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Quantum state tomography from observable time traces in closed quantum systems

control Theory and Technology 2024年第2期22卷 222-234页

作者： Shuixin Xiao Yuanlong Wang Qi Yu Jun Zhang Daoyi Dong Ian R.Petersen University of Michigan–Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong UniversityShanghai200240China School of Engineering The Australian National UniversityCanberraACT2601Australia Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China Centre for Quantum Computation and Communication Technology(Australian Research Council) Centre for Quantum DynamicsGriffith UniversityBrisbaneQLD4111Australia School of Engineering and Technology University of New South WalesCanberraACT2600Australia

The task to estimate all the parameters of an unknown quantum state, also called quantum state tomography, is essential for characterizing and controlling quantum systems. In this paper, we utilize observable time traces to identify the initial quantum state of a closed quantum system, based on the state space approach in the control theory. In the informationally complete scenario, we show that with a linear regression estimation (LRE), the mean squared error (MSE) scales as , where N is the resource number. In the informationally incomplete scenario, we introduce regularization LRE to perform the state tomography task. We employ PBH test to demonstrate that closed quantum systems with only one observable are informationally incomplete and propose using observables, where d is the dimension of the quantum state, for informational completeness. Numerical examples demonstrate the effectiveness of our method.

关键词： Quantum state tomography Time trace Quantum system identification

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Tiny noise, big mistakes: adversarial perturbations induce errors in brain–computer interface spellers

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National Science Review 2021年第4期8卷 78-90页

作者： Xiao Zhang Dongrui Wu Lieyun Ding Hanbin Luo Chin-Teng Lin Tzyy-Ping Jung Ricardo Chavarriaga Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology School of Civil Engineering and Mechanics Huazhong University of Science and Technology Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Swartz Center for Computational Neuroscience Institute for Neural ComputationUniversity of California San Diego Center for Advanced Neurological Engineering Institute of Engineering in Medicine University of California San Diego ZHAW Data Lab Zürich University of Applied Sciences

An electroencephalogram(EEG)-based brain–computer interface(BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g. amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a *** studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e. they can be severely attacked by adversarial perturbations,which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.

关键词： electroencephalogram brain-computer interfaces BCI spellers adversarial examples

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Electroencephalogram and Event-Related Potential in Mild Cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 162-184页

作者： Azami, Hamed Mirjalili, Mina Rajji, Tarek K. Wu, Chien-Te Humeau-Heurtier, Anne Jung, Tzyy-Ping Wei, Chun-Shu Trinh, Thanh-Tung Liu, Yi-Hung University of Toronto Centre for Addiction and Mental Health TorontoONM5S 1A1 Canada University of Texas Southwestern Medical Center DallasTX75390 United States University of Florida Department of Occupational Therapy GainesvilleFL32611 United States University of Florida Center for Cognitive Aging and Memory McKnight Brain Institute GainesvilleFL32611 United States University of Angers LARIS SFR MATHSTIC AngersF-49000 France University of California Institute for Neural Computation Institute of Engineering in Medicine La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu City30010 Taiwan National Taipei University of Technology Graduate Institute of Manufacturing Technology College of Mechanical and Electrical Engineering Taipei10608 Taiwan Vietnam National University Department of Information and Communication Technology Faculty of Nontraditional Security HSB Hanoi720325 Viet Nam National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu City30010 Taiwan

Mild cognitive impairment (MCI) is an early stage of non-age-related cognitive decline with an increased risk of progressing to dementia. Early detection of MCI is essential for implementing preventative strategies that can delay or prevent the onset of dementia, ultimately improving patient outcomes and reducing healthcare costs. Electroencephalograms (EEGs) and event-related potentials (ERPs) have shown significant promise in detecting MCI due to their affordability, real-time monitoring capabilities, and noninvasiveness. EEG provides continuous brain activity data, while ERPs offer insights into specific cognitive processes by analyzing brain responses to stimuli. These methods can complement each other in MCI diagnosis by providing a comprehensive view of overall brain function and detailed information on specific cognitive processes. However, EEG and ERP are susceptible to noise and interindividual variability, which can hinder their reliability. In addition, applying machine learning models on EEG or ERP for MCI detection presents challenges such as the risk of overfitting and difficulties in interpreting the underlying decision-making process. This review emphasizes recent advancements in signal processing and feature extraction methods applied to EEG and ERP data and explores the use of machine learning and deep learning techniques to enhance diagnostic accuracy and interpretative depth. By integrating these methodologies, the review highlights how EEG and ERP can contribute to a more effective understanding and monitoring of cognitive changes associated with MCI, underscoring the importance of early diagnosis for timely intervention and improved patient care. Finally, the review focuses on future research directions, including the development of advanced analytical techniques and multimodal integration approaches involving EEG and ERP to further improve diagnostic accuracy and clinical application. © 2024 IEEE.

关键词： Deep learning

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Quantum state and detector tomography with known rank

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IFAC-PapersOnLine 2023年第2期56卷 5881-5887页

作者： Shuixin Xiao Yuanlong Wang Jun Zhang Daoyi Dong Hidehiro Yonezawa School of Engineering and Information Technology University of New South Wales Canberra ACT 2600 Australia University of Michigan - Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China Centre for Quantum Computation and Communication Technology Australian Research Council Canberra ACT 2600 Australia

Quantum state tomography and quantum detector tomography are two main problems in quantum system identification. In this paper, we study state tomography and detector tomography with prior knowledge about the true rank of the unknown state/detector. In this case, we propose a sufficient condition for static (non-adaptive) tomography algorithms to achieve the optimal infidelity scaling O(1/N) on the number of state copies N. Then we present two examples of such static quantum state tomography and quantum detector tomography algorithms. Numerical examples demonstrate the effectiveness of our algorithms.

关键词： Quantum system identification quantum state tomography quantum detector tomography

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A numerical toy model of Langevin dynamics provides real-time visualization of colloidal microdroplet evaporation

arXiv

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arXiv 2025年

作者： Derkachov, Gennadiy Jakubczyk, Tomasz Alikhanzadeh-Arani, Sima Wojciechowski, Tomasz Jakubczyk, Daniel Institute of Physics Polish Academy of Sciences al. Lotników 32/46 WarsawPL-02668 Poland Institute of Control and Computation Engineering Warsaw University of Technology ul. Nowowiejska 15/19 WarsawPL-00665 Poland Frahangian University P.O. Box 14665-889 Tehran Iran International Research Centre MagTop al. Lotników 32/46 WarsawPL-02668 Poland

We have developed and tested a simplified but versatile numerical model of nanoparticles' aggregation using Langevin dynamics. The model is particularly capable of simulating aggregation in an evaporating (or condensing) microdroplet. It runs on a graphics processing unit (GPU), which makes it sufficiently fast for real-time conceptualization tasks. We have verified the results of modeling against the findings from two types of experiments we conducted in electrodynamic traps. Firstly, our model helped us to elucidate the phenomenon of scattering 'revival', often observed during the evaporation of composite microdroplets. Further on, we were able to mimic our experiments, in which the microdroplets were dried up to form nanoparticle (NP) aggregates, and then soft-landed. Thus we could compare model predictions with SEM imaging. The model was tested for up to 2.5×105 nanoparticles of several coexisting types. Several types of interactions can be accounted for: inter-particle: Lennard-Jones and Coulomb;external: dispersion medium viscosity, centrifugal force, gravity, surface tension, and interface movement. Brownian motion of nanoparticles can be freely controlled. The core program is accompanied by scripts extracting statistical NP aggregates properties in post-processing - fractal dimension and radial distribution functions. The codes are made available in public repositories. Several diverse evolution scenarios are presented. © 2025, CC BY.

关键词： Brownian movement

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On the regularization and optimization in quantum detector tomography

arXiv

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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

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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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Recycling qubits for the generation of Bell nonlocality between independent sequential observers

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Physical Review A 2022年第2期105卷 022411-022411页

作者： Shuming Cheng Lijun Liu Travis J. Baker Michael J. W. Hall The Department of Control Science and Engineering Tongji University Shanghai 201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai 201804 China Institute for Advanced Study Tongji University Shanghai 200092 China College of Mathematics and Computer Science Shanxi Normal University Linfen 041000 China Centre for Quantum Computation and Communication Technology (Australian Research Council) Centre for Quantum Dynamics Griffith University Brisbane QLD 4111 Australia Department of Theoretical Physics Research School of Physics Australian National University Canberra ACT 0200 Australia

There is currently much interest in the recycling of entangled systems, for use in quantum information protocols by sequential observers. In this work, we study the sequential generation of Bell nonlocality via recycling one or both components of two-qubit states. We first give a description of two-valued qubit measurements in terms of measurement bias, strength, and reversibility, and derive useful tradeoff relations between them. Then, we derive one-sided monogamy relations for unbiased observables that strengthen the recent conjecture by Cheng et al. [Phys. Rev. A 104, L060201 (2021)] that if the first pair of observers violate Bell nonlocality then a subsequent independent pair cannot, and give semianalytic results for the best possible monogamy relation. We also extend the construction by Brown and Colbeck [Phys. Rev. Lett. 125, 090401 (2020)] to obtain (i) a broader class of two-qubit states that allow the recycling of one qubit by a given number of observers on one side and (ii) a scheme for generating Bell nonlocality between arbitrarily many independent observers on each side, via the two-sided recycling of multiqubit states. Our results are based on a formalism that is applicable to more general problems in recycling entanglement and hence is expected to aid progress in this field.

关键词： Nonlocality Quantum correlations in quantum information Quantum networks

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

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

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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

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A two-stage solution to quantum process tomography: error analysis and optimal design

arXiv

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arXiv 2024年

作者： Xiao, Shuixin Wang, Yuanlong Zhang, Jun Dong, Daoyi Mooney, Gary J. Petersen, Ian R. Yonezawa, Hidehiro School of Engineering and Technology University of New South Wales CanberraACT2600 Australia University of Michigan–Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China School of Engineering The Australian National University CanberraACT2601 Australia Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China CIICADA Lab School of Engineering Australian National University ACT2601 Australia School of Physics The University of Melbourne ParkvilleVIC3010 Australia School of Physics University of Melbourne ParkvilleVIC3010 Australia School of Engineering Australian National University ACT2601 Australia RIKEN Center for Quantum Computing Japan Centre for Quantum Computation and Communication Technology Australian Research Council CanberraACT2600 Australia

Quantum process tomography is a critical task for characterizing the dynamics of quantum systems and achieving precise quantum control. In this paper, we propose a two-stage solution for both trace-preserving and non-trace-preserving quantum process tomography. Utilizing a tensor structure, our algorithm exhibits a computational complexity of O(MLd2) where d is the dimension of the quantum system and M, L represent the numbers of different input states and measurement operators, respectively. We establish an analytical error upper bound and then design the optimal input states and the optimal measurement operators, which are both based on minimizing the error upper bound and maximizing the robustness characterized by the condition number. Numerical examples and testing on IBM quantum devices are presented to demonstrate the performance and efficiency of our algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Error analysis

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