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State Permutation Control in Non-Hermitian Multiqubit Systems with Suppressed Non-Adiabatic Transitions

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arXiv 2025年

作者： Arkhipov, Ievgen I. Lewalle, Philippe Nori, Franco Özdemir, Şahin K. Whaley, K. Birgitta Joint Laboratory of Optics Palacký University Institute of Physics of CAS Faculty of Science Palacký University 17. listopadu 12 Olomouc771 46 Czech Republic Department of Chemistry University of California BerkeleyCA94720 United States Berkeley Center for Quantum Information and Computation BerkeleyCA94720 United States Theoretical Quantum Physics Laboratory Cluster for Pioneering Research RIKEN Saitama Wako-shi351-0198 Japan Quantum Information Physics Theory Research Team Quantum Computing Center RIKEN Wakoshi Saitama351-0198 Japan Physics Department The University of Michigan Ann ArborMI48109-1040 United States Department of Electrical and Computer Engineering Saint Louis University St. LouisMO63103 United States

Non-Hermitian systems have been at the focus of intense research for over a decade, partly due to their nontrivial energy topology formed by intersecting Riemann manifolds with branch points known as exceptional points (EPs). This spectral property can be exploited, e.g., to achieve controlled state permutations that are necessary for implementing a wide class of classical and quantum information protocols. However, the imaginary spectra of typical non-Hermitian systems lead to instabilities and breakdown of adiabaticity, which impedes the practical use of EP-induced energy topologies in quantum information protocols that explicitly rely on symmetric state flips. On the other hand, techniques that help to suppress non-adiabatic transitions in non-Hermitian systems have also been developed, but these advances have so far been limited to single qubits. In this work, we address this long-standing problem by introducing a model of interacting qubits governed by an effective non-Hermitian Hamiltonian that hosts EPs and possesses a completely real energy spectrum. We demonstrate that such non-Hermitian Hamiltonians enable realization of permutation groups in the multi-qubit eigenspace. Our findings indicate that, contrary to previous beliefs, non-Hermiticity can be utilized to achieve controlled state permutations in time-modulated multiqubit systems, thus paving the way for the advancement and development of novel quantum information protocols. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

Temporal Spectrum Cartography in Low-Altitude Economy Networks: A Generative AI Framework with Multi-Agent Learning

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arXiv 2025年

作者： Zhao, Changyuan Zhang, Ruichen Wang, Jiacheng Niyato, Dusit Sun, Geng Du, Hongyang Li, Zan Jamalipour, Abbas Kim, Dong In College of Computing and Data Science Nanyang Technological University Singapore CNRS@CREATE 1 Create Way 08-01 Create Tower 138602 Singapore College of Computer Science and Technology Jilin University 130012 China Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong State Key Laboratory of Integrated Services Networks Xidian University Xian710071 China School of Electrical and Computer Engineering University of Sydney Australia Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

This paper introduces a two-stage generative AI (GenAI) framework tailored for temporal spectrum cartography in low-altitude economy networks (LAENets). LAENets, characterized by diverse aerial devices such as UAVs, rely heavily on wireless communication technologies while facing challenges, including spectrum congestion and dynamic environmental interference. Traditional spectrum cartography methods have limitations in handling the temporal and spatial complexities inherent to these networks. Addressing these challenges, the proposed framework first employs a Reconstructive Masked Autoencoder (RecMAE) capable of accurately reconstructing spectrum maps from sparse and temporally varying sensor data using a novel dual-mask mechanism. This approach significantly enhances the precision of reconstructed radio frequency (RF) power maps. In the second stage, the Multi-agent Diffusion Policy (MADP) method integrates diffusion-based reinforcement learning to optimize the trajectories of dynamic UAV sensors. By leveraging temporal-attention encoding, this method effectively manages spatial exploration and exploitation to minimize cumulative reconstruction errors. Extensive numerical experiments validate that this integrated GenAI framework outperforms traditional interpolation methods and deep learning baselines by achieving 57.35% and 88.68% reconstruction error reduction, respectively. The proposed trajectory planner substantially improves spectrum map accuracy, reconstruction stability, and sensor deployment efficiency in dynamically evolving low-altitude environments. © 2025, CC BY.

关键词： Deep reinforcement learning

Wireless Acoustic Sensor Networks and Edge computing for Rapid Acoustic Monitoring

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 64-74页

作者： Zhengguo Sheng Saskia Pfersich Alice Eldridge Jianshan Zhou Daxin Tian Victor C.M.Leung the Department of Engineering and Design University of Sussex Infineon Technologies AG the Department of Music University of Sussex Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety ControlSchool of Transportation Science and EngineeringBeihang University the Department of Electrical and Computer Engineering The University of British Columbia

Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity *** scale of the data involved requires automated methods,however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive,hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ *** and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination *** tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions.

关键词： Acoustic sensor networks edge computing energy efficiency

Large AI Models in Health Informatics: Applications, Challenges, and the Future

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arXiv 2023年

作者： Qiu, Jianing Li, Lin Sun, Jiankai Peng, Jiachuan Shi, Peilun Zhang, Ruiyang Dong, Yinzhao Lam, Kyle Lo, Frank P.-W. Xiao, Bo Yuan, Wu Wang, Ningli Xu, Dong Lo, Benny The Department of Computing Imperial College London United Kingdom The Department of Biomedical Engineering The Chinese University of Hong Kong Hong Kong Ltd. Hong Kong The Department of Informatics King’s College London United Kingdom The School of Engineering Stanford University United States The Department of Engineering Science University of Oxford United Kingdom The Faculty of Engineering The University of Hong Kong Hong Kong The Department of Surgery and Cancer Imperial College London United Kingdom The Hamlyn Centre for Robotic Surgery Imperial College London United Kingdom Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University China Beijing Ophthalmology & Visual Sciences Key Laboratory Beijing China The Department of Electrical Engineering and Computer Science United States The Christopher S. Bond Life Sciences Center University of Missouri United States The Facualty of Medicine Imperial College London United Kingdom

Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A prime example is ChatGPT, whose capability has compelled people’s imagination about the far-reaching influence that large AI models can have and their potential to transform different domains of our lives. In health informatics, the advent of large AI models has brought new paradigms for the design of methodologies. The scale of multi-modal data in the biomedical and health domain has been ever-expanding especially since the community embraced the era of deep learning, which provides the ground to develop, validate, and advance large AI models for breakthroughs in health-related areas. This article presents a comprehensive review of large AI models, from background to their applications. We identify seven key sectors in which large AI models are applicable and might have substantial influence, including 1) bioinformatics;2) medical diagnosis;3) medical imaging;4) medical © 2023, CC BY-NC-ND.

关键词： Medical imaging

Macroscopic Tunneling Probe of Moiré Spin Textures in Twisted CrI3

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arXiv 2024年

作者： Yang, Bowen Patel, Tarun Cheng, Meixin Pichugin, Kostyantyn Tian, Lin Sherlekar, Nachiket Yan, Shaohua Fu, Yang Tian, Shangjie Lei, Hechang Reimer, Michael E. Okamoto, Junichi Tsen, Adam W. Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Chemistry University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooN2L 3G1 Canada Department of Physics Beijing Key Laboratory of Optoelectronic Functional Materials & Micro-nano Devices Renmin University of China Beijing100872 China School of Materials Science and Engineering Anhui University Hefei230601 China Institute of Physics University of Freiburg Hermann-Herder-Str. 3 Freiburg79104 Germany EUCOR Centre for Quantum Science and Quantum Computing University of Freiburg Hermann-Herder-Str. 3 Freiburg79104 Germany

Various noncollinear spin textures and magnetic phases have been predicted in twisted two-dimensional CrI3 due to competing ferromagnetic (FM) and antiferromagnetic (AFM) interlayer exchange from moiré stacking — with potential spintronic applications even when the underlying material possesses a negligible Dzyaloshinskii–Moriya or dipole–dipole interaction. Recent measurements have shown evidence of coexisting FM and AFM layer order in small-twist-angle CrI3 bilayers and double bilayers. Yet, the nature of the magnetic textures remains unresolved and possibilities for their manipulation and electrical readout are unexplored. Here, we use tunneling magnetoresistance to investigate the collective spin states of twisted double-bilayer CrI3 under both out-of-plane and in-plane magnetic fields together with detailed micromagnetic simulations of domain dynamics based on magnetic circular dichroism. Our results capture hysteretic and anisotropic field evolutions of the magnetic states and we further uncover two distinct non-volatile spin textures (out-of-plane and in-plane domains) at ≈1° twist angle, with a different global tunneling resistance that can be switched by magnetic field. © 2024, CC BY-NC-ND.

关键词： Chromium compounds

Editorial: special issue on “The security, privacy, and digital forensics of mobile networks and mobile cloud”

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Digital Communications and Networks 2019年第4期5卷 203-204页

作者： Lei Chen Guofei Gu Wenjia Li Rami J.Haddad the Department of Information Technology Allen E. Paulson College of Engineering and Computing at Georgia Southern University USA the Department of Computer Science & Engineering at Texas A&M University Computer Science at New York Institute of Technology (NYIT) since the Department of Electrical and Computer Allen E. Paulson College of Engineering and Computing at Georgia Southern University the Optical Networks and Smart Grid Applications (ONSmart) Laboratory GSU

To many people, smartphones and other mobile devices have been an indispensable part of their daily lives. With personal financial and medical information and job-related data processed on mobile devices,systems and networks as well as the connected mobile cloud, it is critically important that secure mobile operating systems, secure mobile applications, secure smartphone devices, secure cellular networks and secure mobile cloud all keep up with the ever-growing mobile usage.

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A Spectrum Resource Sharing Algorithm for IoT Networks based on Reinforcement Learning 16

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A Spectrum Resource Sharing Algorithm for IoT Networks based...

16th IEEE International Wireless Communications and Mobile computing Conference, IWCMC 2020

作者： Shi, Zhaoyuan Xie, Xianzhong Kadoch, Michel Cheriet, Mohamed Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China École de Technologie Supérieure University of Quebec Department of Electrical Engineering Montreal Canada

ISBN: (纸本)9781728131290

Internet of Things (IoT) has attracted tremendous interest since it can improve production efficiency and system intelligence significantly. However, with the explosive growth of various types of device and data flow, IoT suffers spectrum resource scarcity for wireless applications. In this paper, we propose a solution for spectrum resource sharing in the IoT network, with the objective to facilitate the limited spectrum sharing between different kinds of sensors. To overcome the challenges of unknown dynamic IoT environment, the deep Q-learning network (DQN) is adopted. BS acts as the single agent and centrally manages all spectrum resources. First, a new reward function is designed to drive the learning process, which takes into account the different communication requirements of various sensors. In addition, to improve the learning efficiency of DQN, we compress the action space. Finally, simulation results show that compared with other algorithms, the proposed algorithm can achieve good network performance. © 2020 IEEE.

关键词： Internet of things

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

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arXiv 2022年

作者： Feng, Xin-Ru Li, Heng-Chao Wang, Rui Du, Qian Jia, Xiuping Plaza, Antonio School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS39762 United States School of Engineering and Information Technology University of New South Wales CanberraACT2612 Australia Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica University of Extremadura Cáceres10071 Spain

Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing. Taking the NMF model as a baseline, we show how to improve NMF by utilizing the main properties of HSIs (e.g., spectral, spatial, and structural information). We categorize three important development directions including constrained NMF, structured NMF, and generalized NMF. Furthermore, several experiments are conducted to illustrate the effectiveness of associated algorithms. Finally, we conclude the article with possible future directions with the purposes of providing guidelines and inspiration to promote the development of hyperspectral unmixing. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

Digital noise spectroscopy with a quantum sensor

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arXiv 2022年

作者： Wang, Guoqing Zhu, Yuan Li, Boning Li, Changhao Viola, Lorenza Cooper, Alexandre Cappellaro, Paola Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Nuclear Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics and Astronomy Dartmouth College 6127 Wilder Laboratory HanoverNH03755 United States Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada

We introduce and experimentally demonstrate a quantum sensing protocol to sample and reconstruct the auto-correlation of a noise process using a single-qubit sensor under digital control modulation. This Walsh noise spectroscopy method exploits simple sequences of spin-flip pulses to generate a complete basis of digital filters that directly sample the power spectrum of the target noise in the sequency domain - from which the auto-correlation function in the time domain, as well as the power spectrum in the frequency domain, can be reconstructed using linear transformations. Our method, which can also be seen as an implementation of frame-based noise spectroscopy, solves the fundamental difficulty in sampling continuous functions with digital filters by introducing a transformation that relates the arithmetic and logical time domains. In comparison to standard, frequency-based dynamical-decoupling noise spectroscopy protocols, the accuracy of our method is only limited by the sampling and discretization in the time space and can be easily improved, even under limited evolution time due to decoherence and hardware limitations. Finally, we experimentally reconstruct the auto-correlation function of the effective magnetic field produced by the nuclear-spin bath on the electronic spin of a single nitrogen-vacancy center in diamond, discuss practical limitations of the method, and avenues for further improving the reconstruction accuracy. Copyright © 2022, The Authors. All rights reserved.

关键词： Digital filters