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Entanglement-enhanced ac magnetometry in the presence of Markovian noise

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Physical Review A 2025年第4期111卷 042605-042605页

作者： Thanaporn Sichanugrist Hajime Fukuda Takeo Moroi Kazunori Nakayama So Chigusa Norikazu Mizuochi Masashi Hazumi Yuichiro Matsuzaki Department of Physics The University of Tokyo Tokyo 113-0033 Japan International Center for Quantum-field Measurement Systems for Studies of the Universe and Particles (QUP) High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba Ibaraki 305-0801 Japan Department of Physics Tohoku University Sendai Miyagi 980-8578 Japan Theoretical Physics Group Lawrence Berkeley National Laboratory Berkeley California 94720 USA Berkeley Center for Theoretical Physics Department of Physics University of California Berkeley California 94720 USA Center for Theoretical Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Institute for Chemical Research Kyoto University Gokasho Uji-city Kyoto 611-0011 Japan Institute of Particle and Nuclear Studies (IPNS) KEK Tsukuba Ibaraki 305-0801 Japan Japan Aerospace Exploration Agency (JAXA) Institute of Space and Astronautical Science (ISAS) Sagamihara Kanagawa 252-5210 Japan Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU WPI) UTIAS The University of Tokyo Kashiwa Chiba 277-8583 Japan The Graduate University for Advanced Studies (SOKENDAI) Miura District Kanagawa 240-0115 Hayama Japan Department of Electrical Electronic and Communication Engineering Faculty of Science and Engineering Chuo University 1-13-27 Kasuga Bunkyo-ku Tokyo 112-8551 Japan

Entanglement is a resource to improve the sensitivity of quantum sensors. In an ideal case, using an entangled state as a probe to detect target fields, we can beat the standard quantum limit by which all classical sensors are bounded. However, since entanglement is fragile against decoherence, it is unclear whether entanglement-enhanced metrology is useful in a noisy environment. Its benefit is indeed limited when estimating the amplitude of dc magnetic fields under the effect of parallel Markovian decoherence, where the noise operator is parallel to the target field. In this paper, on the contrary, we show an advantage to using an entanglement over the classical strategy under the effect of parallel Markovian decoherence when we try to detect ac magnetic fields. We consider a scenario to induce a Rabi oscillation of the qubits with the target ac magnetic fields. Although we can, in principle, estimate the amplitude of the ac magnetic fields from the Rabi oscillation, the signal becomes weak if the qubit frequency is significantly detuned from the frequency of the ac magnetic field. We show that, by using the Greenberger-Horne-Zeilinger (GHZ) states, we can significantly enhance the signal of the detuned Rabi oscillation even under the effect of parallel Markovian decoherence. Our method is based on the fact that the interaction time between the GHZ states and ac magnetic fields scales as 1/L to mitigate the decoherence effect, where L is the number of qubits, which contributes to improving the bandwidth of the detectable frequencies of the ac magnetic fields. Our results pave the way for new applications of entanglement-enhanced ac magnetometry.

关键词： Qubits

On the regularization and optimization in quantum detector tomography

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arXiv 2022年

作者： Xiao, Shuixin Wang, Yuanlong Zhang, Jun Dong, Daoyi Yokoyama, Shota Petersen, Ian R. Yonezawa, Hidehiro University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Centre for Quantum Computation and Communication Technology Australian Research Council Centre for Quantum Dynamics Griffith University BrisbaneQLD4111 Australia Centre for Quantum Computation and Communication Technology Australian Research Council CanberraACT2600 Australia School of Engineering Australian National University CanberraACT2601 Australia

Quantum detector tomography (QDT) is a fundamental technique for calibrating quantum devices and performing quantum engineering tasks. In this paper, we utilize regularization to improve the QDT accuracy whenever the probe states are informationally complete or informationally incomplete. In the informationally complete scenario, without regularization, we optimize the resource (probe state) distribution by converting it to a semidefinite programming problem. Then in both the informationally complete and informationally incomplete scenarios, we discuss different regularization forms and prove the mean squared error scales as O(1/N) or tends to a constant with N state copies under the static assumption. We also characterize the ideal best regularization for the identifiable parameters, accounting for both the informationally complete and informationally incomplete scenarios. Numerical examples demonstrate the effectiveness of different regularization forms and a quantum optical experiment test shows that a suitable regularization form can reach a reduced mean squared error. Copyright © 2022, The Authors. All rights reserved.

关键词： Tomography

Recursive Weighted Null-Space Fitting Method for Identification of Multivariate Systems ⁎

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IFAC-PapersOnLine 2021年第7期54卷 345-350页

作者： Mengyuan Fang Miguel Galrinho Håkan Hjalmarsson Zhejiang Engineering Laboratory of Industrial Internet Communication Technology Zhejiang Sci-Tech University Hangzhou China. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China OHB Sweden AB Kista Sweden Centre for Advanced Bio Production and Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

Recursive identification of structured multivariate models is known to be difficult due to the general non-convexity of the likelihood function. In this work, we propose a recursive multivariate weighted null-space fitting method for identification of structured multivariate models. The proposed method first uses recursive least squares to estimate a high order non-parametric model, then a parametric model is obtained through weighted least squares from the non-parametric model. In this way, the method avoids directly optimizing a non-convex likelihood function and has guaranteed global convergency. Moreover, the proposed method is flexible in model structures and has the same finite sample performance as its off-line counterpart. We use simulation examples to illustrate the performance.

关键词： Recursive system identification multivariate systems weighted null-space fitting

Sensing With Random communication Signals

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arXiv 2025年

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

关键词： Signaling

Two-stage solution of quantum process tomography in the natural basis

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Two-stage solution of quantum process tomography in the natu...

IEEE Conference on Decision and control

作者： Shuixin Xiao Yuanlong Wang Daoyi Dong Jun Zhang School of Engineering and Information Technology University of New South Wales Canberra ACT Australia UMich-SJTU Joint Institute Shanghai Jiao Tong University Shanghai China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Centre for Quantum Computation and Communication Technology (Australian Research Council) Centre for Quantum Dynamics Griffith University Brisbane Queensland Australia

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Quantum process tomography (QPT) is a critical task for characterizing the dynamics of quantum systems and achieving precise quantum control. In this paper, we propose an effective two-stage solution (TSS) for QPT in the natural basis. Our algorithm has O(d 6 ) computational complexity where d is the dimension of the quantum system. We establish an error bound $O\left( {\frac{{{d^5}}}{{\sqrt N }}} \right)$ where N is the copy number for each output state. The numerical examples compared with a convex optimization algorithm demonstrate the effectiveness of our algorithm.

关键词： Quantum system Error analysis Heuristic algorithms Process control Tomography Quantum state Convex functions

Adaptive neural network output-feedback tracking control for switched nonlinear ship maneuvering systems with time delays under arbitrary switching

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Adaptive neural network output-feedback tracking control for...

Chinese Automation Congress (CAC)

作者： Zhenhua Li Xiangxuan Ren Botao Dong Hong Chen Weidong Zhang SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya P. R. China Department of Automation Shanghai Jiao Tong University Shanghai P. R. China College of Mechatronics and Control Engineering Shenzhen University Shenzhen P. R. China School of Information and Communication Engineering Hainan University Haikou P. R. China

This paper focuses on the tracking control problem for switched nonlinear ship maneuvering time-delay systems with only a heading angle available by adaptive neural network (NN) output feedback. Using the backstepping method, an adaptive NN control mechanism is designed to solve the problem cooperated with the state observer. The uncertain terms of the system are approximated by NNs, and the state observer is designed to estimate the yaw rate and rudder angle. The unknown time delays are overcome by exploiting the common Lyapunov-Krasovskii functionals (CLKFs). Combined with error transformation, the proposed control method guarantees that i) all of the signals for the system are semi-global uniformly ultimately boundedness (SGUUB) under arbitrary switching; and, ii) the tracking error of system output keeps within a small neighborhood around the origin. The results of simulation results are shown to demonstrate the feasibility of the control strategy.

关键词： Adaptive systems Backstepping Delay effects Simulation Switches Artificial neural networks Observers

MDD-Enabled Two-Tier Terahertz Fronthaul in Indoor Industrial Cell-Free Massive MIMO

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arXiv 2023年

作者： Li, Bohan Dupleich, Diego Xia, Guoqing Zhou, Huiyu Zhang, Yue Xiao, Pei Yang, Lie-Liang The School of CMS Uni. of Leicester LeicesterLE1 7RH United Kingdom Fraunhofer Institute for Integrated Circuits IIS Erlangen91058 Germany The School of Engineering Uni. of Leicester LeicesterLE1 7RH United Kingdom The Institute of Communication Measurement Technology Chengdu610095 China 5GIC 6GIC Uni. of Surrey GuildfordGU2 7XH United Kingdom The School of ECS Univ. of Southampton SouthamptonSO17 1BJ United Kingdom

To make indoor industrial cell-free massive multiple-input multiple-output (CF-mMIMO) networks free from wired fronthaul, this paper studies a multicarrier-division duplex (MDD)enabled two-tier terahertz (THz) fronthaul scheme. More specifically, two layers of fronthaul links rely on the mutually orthogonal subcarreir sets in the same THz band, while access links are implemented over sub-6G band. The proposed scheme leads to a complicated mixed-integer nonconvex optimization problem incorporating access point (AP) clustering, device selection, the assignment of subcarrier sets between two fronthaul links and the resource allocation at both the central processing unit (CPU) and APs. In order to address the formulated problem, we first resort to the low-complexity but efficient heuristic methods thereby relaxing the binary variables. Then, the overall end-to-end rate is obtained by iteratively optimizing the assignment of subcarrier sets and the number of AP clusters. Furthermore, an advanced MDD frame structure consisting of three parallel data streams is tailored for the proposed scheme. Simulation results demonstrate the effectiveness of the proposed dynamic AP clustering approach in dealing with the varying sizes of networks. Moreover, benefiting from the well-designed frame structure, MDD is capable of outperforming TDD in the two-tier fronthaul networks. Additionally, the effect of the THz bandwidth on system performance is analyzed, and it is shown that with sufficient frequency resources, our proposed two-tier fully-wireless fronthaul scheme can achieve a comparable performance to the fiber-optic based systems. Finally, the superiority of the proposed MDD-enabled fronthaul scheme is verified in a practical scenario with realistic ray-tracing simulations. © 2023, CC BY.

关键词： Heuristic methods

Spectrum Allocation and Aggregation in Cognitive Radio Satellite communications

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Spectrum Allocation and Aggregation in Cognitive Radio Satel...

International Conference on communications, Signal Processing, and Systems, CSPS 2018

作者： Fu, Rong Xue, Yuhong Song, Meng He, Shi Yang, Zhen Liu, Jinghui Yan, Suna Liu, Xin Beijing Institute of Control Engineering China Academy of Space Technology Beijing100094 China School of Information and Communication Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9789811362637

With the rapid development of satellite communications, more and more users and services are flooding into satellite communications networks, causing a shortage of network resources. The most serious of these is the shortage of spectrum resources, which has become one of the bottlenecks to the development of satellite communications. The introduction of cognitive radio technology has greatly eased this conflict. In this paper, focusing on the shortage of KU-band satellite spectrum resources, combining the cutting-edge cognitive radio and spectrum aggregation technology in the field of communications, a KU satellite spectrum allocation strategy based on cognitive radio technology is proposed. By dynamically assigning frequency resources, the use efficiency of spectrum resources is greatly improved and user satisfaction is improved. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Cognitive radio

Compacting deep neural networks for internet of things: Methods and applications

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arXiv 2021年

作者： Zhang, Ke Ying, Hanbo Dai, Hong-Ning Li, Lin Peng, Yuangyuang Guo, Keyi Yu, Hongfang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Science and Technology on Electronic Information Control Laboratory Chengdu China School of Communication and Information Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology Macau University of Science and Technology China Courant Institute of Mathematical Science New York University New York United States

Deep Neural Networks (DNNs) have shown great success in completing complex tasks. However, DNNs inevitably bring high computational cost and storage consumption due to the complexity of hierarchical structures, thereby hindering their wide deployment in Internet-of-Things (IoT) devices, which have limited computational capability and storage capacity. Therefore, it is a necessity to investigate the technologies to compact DNNs. Despite tremendous advances in compacting DNNs, few surveys summarize compacting-DNNs technologies, especially for IoT applications. Hence, this paper presents a comprehensive study on compacting-DNNs technologies. We categorize compacting-DNNs technologies into three major types: 1) network model compression, 2) Knowledge Distillation (KD), 3) modification of network structures. We also elaborate on the diversity of these approaches and make side-by-side comparisons. Moreover, we discuss the applications of compacted DNNs in various IoT applications and outline future *** Codes 68T07 © 2021, CC BY-NC-SA.

关键词： Internet of things