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Quantum algorithm to simulate Lindblad master equations

Physical Review Research 2025年第2期7卷 023076-023076页

作者： Evan Borras Milad Marvian Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131 USA Department of Physics and Astronomy University of New Mexico Albuquerque New Mexico 87131 USA Department of Electrical & Computer Engineering University of New Mexico Albuquerque New Mexico 87131 USA

We present a quantum algorithm for simulating a family of Markovian master equations that can be realized through a probabilistic application of unitary channels and state preparation. Our approach employs a second-order product formula for the Lindblad master equation, achieved by decomposing the dynamics into dissipative and Hamiltonian components and replacing the dissipative segments with randomly compiled, easily implementable elements. The sampling approach eliminates the need for ancillary qubits to simulate the dissipation process and reduces the gate complexity in terms of the number of jump operators. We provide a rigorous performance analysis of the algorithm. We also extend the algorithm to time-dependent Lindblad equations, generalize the family of Markovian master equations it can be applied to, and explore applications beyond the Markovian noise model. A new error bound, in terms of the diamond norm, for second-order product formulas for time-dependent Liouvillians is provided that might be of independent interest.

关键词： Hamiltonians

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Symmetry in linear physical systems

arXiv

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arXiv 2025年

作者： van der Schaft, Arjan Sepulchre, Rodolphe Chaffey, Thomas Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Jan C. Willems Center for Systems and Control University of Groningen PO Box 407 9700 AK Netherlands KU Leuven Belgium University of Cambridge United Kingdom School of Electrical and Computer Engineering University of Sydney Australia

Physical systems with symmetry arise abundantly in applications, and are endowed with interesting mathematical structures. The present paper focusses on linear reciprocal and input-output Hamiltonian systems. Their characterization is studied from an input-output as well as from a state point of view. Geometrically, it turns out that they both define Lagrangian subspaces with corresponding generating functionals. Furthermore, the relations with time reversibility are analyzed. The system classes under consideration are expected to admit scalable control laws, and to be important building blocks in design. Copyright © 2025, The Authors. All rights reserved.

关键词： Hamiltonians

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Fundamental design tradeoffs in filtering, prediction, and smoothing

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IEEE TRANSACTIONS ON AUTOMATIC control 1997年第9期42卷 1240-1251页

作者： Goodwin, GC Seron, MM Centre for Integrated Dynamics and Control Department of Electrical and Computer Engineering University of Newcastle NSW Australia Center for Control Engineering and Computation University of California Santa Barbara CA USA

This paper examines fundamental design tradeoffs that apply to all linear filtering, prediction, and smoothing problems, We introduce sensitivity functions that quantify filter performance, and we show that their frequency response satisfies constraints imposed by unstable poles and nonminimum phase zeros of the system, The constraints allow one to determine, a priori, whether or not a desired filter performance is attainable and how different arrangements of the measurement system influence the achievable performance.

关键词： Bode limits linear filtering performance limits

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Switching Multiplicative Watermark Design Against Covert Attacks

arXiv

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arXiv 2025年

作者： Gallo, Alexander J. Anand, Sribalaji C. Teixeira, Andre M.H. Ferrari, Riccardo M.G. Department of Electronics Information and Bioengineering Politecnico di Milano Milano Italy School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Sweden Department of Information Technology Uppsala University PO Box 337 UppsalaSE-75105 Sweden Delft Center for Systems and Control Mechanical Engineering TU Delft Delft Netherlands

Active techniques have been introduced to give better detectability performance for cyber-attack diagnosis in cyber-physical systems (CPS). In this paper, switching multiplicative watermarking is considered, whereby we propose an optimal design strategy to define switching filter parameters. Optimality is evaluated exploiting the so-called output-to-output gain of the closed loop system, including some supposed attack dynamics. A worst-case scenario of a matched covert attack is assumed, presuming that an attacker with full knowledge of the closed-loop system injects a stealthy attack of bounded energy. Our algorithm, given watermark filter parameters at some time instant, provides optimal next-step parameters. Analysis of the algorithm is given, demonstrating its features, and demonstrating that through initialization of certain parameters outside of the algorithm, the parameters of the multiplicative watermarking can be randomized. Simulation shows how, by adopting our method for parameter design, the attacker’s impact on performance diminishes. © 2025, CC BY-NC-ND.

关键词： Networked control systems

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Extended Dissipativity Analysis and Synthesis for Discrete-Time Lur'e Delayed Systems: Matrix-Separation-Based Lyapunov Functional Method

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IET control Theory and Applications 2025年第1期19卷

作者： Xie, Ke-You Lee, Sangmoon Zhang, Chuan-Ke Jin, Li Liu, Yajuan He, Yong School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Electronic and Electrical Engineering Kyungpook National University Daegu Korea Republic of School of Control and Computer Engineering North China Electric Power University Beijing China

This paper develops a matrix-separation-based Lyapunov functional method to study the extended dissipativity analysis and synthesis issue of discrete-time Lur'e-type delayed systems. The advanced idea of matrix-separation is reflected in Lyapunov functional candidates and estimates summation terms as precisely as feasible. First, we introduce a novel summation inequality based on the matrix-separation method to provide a bound for the augmented summation that involves common state variables. An improved delay-product-type Lyapunov functional is devised by extending non-positive definite summations as separate subblocks of the state-augmented summation. Then, by using the matrix injection method to handle the cubic delay term in the functional forward difference, a delay-variation-dependent stability criterion and an extended dissipativity criterion are developed under the matrix-separation-based method. Subsequently, sufficient conditions for the control design of Lur'e-type systems are derived. Finally, the effectiveness and superiority of the proposed method are verified through its application to Chua's circuits, neural networks, and a stochastic numerical example. © 2025 The Author(s). IET control Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Stability criteria

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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3D printing of self-powered triboelectric intelligent system with enhanced output performance for material identification via machine learning

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Chemical engineering Journal 2025年 515卷

作者： Huang, Bingyu Xu, Kailong Zhang, Xiaofei Zhang, Yongcheng Long, Yun-Ze Ge, Shuzhi Sam Wei, Xiangxia School of Automation Shandong Key Laboratory of Industrial Control Technology Qingdao University Qingdao266071 China College of Materials Science and Engineering Qingdao University Qingdao266071 China Collaborative Innovation Center for Nanomaterials & Devices College of Physics Qingdao University Qingdao266071 China College of Physics Qingdao University Qingdao266071 China Department of Electrical & Computer Engineering National University of Singapore 117576 Singapore

Triboelectric nanogenerators (TENGs) have attracted global attention for energy conversion from various types of mechanical energy into electricity. Traditional approaches for improving triboelectric conversion efficiency generally involved complicated preparation methods and strict structural design, which cannot fulfill the stringent requirements of material adaptability and application scenarios. In this work, a flexible TENG was developed based on zirconium-doped barium titanate (BaZrxTi1−xO3, BZT) particles in polydimethylsiloxane (PDMS) matrix. Subsequently, BZT/PDMS composites with patterned structures were fabricated by direct ink writing (DIW) based three-dimensional (3D) printing to significantly enhance the electrical performance. It was demonstrated that the TENG could deliver a maximum open-circuit voltage (VOC) of 350 V and a short-circuit current (ISC) of 4.9 µA with a maximum power density of 2.4 W m−2. Moreover, an effective utilization of the TENG was designed to detect the presence of humans, saving a huge amount of electrical energy. Finally, a TENG-based tactile sensor was integrated with machine learning to construct a material identification system, which was capable of recognizing different materials with a high accuracy of 98.54 %. The TENG-based tactile sensor with machine learning demonstrated a tremendous application in material perception, promoting the development of versatile sensors in the field of machine intelligence. © 2025 Elsevier B.V.

关键词： Liquid crystals

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Sensing With Random Communication Signals

引用

IEEE Network 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing210044 China Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin10587 Germany Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security China Southeast University MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing211189 China

Communication-centric Integrated Sensing and Communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025 IEEE.

关键词： Mobile telecommunication systems

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention 26

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

引用

26th International Conference on Industrial Technology, ICIT 2025

作者： Bao, Chengao Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Tokyo Institute of Technology Yokohama226-8502 Japan Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798331521950

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method. © 2025 IEEE.

关键词： Modal analysis

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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