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Overcoming entropic limitations on asymptotic state transformations through probabilistic protocols

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arXiv 2022年

作者： Regula, Bartosz Lami, Ludovico Wilde, Mark M. Department of Physics Graduate School of Science The University of Tokyo Bunkyo-ku Tokyo113-0033 Japan Institut für Theoretische Physik und IQST Universität Ulm Albert-Einstein-Allee 11 UlmD-89069 Germany School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States Hearne Institute for Theoretical Physics Department of Physics and Astronomy Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States

The quantum relative entropy is known to play a key role in determining the asymptotic convertibility of quantum states in general resource-theoretic settings, often constituting the unique monotone that is relevant in the asymptotic regime. We show that this is no longer the case when one allows stochastic protocols that may only succeed with some probability, in which case the quantum relative entropy is insufficient to characterize the rates of asymptotic state transformations, and a new entropic quantity based on a regularization of the Hilbert projective metric comes into play. Such a scenario is motivated by a setting where the cost associated with transformations of quantum states, typically taken to be the number of copies of a given state, is instead identified with the size of the quantum memory needed to realize the protocol. Our approach allows for constructing transformation protocols that achieve strictly higher rates than those imposed by the relative entropy. Focusing on the task of resource distillation, we give broadly applicable strong converse bounds on the asymptotic rates of probabilistic distillation protocols, and show them to be tight in relevant settings such as entanglement distillation with non-entangling operations. This generalizes and extends previously known limitations that only applied to deterministic protocols. Our methods are based on recent results for probabilistic one-shot transformations as well as a new asymptotic equipartition property for the projective relative entropy. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

Overview of Ancillary Services in the Nigerian Power Sector

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Overview of Ancillary Services in the Nigerian Power Sector

Multidisciplinary engineering and Applied Science (ICMEAS), International Conference on

作者： Bemdoo Saka Suleiman U. Hussein Biliaminu A. Ishola Yekini S. Mohammed Nanen D. Hembe Umar M. Mustapha Sadiq Thomas Dept. of Electrical/Electronics Eng. Nile University of Nigeria Abuja Nigeria Centre for Satellite Tech. Development Nat. Space Research and Dev. Agency Abuja Nigeria National Control Center Transmission Company of Nigeria Osogbo Abuja Dept. of Electrical/Electronics Eng. Federal Polytechnic Nasarawa Nasarawa Nigeria Department of Geography Nasarawa State University Nasarawa Nigeria Advanced UAV Laboratory Nat. Space Research and Dev. Agency Abuja Nigeria Dept. of Computer Engineering Nile University of Nigeria Abuja Nigeria

ISBN: (数字)9798350358834

ISBN: (纸本)9798350358841

An overview of the ancillary services in the Nigerian power sector is provided in this work. These services are essential in the power system because they are additional services required by the grid during critical times to enable the power grid to operate in a stable, safe, and reliable manner. The ancillary services provided as stipulated in the Grid Code are black start, frequency control, operating reserves, and voltage control. The System Operator procures the relevant ancillary service depending on the requirement of the grid at a particular time. The paper further presents some ways of improving the viability of ancillary services for the energy grid of Nigeria. These include making incentives more attractive for suppliers of ancillary services, utilizing more hydroelectric power plants for generation and load balancing, integrating distributed generation to provide support to the reactive power, and making necessary investments in the grid.

关键词： Codes Hydroelectric power generation Load management Power system reliability Reliability Voltage control Investment

Automated osteoporosis classification and T-score prediction using hip radiographs via deep learning algorithm

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Therapeutic Advances in Musculoskeletal Disease 2024年 16卷

作者： Chen, Yu-Pin Chan, Wing P. Zhang, Han-Wei Tsai, Zhi-Ren Peng, Hsiao-Ching Huang, Shu-Wei Jang, Yeu-Chai Kuo, Yi-Jie Department of Orthopedics Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Department of Radiology Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Biomedica Corporation New Taipei City Taiwan Program for Aging China Medical University Taichung City Taiwan Institute of Population Health Sciences National Health Research Institutes Miaoli County Taiwan Department of Electrical and Computer Engineering Institute of Electrical Control Engineering National Yang Ming Chiao Tung University Hsinchu County Hsinchu City Taiwan Department of Computer Science and Information Engineering Asia University Taichung City Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung City Taiwan Center for Precision Medicine Research Asia University Taichung City Taiwan Department of Applied Science National Taitung University Taitung County Taitung City Taiwan Department of Orthopedics Wan Fang Hospital Taipei Medical University No. 111 Sec. 3 Xinglong Road Wenshan Taipei 11696 Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

Background: Despite being the gold standard for diagnosing osteoporosis, dual-energy X-ray absorptiometry (DXA) is an underutilized screening tool for osteoporosis. Objectives: This study proposed and validated a controllable feature layer of a convolutional neural network (CNN) model with a preprocessing image algorithm to classify osteoporosis and predict T-score on the proximal hip region via simple hip radiographs. Design: This was a single-center, retrospective study. Methods: An image dataset of 3460 unilateral hip images from 1730 patients (age ⩾50 years) was retrospectively collected with matched DXA assessment for T-score for the targeted proximal hip regions to train (2473 unilateral hip images from 1430 patients) and test (497 unilateral hip images from 300 patients) the proposed CNN model. All images were processed with a fully automated CNN model, X1AI-Osteo. Results: The proposed screening tool illustrated a better performance (sensitivity: 97.2%;specificity: 95.6%;positive predictive value: 95.7%;negative predictive value: 97.1%;area under the curve: 0.96) than the open-sourced CNN models in predicting osteoporosis. Moreover, when combining variables, including age, body mass index, and sex as features in the training metric, there was high consistency in the T-score on the targeted hip regions between the proposed CNN model and the DXA (r = 0.996, p < 0.001). Conclusion: The proposed CNN model may identify osteoporosis and predict T-scores on the targeted hip regions from simple hip radiographs with high accuracy, highlighting the future application for population-based opportunistic osteoporosis screening with low cost and high adaptability for a broader population at risk. Trial registration: TMU-JIRB N201909036. © The Author(s), 2024.

关键词： deep learning neural network osteoporosis radiographs T-score

An Exoskeleton BCI System for Stroke Rehabilitation Using Multi-modality Training Mode

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An Exoskeleton BCI System for Stroke Rehabilitation Using Mu...

International Automatic control Conference

作者： Kai-Hsiang Su Chiao-Hsin Chen Chia-Hsin Chen Yong-Liang Zhang Li-Wei Ko Institute of Biological Science and Technology National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B) College of Biological Science and Technology National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Institute of Molecular Medicine and Bioengineering National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Rehabilitation Department of Kaohsiung Medical University Chung-Ho Memorial Hospital Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Department of Electrical and Computer Engineering Brain Research Center National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan

With the advent of the global aging era, the impact of stroke on humans is continuously increasing. Therefore, new therapeutic approaches to enhance patient recovery are receiving growing attention. Stroke is a severe neurological disorder that causes brain damage, requiring extended periods of rehabilitation for patients to return to normal life. Currently, common clinical methods largely rely on traditional passive limb rehabilitation, primarily targeting peripheral nerve training. Nowadays, an increasing number of people are recognizing the importance of post-stroke neuroplasticity. This study aims to activate patients' central nervous system and proposes a brain-machine interface (BCI) exoskeleton system based on human cognition. By designing three cognitive brainwave training models, including an attention-based standing-up model detecting θ-band power decrease, a relaxation-based sitting-down model detecting α-band power increase, and a walking model detecting MRCP, patients can actively control lower limb exoskeleton movement using *** study provides cross-validation accuracy through both normal subjects and stroke patients, with accuracies of 87.13±9.38 and 80.83±9.68, respectively. Envisioning a rehabilitation system guided by human cognition, our team has developed an intuitive BCI exoskeleton system.

关键词：

An Efficient and Robust Feature Selection Approach Based on Zentropy Measure and Neighborhood-Aware Model

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IEEE transactions on neural networks and learning systems 2025年 PP卷 PP页

作者： Kehua Yuan Duoqian Miao Hongyun Zhang Witold Pedrycz Tongji University School of Computer Science and Technology National Pilot Software Engineering School Shanghai 201804 China Silesian University of Technology (SUT) Department of Measurement and Control Systems Gliwice 44-100 Poland University of Alberta Department of Electrical and Computer Engineering Edmonton T6G 2R3 AB Canada Istinye University Research Center of Performance and Productivity Analysis Istanbul 21589 Turkey

The feature selection based on the rough set (RS) theory has been an active research topic in data mining and knowledge discovery. Fuzzy RSs (FRSs), an efficient tool to process the inconsistency between features and decisions, have attracted attention to the problems of feature selection. However, most FRSs-based feature selection methods pay much attention to the approximation space while ignoring the interaction between different levels. Note that the single-level feature selection method, depending on the boundary objects, is easily influenced by the noise data and cannot integrate multiple granular levels to evaluate features accurately. Therefore, this article proposes an efficient and robust feature selection approach based on the neighborhood-aware model and zentropy measure. Specifically, we first define a neighborhood-aware FRS (NAFRS) with weighted fuzzy relation to improve the antinoise ability of FRSs. Then, we propose a fuzzy granule zentropy (FGZE) measure based on zentropy by analyzing the granular level relation in NAFRS. Moreover, a significance measure with FGZE is designed and applied to feature selection. Finally, the experimental results of our method on 22 datasets by comparing it with 12 representative feature selection methods demonstrate the antinoise and the classification ability of the proposed method.

关键词： Feature selection fuzzy granule zentropy (FGZE) fuzzy rough set (FRS) granular computing uncertainty measure

Limitations of variational quantum algorithms: A quantum optimal transport approach

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arXiv 2022年

作者： De Palma, Giacomo Marvian, Milad Rouzé, Cambyse França, Daniel Stilck Department of Mathematics University of Bologna Bologna40126 Italy Department of Electrical & Computer Engineering Center for Quantum Information and Control University of New Mexico AlbuquerqueNM87131 United States Zentrum Mathematik Technische Universität München Garching85748 Germany QMATH Department of Mathematical Sciences University of Copenhagen Universitetsparken 5 Copenhagen2100 Denmark Univ Lyon ENS Lyon UCBL CNRS Inria LIP LyonF-69342 Cedex 07 France

The impressive progress in quantum hardware of the last years has raised the interest of the quantum computing community in harvesting the computational power of such devices. However, in the absence of error correction, these devices can only reliably implement very shallow circuits or comparatively deeper circuits at the expense of a nontrivial density of errors. In this work, we obtain extremely tight limitation bounds for standard NISQ proposals in both the noisy and noiseless regimes, with or without error-mitigation tools. The bounds limit the performance of both circuit model algorithms, such as QAOA, and also continuous-time algorithms, such as quantum annealing. In the noisy regime with local depolarizing noise p, we prove that at depths L = O(p−1) it is exponentially unlikely that the outcome of a noisy quantum circuit outperforms efficient classical algorithms for combinatorial optimization problems like Max-Cut. Although previous results already showed that classical algorithms outperform noisy quantum circuits at constant depth, these results only held for the expectation value of the output. Our results are based on newly developed quantum entropic and concentration inequalities, which constitute a homogeneous toolkit of theoretical methods from the quantum theory of optimal mass transport whose potential usefulness goes beyond the study of variational quantum algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Error correction

Stabilizing two-qubit entanglement with dynamically decoupled active feedback

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arXiv 2023年

作者： Greenfield, Sacha Martin, Leigh Motzoi, Felix Birgitta Whaley, K. Dressel, Justin Levenson-Falk, Eli M. Department of Physics and Astronomy University of Southern California Los AngelesCA90089 United States Center for Quantum Information Science and Technology University of Southern California Los AngelesCA90089 United States Institute for Quantum Studies Chapman University OrangeCA92866 United States Berkeley Center for Quantum Information and Computation BerkeleyCA94720 United States Department of Physics University of California BerkeleyCA94720 United States Department of Chemistry University of California BerkeleyCA94720 United States Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße Jülich52428 Germany Schmid College of Science and Technology Chapman University OrangeCA92866 United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States

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, non-commuting 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. © 2023, CC BY.

关键词： Quantum entanglement

Prediction of fitness in bacteria with causal jump dynamic mode decomposition

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arXiv 2020年

作者： Balakrishnan, Shara Hasnain, Aqib Boddupalli, Nibodh Joshy, Dennis M. Egbert, Robert G. Yeung, Enoch Department of Electrical and Computer Engineering University of California Santa Barbara United States Department of Mechanical Engineering University of California Santa Barbara United States Environmental and Biological Sciences Directorate Pacific Northwest National Laboratory RichlandWA United States Department of Mechanical Engineering Center for Control Dynamical Systems and Computation Biomolecular Science and Engineering University of California Santa Barbara United States

— In this paper, we consider the problem of learning a predictive model for population cell growth dynamics as a function of the media conditions. We first introduce a generic data-driven framework for training operator-theoretic models to predict cell growth rate. We then introduce the experimental design and data generated in this study, namely growth curves of Pseudomonas putida as a function of casein and glucose concentrations. We use a data driven approach for model identification, specifically the nonlinear autoregressive (NAR) model to represent the dynamics. We show theoretically that Hankel DMD can be used to obtain a solution of the NAR model. We show that it identifies a constrained NAR model and to obtain a more general solution, we define a causal state space system using 1-step, 2-step,..., τ-step predictors of the NAR model and identify a Koopman operator for this model using extended dynamic mode decomposition. The hybrid scheme we call causal-jump dynamic mode decomposition, which we illustrate on a growth profile or fitness prediction challenge as a function of different input growth conditions. We show that our model is able to recapitulate training growth curve data with 96.6% accuracy and predict test growth curve data with 91% accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： Bacteria

Traceable random numbers from a nonlocal quantum advantage

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arXiv 2024年

作者： Kavuri, Gautam A. Palfree, Jasper Reddy, Dileep V. Zhang, Yanbao Bienfang, Joshua C. Mazurek, Michael D. Alhejji, Mohammad A. Siddiqui, Aliza U. Cavanagh, Joseph M. Dalal, Aagam Abellán, Carlos Amaya, Waldimar Mitchell, Morgan W. Stange, Katherine E. Beale, Paul D. Brandão, Luís T.A.N. Booth, Harold Peralta, René Nam, Sae Woo Mirin, Richard P. Stevens, Martin J. Knill, Emanuel Shalm, Lynden K. Department of Physics University of Colorado BoulderCO80309 United States The National Institute of Standards and Technology BoulderCO80305 United States Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Joint Quantum Institute National Institute of Standards and Technology University of Maryland 100 Bureau Drive GaithersburgMD20899 United States Center for Quantum Information and Control University of New Mexico AlbuquerqueNM87131 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Quside Technologies S.L. Castelldefels Barcelona Spain ICFO-Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Barcelona08860 Spain ICREA - Institució Catalana de Recerca i Estudis Avançats Barcelona08010 Spain Department of Mathematics University of Colorado BoulderCO80309 United States GaithersburgMD20899 United States Information Technology Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Physical Measurement Laboratory National Institute of Standards and Technology BoulderCO80305 United States Center for Theory of Quantum Matter University of Colorado BoulderCO80305 United States Applied and Computational Mathematics Division National Institute of Standards and Technology BoulderCO80305 United States Quantum Engineering Initiative Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States

The unpredictability of random numbers is fundamental to both digital security [1, 2] and applications that fairly distribute resources [3, 4]. However, existing random number generators have limitations—the generation processes cannot be fully traced, audited, and certified to be unpredictable. The algorithmic steps used in pseudorandom number generators [5] are auditable, but they cannot guarantee that their outputs were a priori unpredictable given knowledge of the initial seed. Device-independent quantum random number generators [6–9] can ensure that the source of randomness was unknown beforehand, but the steps used to extract the randomness are vulnerable to tampering. Here, for the first time, we demonstrate a fully traceable random number generation protocol based on device-independent techniques. Our protocol extracts randomness from unpredictable non-local quantum correlations, and uses distributed intertwined hash chains to cryptographically trace and verify the extraction process. This protocol is at the heart of a public traceable and certifiable quantum randomness beacon that we have launched [10]. Over the first 40 days of operation, we completed the protocol 7434 out of 7454 attempts—a success rate of 99.7%. Each time the protocol succeeded, the beacon emitted a pulse of 512 bits of traceable randomness. The bits are certified to be uniform with error times actual success probability bounded by 2−64. The generation of certifiable and traceable randomness represents one of the first public services that operates with an entanglement-derived advantage over comparable classical approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Random number generation