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作者： Ramakrishnan, Navneeth Tomamichel, Marco Berta, Mario Imperial College London Department of Computing LondonSW7 2BX United Kingdom National University of Singapore Centre for Quantum Technologies Department of Electrical and Computer Engineering Queenstown119077 Singapore Institute for Quantum Information RWTH Aachen University Aachen52056 Germany

The moderate deviation regime is concerned with the finite block length trade-off between communication cost and error for information processing tasks in the asymptotic regime, where the communication cost approaches a capacity-like quantity and the error vanishes at the same time. We find exact characterisations of these trade-offs for a variety of fully quantum communication tasks, including quantum source coding, quantum state splitting, entanglement-assisted quantum channel coding, and entanglement-assisted quantum channel simulation. The main technical tool we derive is a tight relation between the partially smoothed max-information and the hypothesis testing relative entropy. This allows us to obtain the expansion of the partially smoothed max-information for i.i.d. states in the moderate deviation regime. © 1963-2012 IEEE.

关键词： quantum entanglement

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Evaluating the Effectiveness of Multi-Factor Authentication for Preventing Cyber Attacks 15

Evaluating the Effectiveness of Multi-Factor Authentication ...

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

作者： Venkatasubramanian, D. Goyal, Saumya Ezhilarasan, G. Sharma, Yogesh K. Vijayalakshmi, V.J. Garg, Prateek Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Electrical and Electronics Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140417 India

ISBN: (纸本)9798350370249

this article affords an outline of the effectiveness of multi-thing authentication (MFA) for stopping cyber attacks. MFA is an authentication procedure that combines or greater elements or traits to verify the identification of a character or user earlier than granting them access. This sort of authentication is increasingly being used to protect non-public data and to prevent unauthorized get admission to systems and services. Diverse studies have been carried out to evaluate the effectiveness of MFA as compared to traditional authentication methods, like passwords. The outcomes of these studies show that after properly implemented, MFA may be a powerful method to shield towards cyber-assaults. This article also presents satisfactory practices for implementing MFA to maximize its effectiveness and discusses risks related to using MFA. © 2024 IEEE.

关键词： Authentication Character Diverse Effectiveness Elements Identification Presents Protect

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Progress Towards quantum-Enhanced Microwave Remote Sensing and Communication Applications

Progress Towards Quantum-Enhanced Microwave Remote Sensing a...

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US National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM)

作者： Jérôme Bourassa Christopher M. Wilson Qubic Inc. Sherbrooke QC CANADA Institute for Quantum Computing & Department of Electrical and Computer Engineering University of Waterloo ON CANADA

RADAR and communications are vulnerable as noise and interference contaminate the received signals and produce errors limiting their resolution, their accuracy and speed. To answer the growing needs for more reliable and accurate microwave intelligence in an ever more complex electromagnetic environment, there is a growing need to develop innovative microwave sensors with greater signal sensitivity, selectivity and integrity.

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High-yield and rapid isolation of extracellular vesicles by flocculation via orbital acoustic trapping:FLOAT

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Microsystems & Nanoengineering 2024年第1期10卷 313-323页

作者： Joseph Rufo Peiran Zhang Zeyu Wang Yuyang Gu Kaichun Yang Joseph Rich Chuyi Chen Ruoyu Zhong Ke Jin Ye He Jianping Xia Ke Li Jiarong Wu Yingshi Ouyang Yoel Sadovsky Luke P.Lee Tony Jun Huang Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNCUSA Department of Biomedical Engineering Duke UniversityDurhamNCUSA Department of Obstetrics Gynecologyand Reproductive SciencesMagee-Womens Research InstituteUniversity of PittsburghPittsburghPAUSA Department of Microbiology and Molecular Genetics School of MedicineUniversity of PittsburghPittsburghPAUSA Renal Division and Division of Engineering in Medicine Department of MedicineBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA Department of Bioengineering Department of Electrical Engineering and Computer ScienceUniversity of CaliforniaBerkeleyBerkeleyCAUSA Department of Biophysics Institute of Quantum BiophysicsSungkyunkwan UniversitySuwonKorea

Extracellular vesicles(EVs)have been identified as promising biomarkers for the noninvasive diagnosis of various ***,challenges in separating EVs from soluble proteins have resulted in variable EV recovery rates and low ***,we report a high-yield(>90%)and rapid(<10 min)EV isolation method called FLocculation via Orbital Acoustic Trapping(FLOAT).The FLOAT approach utilizes an acoustofluidic droplet centrifuge to rotate and controllably heat liquid *** adding a thermoresponsive polymer flocculant,nanoparticles as small as 20 nm can be rapidly and selectively concentrated at the center of the *** demonstrate the ability of FLOAT to separate urinary EVs from the highly abundant Tamm-Horsfall protein,addressing a significant obstacle in the development of EV-based liquid *** to its high-yield nature,FLOAT reduces biofluid starting volume requirements by a factor of 100(from 20 mL to 200µL),demonstrating its promising potential in point-of-care diagnostics.

关键词： trapping utilize separating

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High-Efficiency Asymmetrically Designed Three-Phase Virtual 48-pulse Power Supply for Electrolytic Hydrogen

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IEEE Transactions on Power Electronics 2025年第8期40卷 10335-10342页

作者： Tian, Hanlei Cui, Haoran Han, Wei Sathik M, Jagabar Liang, Guozhuang Chen, Maolin Mekhilef, Saad Sustainable Energy and Environment Thrust The Hong Kong University of Science and Technology (GZ) Guangzhou China Electrical Engineering School of Hebei University of Science and Technology Shijiazhuang China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India College of Electrical Engineering Sichuan University Chengdu China School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne VIC Australia

To address the low-voltage, high-current requirements in hydrogen production applications, a virtual 48-pulse three-phase rectifier is proposed, and achieves the equivalent performance of four parallel 12-pulse rectifiers (12-PR). The proposed approach builds upon the conventional 12-PR design and incorporates novel Asymmetric Current Injection Units (ACIU). The ACIU is installed on the DC side of the AC-DC converter, generating a specific current at the DC link to modulate and enhance the operating modes of the 12-PR. Consequently, the 12-pulse is extended to 48, resulting in an almost sinusoidal input current with a Total Harmonic Distortion (THD) of 3.2% and an output current with a ripple rate (RR) of 2.3%, thus significantly improving power quality. Furthermore, the asymmetric design of the current injection units eliminates the need for a phase-shifting transformer. The efficacy of the proposed rectifier for electrolytic hydrogen production is validated using a 2.2 kW experimental prototype. © 1986-2012 IEEE.

关键词： Voltage Hydrogen Rectifiers Production Windings Circuits Inductors Transformers Harmonic analysis Electrochemical processes

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Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors 38

Principled Probabilistic Imaging using Diffusion Models as P...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wu, Zihui Sun, Yu Chen, Yifan Zhang, Bingliang Yue, Yisong Bouman, Katherine L. Department of Computing and Mathematical Sciences Caltech United States Department of Electrical Engineering Caltech United States Department of Astronomy Caltech United States Department of Electrical and Computer Engineering Johns Hopkins University United States Courant Institute of Mathematical Sciences New York University United States

Diffusion models (DMs) have recently shown outstanding capabilities in modeling complex image distributions, making them expressive image priors for solving Bayesian inverse problems. However, most existing DM-based methods rely on approximations in the generative process to be generic to different inverse problems, leading to inaccurate sample distributions that deviate from the target posterior defined within the Bayesian framework. To harness the generative power of DMs while avoiding such approximations, we propose a Markov chain Monte Carlo algorithm that performs posterior sampling for general inverse problems by reducing it to sampling the posterior of a Gaussian denoising problem. Crucially, we leverage a general DM formulation as a unified interface that allows for rigorously solving the denoising problem with a range of state-of-the-art DMs. We demonstrate the effectiveness of the proposed method on six inverse problems (three linear and three nonlinear), including a real-world black hole imaging problem. Experimental results indicate that our proposed method offers more accurate reconstructions and posterior estimation compared to existing DM-based imaging inverse methods. © 2024 Neural information processing systems foundation. All rights reserved.

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Learning to Predict Arbitrary quantum Processes

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PRX quantum 2023年第4期4卷 040337-040337页

作者： Hsin-Yuan Huang Sitan Chen John Preskill Institute for Quantum Information and Matter and Department of Computing and Mathematical Sciences Caltech Pasadena California USA Department of Electrical Engineering and Computer Sciences UC Berkeley Berkeley California USA AWS Center for Quantum Computing Pasadena California USA

We present an efficient machine-learning (ML) algorithm for predicting any unknown quantum process E over n qubits. For a wide range of distributions D on arbitrary n-qubit states, we show that this ML algorithm can learn to predict any local property of the output from the unknown process E, with a small average error over input states drawn from D. The ML algorithm is computationally efficient even when the unknown process is a quantum circuit with exponentially many gates. Our algorithm combines efficient procedures for learning properties of an unknown state and for learning a low-degree approximation to an unknown observable. The analysis hinges on proving new norm inequalities, including a quantum analogue of the classical Bohnenblust-Hille inequality, which we derive by giving an improved algorithm for optimizing local Hamiltonians. Numerical experiments on predicting quantum dynamics with evolution time up to 106 and system size up to 50 qubits corroborate our proof. Overall, our results highlight the potential for ML models to predict the output of complex quantum dynamics much faster than the time needed to run the process itself.

关键词： quantum computation quantum information processing quantum information theory

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Joint Communication and Synchronization Performance Optimization in Digital Twin Enabled Networks

Joint Communication and Synchronization Performance Optimiza...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yu, Hanzhi Liu, Yuchen Chen, Mingzhe University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States

ISBN: (纸本)9798350351255

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT) that is a virtual representation of the physical network. The considered network includes a physical network where a base station (BS) serves a set of users, and a DNT that evolves with the status of both DNT and the physical network. The BS must use its limited spectrum resources to serve the users, as well as transmit the physical network information to the cloud server for DNT synchronization. Since the DNT can predict the physical network status, the BS may not need to transmit physical network information to the server at each time slot thus saving spectrum resources to serve users. However, if the BS does not transmit physical information to the DNT over a long period of time, the DNT may not be able to represent the physical network accurately. To this end, the BS must determine whether to send physical network information to the server to update DNT and the spectrum resources used for physical network information transmission and serving users. We formulate this resources allocation problem as an optimization problem aiming to maximize the sum of data rates of all users, while minimizing the gap between the states of the physical network and the DNT. The formulated problem is challenging to solve by conventional optimization methods, since the BS may not be able to know the future status of the DNT. To solve this problem, we design a gate recurrent unit (GRU) and soft action-critic (SAC) based algorithm. The GRU enables the DNT to predict its future states by using historical state data, and updating the DNT when the BS does not transmit physical network information. The SAC based algorithm enables the BS to learn the relationship between the physical network information transmission and the future status estimation accuracy of the DNT thus determining whether to transmit physical network information to the cloud server, ensuring an accurac

关键词： Resource allocation

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Predicting Cervical Cancer Using Deep Learning Mutation-based Atom Search Optimization (MASO) Algorithm 2

Predicting Cervical Cancer Using Deep Learning Mutation-base...

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2nd IEEE International Conference on Trends in quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Mayuri, A.V.R. Selvekumar Subash, K. Aeron, Anurag Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Sehore India Bahir Dar University Bahir Dar Institute of Technology Department of Electrical and Computer Engineering Ethiopia Department of Data Science Trichy India Miet Meerut Department of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350384277

Worldwide, women are compressed by cervical cancer, which is a prevalent malignancy. This disease, which is currently the fourth leading cause of death for women, shows no symptoms when it first arises. Cells that cause cervical cancer multiply gradually at the cervix. If this cancer is detected early enough, treatment can be effective. Presently, it is difficult for medical workers to detect this kind of cancer before it spreads rapidly. This study used a number of machine learning classification algorithms with risk markers to predict cervical cancer. These study suggestions mutation-based Atom Search Optimization (MASO) with a Deep Convolutional Neural Network (DCNN) to offer a original method for automated diagnosis of uterine cervical cancer. Though DCNN is used to extract features from medical imaging data, MASO is used to increase the optimization of the diagnostic model. Composed, these technologies have the potential to rise cervical cancer diagnosis accuracy and efficiency, offering an automated approach for early detection and prompt intervention. Our proposed model established a strong 95% accuracy rate for generalization. This study explores the difficulties caused by missing values and class imbalance in the specific dataset with the goal of assisting medical professionals in the early detection of cervical cancer and improving treatment for people that are impacted by the illness. © 2024 IEEE.

关键词： Convolutional neural networks

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Spontaneous strong symmetry breaking in open systems: Purification perspective

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Physical Review B 2024年第15期110卷 155150页

作者： Pablo Sala Sarang Gopalakrishnan Masaki Oshikawa Yizhi You Department of Physics and Institute for Quantum Information and Matter Walter Burke Institute for Theoretical Physics Department of Electrical and Computer Engineering Institute for Solid State Physics Department of Physics

We explore the effect of decoherence on many-body mixed states from a purification perspective. Here, quantum channels map to unitary transformations acting on a purified state defined within an extended Hilbert space. First, we exploit this approach to analyze the phenomenon of spontaneous strong-to-weak symmetry breaking (sw-SSB) triggered by strongly symmetric local quantum channels. We find that sw-SSB for mixed states relates to proximity to a symmetry-protected topological (SPT) order in the purified state. Remarkably, the measurement-induced long-range order in the purified SPT state, as characterized by the Rényi-2 correlator, mirrors the ensuing long-range order in the mixed state due to sw-SSB. Moreover, we establish a 1-to-1 correspondence between various sw-SSB order parameters for the mixed state, and strange correlators in the purification, which signify the SPT order. This purification perspective is further extended to explore intrinsic mixed-state topological order and decoherent symmetry-protected topological phases.

关键词： Open quantum systems & decoherence quantum correlations in quantum information Topological phases of matter Symmetries in condensed matter

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