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Inferring Quantum Network Topology Using Local Measurements

PRX Quantum 2023年第4期4卷 040347-040347页

作者： Daniel T. Chen Brian Doolittle Jeffrey Larson Zain H. Saleem Eric Chitambar Mathematics and Computer Science Division Argonne National Laboratory USA Department of Physics University of Illinois at Urbana-Champaign USA Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign USA

Statistical correlations that can be generated across the nodes in a quantum network depend crucially on its topology. However, this topological information might not be known a priori, or it may need to be verified. In this paper, we propose an efficient protocol for distinguishing and inferring the topology of a quantum network. We leverage entropic quantities—namely, the von Neumann entropy and the measured mutual information—as well as measurement covariance to uniquely characterize the topology. We show that the entropic quantities are sufficient to distinguish two networks that prepare GHZ states. Moreover, if qubit measurements are available, both entropic quantities and covariance can be used to infer the network topology without state-preparation assumptions. We show that the protocol can be entirely robust to noise and can be implemented via quantum variational optimization. Numerical experiments on both classical simulators and quantum hardware show that covariance is generally more reliable for accurately and efficiently inferring the topology, whereas entropy-based methods are often better at identifying the absence of entanglement in the low-shot regime.

关键词： Entanglement detection Quantum communication Quantum computation

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Communication and Control Co-design in Non-cooperative Games

arXiv

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arXiv 2025年

作者： Aggarwal, Shubham Başar, Tamer Maity, Dipankar UrbanaIL61801 United States Department of Electrical & Computer Engineering Coordinated Science Laboratory UIUC United States Department of Electrical and Computer Engineering University of North Carolina at Charlotte CharlotteNC28223 United States

In this article, we revisit a communication-control co-design problem for a class of two-player stochastic differential games on an infinite horizon. Each ‘player’ represents two active decision makers, namely a scheduler and a remote controller, which cooperate to optimize over a global objective while competing with the other player. Each player’s scheduler can only intermittently relay state information to its respective controller due to associated cost/constraint to communication. The scheduler’s policy determines the information structure at the controller, thereby affecting the quality of the control inputs. Consequently, it leads to the classical communication-control trade-off problem. A high communication frequency improves the control performance of the player on account of a higher communication cost, and vice versa. Under suitable information structures of the players, we first compute the Nash controller policies for both players in terms of the conditional estimate of the state. Consequently, we reformulate the problem of computing Nash scheduler policies (within a class of parametrized randomized policies) into solving for the steady-state solution of a generalized Sylvester equation. Since the above-mentioned reformulation involves infinite sum of powers of the policy parameters, we provide a projected gradient descent-based algorithm to numerically compute a Nash equilibrium using a truncated polynomial approximation. Finally, we demonstrate the performance of the Nash control and scheduler policies using extensive numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Blockchain-Based Secure Crowdsourcing in Wireless IoT

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Journal of Communications and Information Networks 2022年第1期7卷 23-36页

作者： Daquan Feng Long Zhang Shengli Zhang Qihui Wu Xianggen Xia Guangdong Province Engineering Laboratory for Digital Creative Technology Shenzhen UniversityShenzhen 518060China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of ChinaChengdu 611731China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China Department of Electrical and Computer Engineering University of DelawareNewarkDE 19716USA

With the explosive growth of interconnected smart devices and sensors,the Internet has been entering the Internet of things(IoT)era and revolutionizing many aspects of our daily life. Meanwhile, crowdsourcing has been considered as a promising technology to realize collaborative intelligence. Therefore, more and more IoT-based crowdsourcing applications are emerged to take advantages of the widely distributed IoT devices to sense, collect, and analyze data with the aim to solve complex and nontrivial tasks. However,there exist many technical challenges to be addressed in the IoT-based crowdsourcing, such as security, privacy, and incentive provision. In this paper, we propose a blockchain-based architecture as an integrated solution to realize the secure and trustworthy crowdsourcing in wireless IoT. We first overview the challenges in the traditional crowdsourcing system. Then, we briefly introduce the background of the blockchain and smart contract, and propose a blockchain-based crowdsourcing architecture. In particular, we elaborate the utilization of smart contract on the specific phases of crowdsourcing. By deploying the smart contract instance, we confirm the proposed blockchain-based architecture is feasible.

关键词： crowdsourcing blockchain wireless IoT security privacy incentive

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Analysis of Partial Discharge Characteristics of MVDC Power Cables Optimally Designed for All-Electric Wide Body Aircraft Under Varying Pressure During Takeoff and Descent

Analysis of Partial Discharge Characteristics of MVDC Power ...

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Conference on electrical Insulation and Dielectric Phenomena, (CEIDP)

作者： Md Asifur Rahman Anoy Saha Saikat Chowdhury Mona Ghassemi Jane Lehr Department of Electrical and Computer Engineering Zero Emission Realization of Optimized Energy Systems (ZEROES) Laboratory The University of Texas at Dallas Richardson TX USA Department of Electrical and Computer Engineering University of New Mexico Albuquerque UM USA

ISBN: (数字)9798350366679

ISBN: (纸本)9798350366686

Introducing all-electric aircraft (AEA) is a viable option to reduce greenhouse gas (GHG) emissions from the aviation sector. The wide-body AEA's electric power system needs higher voltage levels (a few kVs) than the voltage levels of existing aircraft cables. Consequently, the aircraft cable insulation needs to withstand higher voltage, maintaining low mass since the EPS contributes about 30% of the total mass of aircraft. Aircraft cables face several challenges: arcing, partial discharge (PD), and electromagnetic interference due to low-pressure environments at high altitudes. Several multilayer insulation designs for aircraft cables were proposed to overcome these challenges, and the optimized fabrication process of these cables was presented in our previous works. This paper analyzes the PD characteristics of fabricated flat insulation samples for the cables under different air pressures as the aircraft operates from 1 atm at the time of takeoff to 18.8 kPa at the typical cruising altitude (12.2 km).

关键词： Partial discharges Insulation Power cables Voltage Nonhomogeneous media Power systems Aircraft Greenhouse gases Faces Cables

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Generating Multichannel Colored Noise for Underwater Acoustic Simulations

Generating Multichannel Colored Noise for Underwater Acousti...

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Underwater Communications and Networking (UComms)

作者： Mandar Chitre Acoustic Research Laboratory Tropical Marine Science Institute Department of Electrical and Computer Engineering National University of Singapore

ISBN: (数字)9798331542511

ISBN: (纸本)9798331542528

Underwater acoustic communication algorithms typically undergo numerical testing within simulated environments prior to real-world application. These simulations require noise samples that accurately reflect the ambient noise in the underwater environment. The common practice of employing a white Gaussian noise model for additive noise in simulations fails to capture the true nature of under-water noise. Given the wideband nature of most underwater communication systems and the frequency-dependence of ambient noise, the assumption of white noise becomes untenable. Furthermore, communication systems equipped with multiple hydrophones for spatial diversity experience correlated noise across hy-drophones. This paper presents a simple technique for generating colored noise that is consistent with the spatial and temporal correlation characteristics of ambient noise measured at sea.

关键词： Underwater communication Spatial diversity Noise Sea measurements White noise Numerical models Underwater acoustics Colored noise Wideband Testing

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An Optimal Approach to Fabricate MVDC Multilayer Insulation systems as Flat Samples for Wide-Body All-Electric Aircraft

An Optimal Approach to Fabricate MVDC Multilayer Insulation ...

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Proceedings of the IEEE Dallas Circuits and systems Workshop (DCAS)

作者： Saikat Chowdhury Arian Azizi Anoy Saha Md Asifur Rahman Mona Ghassemi Jane Lehr Department of Electrical and Computer Engineering Zero Emission Realization of Optimized Energy Systems (ZEROES) Laboratory The University of Texas at Dallas Richardson TX USA Department of Electrical and Computer Engineering University of New Mexico Albuquerque UM USA

ISBN: (数字)9798350349535

ISBN: (纸本)9798350349542

To accomplish the net zero emission target, the current fuel-based transportation sector, which is the largest greenhouse gas emitter, needs to be decarbonized. Moving towards electrified ones is a highly promising solution to reduce carbon emissions. Aviation sectors, which rely on fossil fuels and emit harmful pollutants, are also being challenged by electric and hybrid-electric propulsion systems. Fully electrifying aviation or all-electric aircraft (AEA) requires high-power density and low-system mass electric power systems (EPS) and MVDC emerges as a viable solution to address this critical need. Multilayer Multifunctional electrical Insulation (MMEI) systems can be a solution to address challenges towards higher voltages and higher density designs under aviation harsh environments. Many MMEI systems have been designed in our earlier work. However, only experimental testing can ascertain the intended MMEI systems' electrical insulation performance. This study presents an optimal approach to fabricating the flat samples of down-selected MMEI designs by a compression-molding process and is discussed in detail. For evaluating different fabrication approaches to determine the optimal one, partial discharge levels of built samples are measured and compared.

关键词： Fabrication Insulation Power cables Transportation Prototypes Nonhomogeneous media Power systems

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Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate Data

Implicit Neural Representations for Simultaneous Reduction a...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Alif Bin Abdul Qayyum Xihaier Luo Nathan M. Urban Xiaoning Qiari Byung-Iun Yoon Department of Electrical and Computer Engineering Texas A&M University Computational Science Initiative Brookhaven National Laboratory

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

The world is moving towards clean and renewable energy sources, such as wind energy, in an attempt to reduce green-house gas emissions that contribute to global warming. To enhance the analysis and storage of wind data, we introduce a deep learning framework designed to simultaneously enable effective dimensionality reduction and continuous representation of multi-altitude wind data from discrete observations. The framework consists of three key components: dimensionality reduction, cross-modal prediction, and super-resolution. We aim to: (1) improve data resolution across diverse climatic conditions to recover high-resolution details; (2) reduce data dimensionality for more efficient storage of large climate datasets; and (3) enable cross-prediction between wind data measured at different heights. Comprehensive testing confirms that our approach surpasses existing methods in both super-resolution quality and compression efficiency.

关键词： Dimensionality reduction Deep learning Wind energy generation Extrapolation Wind energy Wind speed Superresolution Wind farms Data models Testing Climate change Renewable energy sources

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A distributed algorithm with network-independent step-size and event-triggered mechanism for economic dispatch problem

A distributed algorithm with network-independent step-size a...

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作者： Chen, Baitong Yang, Jianhua Lu, Wei Pedrycz, Witold Sun, Changhai School of Control Science and Engineering Dalian University of Technology Dalian China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Institute of Systems Engineering Macau University of Science and Technology SARTaipa 999078 China Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey School of Electrical Engineering Dalian University of Technology Dalian China

The economic dispatch problem (EDP) poses a significant challenge in energy management for modern power systems, particularly as these systems undergo expansion. This growth escalates the demand for communication resources and increases the risk of communication failures. To address this challenge, we propose a distributed algorithm with network-independent step sizes and an event-triggered mechanism, which reduces communication requirements and enhances adaptability. Unlike traditional methods, our algorithm uses network-independent step sizes derived from each agent's local cost functions, thus eliminating the need for detailed network topology knowledge. The theoretical derivation identifies a range of step size values that depend solely on the objective function's strong convexity and the gradient's Lipschitz continuity. Furthermore, the proposed algorithm is shown to achieve a linear convergence rate, assuming the event triggering threshold criteria are met for linear convergence. Numerical experiments further validate the effectiveness and advantages of our proposed distributed algorithm by demonstrating its ability to maintain good convergence characteristics while reducing communication frequency. © 2024 John Wiley & Sons Ltd.

关键词： Cost functions

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The Deep Analysis of Accuracy of Performance Level of NBC Algorithm for Diagnosing BC 4

The Deep Analysis of Accuracy of Performance Level of NBC Al...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Valarmathi, P. Ramchander, M. Suneetha, Sampathirao Sasirekha, P. Mohammed, Saad Khudhur Hussain, Ahmed Mookambigai College of Engineering Department of Cse India Chaitanya Bharathi Institute of Technology Department of Mca Hyderabad India Andhra University College of Engineering Andhra University Department of Computer Science & Systems Engineering Andhra Pradesh India M.Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India National University of Science and Technology Collage of Pharmacy Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

These days, breast cancer is the foremost predominant cancer among ladies around the world and one of the driving causes of cancer-related mortality. In this ponder, an existing Observation, The study of disease transmission, and Conclusion Comes about (Diviner) dataset is utilized to assess the survival rate of breast cancer patients. Numerous of the profound and machine learning strategies accessible nowadays are so great that they can illuminate a wide course of categorization issues based on precise consider of large-scale datasets. These advances are broadly utilized in most biomedical investigate disciplines. Careful information planning, counting both investigation and visualization, offers pivotal bits of knowledge to support basic decision-making within the field. In this article, Soothsayer breast cancer information is classified utilizing machine learning based strategies. Furthermore, a two-step highlight choice strategy utilizing Foremost Component Examination and Change Edge is utilized to extricate pertinent highlights from the dataset. The dataset is classified utilizing Administered and Outfit learning approaches, counting Ada Boosting, XG Boosting, Incline Boosting, Naive Bayes varieties, and Choice Tree calculations, once the chosen highlights have been removed from the breast cancer dataset. The diverse machine learning techniques are totally overviewed with the utilize of train-test portion and k fold cross-validation. In specific, the Choice Tree calculation works exceptionally well, showing an outstandingly tall accuracy rate in both the train-test part and cross-validation procedures. Subsequently, for the Diviner Breast Cancer dataset, this inquire about illustrates the predominance of the choice Tree over the other elective approaches in Administered and Outfit learning procedures and gives a valuable knowledge into moving forward clinical choice and expectation. © 2024 IEEE.

关键词： Diseases

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HARNESSING OUT-OF-DISTRIBUTION EXAMPLES VIA AUGMENTING CONTENT AND STYLE 11

HARNESSING OUT-OF-DISTRIBUTION EXAMPLES VIA AUGMENTING CONTE...

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11th International Conference on Learning Representations, ICLR 2023

作者： Huang, Zhuo Xia, Xiaobo Shen, Li Han, Bo Gong, Mingming Gong, Chen Liu, Tongliang Syndey AI Centre The University of Sydney Australia JD Explore Academy Department of Computer Science Hong Kong Baptist University Hong Kong The University of Melbourne Australia Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China

Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples, and such a problem has drawn much attention. However, current methods lack a full understanding of different types of OOD data: there are benign OOD data that can be properly adapted to enhance the learning performance, while other malign OOD data would severely degenerate the classification result. To Harness OOD data, this paper proposes a HOOD method that can leverage the content and style from each image instance to identify benign and malign OOD data. Particularly, we design a variational inference framework to causally disentangle content and style features by constructing a structural causal model. Subsequently, we augment the content and style through an intervention process to produce malign and benign OOD data, respectively. The benign OOD data contain novel styles but hold our interested contents, and they can be leveraged to help train a style-invariant model. In contrast, the malign OOD data inherit unknown contents but carry familiar styles, by detecting them can improve model robustness against deceiving anomalies. Thanks to the proposed novel disentanglement and data augmentation techniques, HOOD can effectively deal with OOD examples in unknown and open environments, whose effectiveness is empirically validated in three typical OOD applications including OOD detection, open-set semi-supervised learning, and open-set domain adaptation. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Learning systems

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