-setting directional overcurrent relays. © 2024 IEEE.

关键词： Genetic algorithms

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Perturbative Design for Quasi-isospectrality in Multidimensional Photonic systems

Perturbative Design for Quasi-isospectrality in Multidimensi...

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2023 Novel Optical Materials and Applications, NOMA 2023

作者： Lee, Dayeong Park, Hyungchul Park, Seungkyun Park, Namkyoo Yu, Sunkyu Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

We propose a perturbative design method for engineering quasi-isospectrality in multidimensional photonic systems. Our study provides platform-transparency alleviating mathematical strictness of supersymmetric transfo... 详细信息

关键词： Photonic devices

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Modal Localization in Disordered Hyperbolic Lattices

Modal Localization in Disordered Hyperbolic Lattices

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2023 Novel Optical Materials and Applications, NOMA 2023

作者： Park, Hyungchul Park, Seungkyun Kim, Gyunghun Park, Namkyoo Yu, Sunkyu Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

We investigate modal localization of light in disordered hyperbolic lattices. We examine modal area at the bulk of a disordered hyperbolic lattice, which demonstrates that high degree in the lattice leads to the deloc...

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Coordinated EMS for Stability Enhancement of NMGs During Disturbance and Fault Conditions 50

Coordinated EMS for Stability Enhancement of NMGs During Dis...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Hussein, Hossam M. Abdelrahman, Mahmoud S. Kharchouf, Ibtissam Rafin, S.M. Sajjad Hossain Mohammed, Osama A. Energy Systems Research Laboratory Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States

ISBN: (纸本)9781665464543

Power systems are evolving from centralized power grid structures to networks of intelligent microgrids (MGs) that can share power more independently. The interconnection between these MGs, forming the networked MGs (NMGs), will increase the power system’s stability and expand its capabilities. However, a critical concern that necessitates sophisticated management strategies for voltage stabilization across the microgrids is the optimal and effective power sharing between renewable resources and energy storage devices. Effective power sharing between AC and DC sides is critical, necessitating robust control mechanisms for interlink converters (ILC). This paper presents a coordinated energy management strategy for hybrid networked MGs (NMGs) to enhance stability during contingencies and fault instances. The proposed Energy Management System (EMS) operates in two distinct modes: grid-connected and islanded. The system takes specific actions to maintain stability depending on the current mode. In grid-connected mode, the grid supplies the load demand while one of the microgrids (MGs) serves as a backup to ensure system stability. Several scenarios were created using MATLAB/Simulink to test and validate the EMS. These scenarios included varying load demands, pulsed power loads, uncertainties in photovoltaic (PV) system generation, disconnection from the AC side, and fault conditions. The proposed EMS has proven its efficiency while maintaining the system stability within the standard limits and fulfilling the load demands. © 2024 IEEE.

关键词： Microgrids

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Nagaoka ferromagnetism in doped Hubbard models in optical lattices

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Physical Review A 2024年第2期110卷 L021303页

作者： Rhine Samajdar R. N. Bhatt Department of Physics Princeton Center for Theoretical Science Department of Electrical and Computer Engineering

The search for ferromagnetism in the Hubbard model has been a problem of outstanding interest since Nagaoka's original proposal in 1966. Recent advances in quantum simulation have today enabled the study of tunable doped Hubbard models in ultracold atomic systems. Employing large-scale density-matrix renormalization group calculations, we establish the existence of high-spin ground states of the Hubbard model on finite-sized triangular lattices, analyze the microscopic mechanisms behind their origin, and investigate the interplay between ferromagnetism and other competing orders, such as stripes. These results explain (and shed light on) the intriguing observations of ferromagnetic correlations in recent optical-lattice experiments. Additionally, we examine a generalized variant of the Hubbard model, wherein any second electron on a single lattice site is weakly bound compared to the first one, and demonstrate how this modification can lead to enhanced ferromagnetism, at intermediate lengthscales, on the nonfrustrated square lattice as well.

关键词： Cold gases in optical lattices Ferromagnetism Frustrated magnetism Ultracold gases Hubbard model

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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Predictive Analytics for Business Decision-Making: Unleashing the Power of Data-driven Insights 15

Predictive Analytics for Business Decision-Making: Unleashin...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nishat, Nourin Mehedi, Mithila Rashid, Saidur Raasetti, Muniroopesh Lamar University Department of Management Information Systems TX United States Rajshahi University of Engineering and Technology Department of Electrical and Computer Engineering Rajshahi6204 Bangladesh Lamar University Department of Electrical and Electronics Engineering TX United States Lamar University Department of Electrical Engineering TX United States

ISBN: (纸本)9798350370249

This study examines how Predictive Analytics may alter company decision-making by employing sophisticated data-driven insights to improve strategic decisions. The report examines predictive analytics' methods, algorithms, and applications, showing how it has transformed industry decision-making. Predictive Analytics' ability to anticipate future trends, recognize patterns, and generate accurate forecasts based on previous data is examined in corporate operations. The paper uses case studies and practical examples to show how predictive models enhance risk management, resource efficiency, and strategic planning. Business decision-making is explored in relation to predictive analytics components including machine learning algorithms, statistical modeling, and data mining. The study highlights predictive analytics' benefits in sales forecasting, consumer behavior research, and market trend projections. The study also examines the social and economic effects of corporate predictive analytics. It examines how data-driven decision-making boosts competitiveness, innovation, and economic development, serving national interests. For a balanced and transparent deployment, ethics, data privacy, and predictive analytics are considered. This study shows how predictive analytics can help firms make smart decisions in an age of unparalleled data availability. The study shares successful applications and addresses problems to help business professionals, analysts, and researchers navigate datadriven business strategies. © 2024 IEEE.

关键词： Business Decision Making Linear Regression Polynomial Regression Predictive analytics

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Self-Adaptive physics Informed Neural Network for Paper Insulation Degree of Polymerization Prediction 50

Self-Adaptive Physics Informed Neural Network for Paper Insu...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Alquennah, Alamera Nouran AlShaikh Saleh, Mohammad Ghrayeb, Ali Abu-Rub, Haitham Refaat, Shady S. Al-Hajri, Mohammed Khatri, Sunil Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Department of Electrical and Computer Engineering Texas A&M University at Qatar Qatar Foundation Doha Qatar Department of Electrical and Computer Engineering College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar School of Physics Engineering and Computer Science University of Hertfordshire United Kingdom Doha Qatar

ISBN: (纸本)9781665464543

This paper proposes a self adaptive physics informed neural network (SAPINN) model to predict the degree of polymerization (DP) of oil-impregnated paper insulation to quantify the level of degradation and the remaining useful lifetime. The prediction is performed based on historical DP values and the corresponding prediction time step, which are used as input data points to the proposed model. The DP mathematical model is used to constrain the training phase of the AI-model through a weighted sum loss function. The weights of this loss function are adjusted for each epoch through a self-adaptive weighting method to determine the relative importance of the data component and the mathematical model throughout the training by defining these weights as trainable parameters. The trained model is then tested using different datasets which are not part of the training phase. The training and testing datasets are generated synthetically through an algorithm that considers the deviation from the ideal DP degradation curve and incorporates actual measurement noise. The performance of the proposed SAPINN is compared to the baseline PINN and NN (in the absence of physics) to highlight the importance of embedding the mathematical model and the self adaptation algorithm, and theses experiments demonstrate that SAPINN significantly enhances the DP prediction. © 2024 IEEE.

关键词： Forecasting

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