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Robust optimization for quantum reinforcement learning control using partial observations

Physical Review A 2022年第6期105卷 062443-062443页

作者： Chen Jiang Yu Pan Zheng-Guang Wu Qing Gao Daoyi Dong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou 310027 China The School of Automation Science and Electrical Engineering and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China The School of Engineering and Information Technology University of New South Wales Canberra Australian Capital Territory 2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of the quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of the quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and the quantum approximate optimization algorithm (QAOA). It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for a QAOA with a noisy control Hamiltonian. This robust control optimization model can be trained to compensate for the uncertainties in practical quantum computing.

关键词： Machine learning Quantum control Quantum engineering

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Modeling human travel and social contact with multi-layer networks for epidemic prediction 9

Modeling human travel and social contact with multi-layer ne...

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9th IEEE International Conference on Bioinformatics and Computational Biology, ICBCB 2021

作者： Duan, Wei Wang, Tao Wang, Peng Ju, Rusheng Wang, Xiao Yang, Tian College of Systems Engineering National University of Defense Technology Changsha City China State Key Laboratry of Complex Systems Management and Control Institute of Automation Chinese Academy of Sciences Beijing City China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha City China

ISBN: (纸本)9780738132020

It is a key issue to reasonably represent human travel and social contact in epidemic models. Various measures were applied to develop the models of human mobility and contact in a long range or a short range, such as Brown movement, random walks, spatial networks, gravity models, contact networks. We proposed a method of representing human daily movement and social contact by using multi-layer networks with temporal edge weights. We combined bipartite networks with social networks to describe human daily trip and social contact, respectively. Temporal edge weights of multi-layer networks were employed to denote the propensity of individual movement and contact. We also verified our models and parameters by incorporating human daily travel and contact regularities, as well as comparing experimental results with human behavior statistical laws. At last, we applied a Chinese university campus as a case study to investigate students' daily travel and social contact, and studied the transmission and control strategies of COVID-19 virus. We found stricter control strategies are needed to mitigate the transmission of COVID-19 virus in a university. Once a patient case emerges in a university, it is better to close the campus and quarantine all students. Partial control strategies such as quarantining a part of students and buildings cannot achieve a great effect of mitigating the transmission of COVID-19 virus. Our works are beneficial for the practitioners in the field of computational epidemiology. © 2021 IEEE.

关键词： Viruses

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A Model-based Approach for Indoor Localization Leveraging Single Mobile Sensor

A Model-based Approach for Indoor Localization Leveraging Si...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Yaoxin Duan Rongbin Hu Zexing Liu Yuanyi Zhang Wendi Nie Kam-Yiu Lam Chun Jason Xue Yongli Song Guan Gui Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education China School of Automation/School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China Institute of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China Computer Science Department City University of Hong Kong Hong Kong PRC Beijing Institute of Computer Technology and Applications China College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798331516024

ISBN: (纸本)9798331516031

As widely deployed WiFi sensors in indoor scenarios, e.g., WiFi access points and WiFi monitors, WiFi signal-based indoor localization has attracted increasing attention from research communities in the past decade. Among various WiFi-based localization techniques, received signal strength (RSS) fingerprinting based on multiple sensors reveals its superiority and effectiveness in complex indoor environments. Existing multi-sensor-based techniques mainly focus on designing efficient algorithms to improve localization performance. However, the limitations of 1) densely pre-deployed WiFi sensors and, 2) sensitivity to changing sensors are not considered appropriately. In this paper, we propose a novel technique called Single Mobile Sensor (SMS) localization, which leverages a single mobile sensor for indoor localization. The SMS localization technique employs a fingerprinting technique with a custom-designed model, named SMS Fingerprint Matching (SMSFM) model, which is responsible for matching fingerprints constructed by a single mobile sensor to estimate targets' location. Numerous experiments conducted on a practical testbed have revealed that the SMSFM model surpasses conventional models, leading to SMS localization delivering competitive localization accuracy compared to previous multi-sensor-based technologies, despite relying solely on a single sensor.

关键词： Location awareness Accuracy Sensitivity Fingerprint recognition Sensors Trajectory Indoor environment Wireless fidelity Monitoring

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Study on Gene Splicing Site Recognition Based on Particle Swarm Optimization Twin Support Vector Machine Algorithm for Smart Healthcare

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Zhang, Fuquan Wang, Yiou Mei, Peng Dai, Aibing Wang, Bo Liu, Laiyang Xia, Yong Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350117 China Institute of Science and Technology Information Beijing Academy of Science and Technology Beijing100089 China Beijing100089 China Industrial Digital Finance Department Huaxia Bank Beijing100020 China Digital Performance and Simulation Technology Lab Beijing Institute of Technology Beijing100081 China Fuzhou Maternity and Child Health Care Hospital Fuzhou350005 China Fujian Medical University Fuzhou350122 China

Gene splicing site recognition is a very important research topic in smart healthcare. Gene splicing site recognition is of great significance, not only for the large-scale and high-quality computational annotation of genomes but also for the analysis and recognition of the gene sequences evolutionary process. It is urgent to study a reliable and effective algorithm for gene splice site recognition. Traditional Twin Support Vector Machine (TWSVM) algorithm has advantages in solving small-sample, nonlinear, and high-dimensional problems, but it cannot deal with parameter selection well. To avoid the blindness of parameter selection, particle swarm optimization algorithm was used to find the optimal parameters of twin support vector machine. Therefore, a Particle Swarm Optimization Twin Support Vector Machine (PSO-TWSVM) algorithm for gene splicing site recognition was proposed in this paper. The proposed algorithm was compared with traditional Support Vector Machine algorithm, TWSVM algorithm, and Least Squares Support Vector Machine algorithm. The comparison results show that the positive sample recognition rate, negative sample recognition rate, and correlation coefficient (CC) of the proposed algorithm are the best among the four different support vector machine algorithms. The proposed algorithm effectively improves the recognition rate and the accuracy of splice sites. The comparison experiments verify the feasibility of the proposed algorithm. © 2023 Fuquan Zhang et al.

关键词： Genes

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Lattice piecewise affine approximation of explicit linear model predictive control

Lattice piecewise affine approximation of explicit linear mo...

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IEEE Conference on Decision and control

作者： Jun Xu School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai

ISBN: (纸本)9781665436601

In this paper, the lattice piecewise affine (PWA) approximation of explicit linear model predictive control (MPC) is proposed. The training data consists of the state samples and corresponding affine control laws, based on which the lattice PWA approximation is constructed. The proposed approximation is identical to the explicit MPC control law in unique-order (UO) regions containing the sample points as interior points, thus resemble the explicit MPC control law in a large number of regions. Through simplifying the terms and literals in the lattice PWA approximation, both the storage requirement and online computation complexity are largely decreased. The performance of the proposed approximation strategy is tested through a simulation example, and the result shows that with a moderate number of sample points, the lattice PWA approximation is very close to the explicit MPC control law.

关键词： Conferences Computational modeling Lattices Training data Computational complexity Predictive control

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Self-Organized Criticality in C. Elegans Neural Network

Self-Organized Criticality in C. Elegans Neural Network

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Chinese control Conference (CCC)

作者： Xiao Gu Jian Liu Ye Yuan Peng Zhao Kuankuan Xin Tao Fang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai China Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China Queensland Brain Institute The University of Queensland Brisbane QLD Australia

The self-organized criticality is a fundamental property for organisms to perform normal functional activities. Extensive large-scale biological neural network researches have found the existence of self-organized criticality through avalanche phenomenon, which is typical characteristic of self-organized criticality. However, such research method is not suitable for small-scale biological neural network, because number of neurons in small-scale biological neural network is not large enough to support the emergence of avalanche phenomenon. It has been demonstrated that branching parameter can also be used to describe the propagation of activity in the neural network. Here, in this paper, the C. elegans neural network composed of 302 neurons is selected to investigate the existence of self-organized criticality using branching parameter. Based on the obtained connectome of C. elegans, a directed graph model is constructed to simulate the gradient potential of neurons and the activity of C. elegans, then, the existence of self-organized criticality in the neural network of C. elegans is proved by using branching parameter method. Meanwhile, the result is further confirmed by analyzing the calcium activity data of C. elegans obtained from biological experiments.

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A Constructive Semi-global Differential Game Design Method for Multi-agent Systems under Input Constraint and Collision Avoidance

A Constructive Semi-global Differential Game Design Method f...

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2021 China automation Congress, CAC 2021

作者： Wu, Xiongjun Zhou, Jialing Li, Yuanlong Chen, Chunfang Li, Kai The 802 Institute of Shanghai Academy of Space Flight Technology Eighth Academy of China Aerospace Science and Technology Corporation National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering Shanghai201109 China School of Automation Nanjing University Science and Technology Nanjing210094 China Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The 802 Institute of Shanghai Academy of Space Flight Shanghai210094 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781665426473

The constructive semi-global differential game concept is introduced into the multi-agent's cooperative controller designing of typical autonomous unmanned systems subject to controller input constraint as well as the collision avoidance requirements simultaneously. The problem of driving each agent which is assume to subject to input constraint nonlinearity from the initial position to a desired one and at the same time avoiding collisions (referring to both static obstacles and other agents) is modeled and transformed into multi-agent's collision avoidance problem. In view of the requirement that different agents working together might possibly have individual but conflicting purpose (such as in the multi-missile cooperative detection requiring slowly and continuing actions vs fast formation control scenario, and multi-agents' fast arriving vs avoiding different kinds of obstacles). The differential game theory provided a mechanism or framework for investigating different kinds of problems in which several players seek to achieve individual purposes or goals, which might be competing rather than cooperation. In view of the input constraints, the problem is reduced as a semi-global differential game. Conditions are established to guarantee input constraints are satisfied, and mainly by using the Hamilton-Jacobi-Isaacs (HJI for short) PDIs and as well as Ε-Nash equilibria. The semi-global property of the states are also guaranteed. It provides a description in terms of the invariant set for the overall differential game system under constraint controller input. Illustrative examples are carried out to verify the effectiveness of the method, and also the feasibility of the suggested control protocols. © 2021 IEEE

关键词： Multi agent systems

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Efficient Depth Selection for the Implementation of Noisy Quantum Approximate Optimization Algorithm

arXiv

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arXiv 2022年

作者： Pan, Yu Tong, Yifan Xue, Shibei Zhang, Guofeng State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

Noise on near-term quantum devices will inevitably limit the performance of Quantum Approximate Optimization Algorithm (QAOA). One significant consequence is that the performance of QAOA may fail to monotonically improve with depth. In particular, optimal depth can be found at a certain point where the noise effects just outweigh the benefits brought by increasing the depth. In this work, we propose to use the model selection algorithm to identify the optimal depth with a few iterations of regularization parameters. Numerical experiments show that the algorithm can efficiently locate the optimal depth under relaxation and dephasing noises. © 2022, CC BY.

关键词： Optimization

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Use Knowledge to Augment Data from Scratch: An Explainable Deep Immunohistochemical Diagnosis Model

SSRN

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SSRN 2024年

作者： Zhao, Jing Liu, Yuhan Zhang, Xinzhu Sun, Shiliang Yin, Minzhi School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of Pathology Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine Shanghai200127 China

Pathological diagnosis has an important role as the last step in cancer diagnosis. When the naked eye is not enough to identify cancer, we usually use further immunohistochemical diagnosis, which is the detection of specific proteins to obtain protein sequences and their negative/positive expression, and make the final diagnosis based on the sequences. This process is a classification task for protein detection sequences. However, when deep learning goes into this field, it often has the problem of lack of data and poor explainability. Based on the above description, to solve the problem of small sample classification, we propose to extract knowledge from scratch, build protein knowledge graphs, and reuse protein embeddings from the knowledge graph to improve classification performance. In addition, we give explanation for the diagnostic model by visualizing importance and coupling degree of proteins. In our experiments, we find that our proposed classification model has good classification performance and explainability. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Prognostic Prediction for Non-small-Cell Lung Cancer Based on Deep Neural Network and Multimodal Data 17th

Prognostic Prediction for Non-small-Cell Lung Cancer Based o...

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17th International Conference on Intelligent Computing, ICIC 2021

作者： Zhang, Zhong-Si Xu, Fei Jiang, Han-Jing Chen, Zhan-Heng Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University No. 4800 Caoan Road Shanghai201804 China

ISBN: (纸本)9783030845315

Non-small-cell lung cancer (NSCLC) is the most common lung cancer with poor prognosis. Prognostic prediction is significant in improving the prognosis of NSCLC patients. Clinical information and multi-omics data including gene expression, miRNA, copy number variations, and DNA methylation are closely related to NSCLC prognosis. In this study, we propose a deep neural network to conduct prognostic prediction for NSCLC patients based on all five types of data. Given the high dimensional features of the omics data, past works reduce the feature dimension by regression analysis or correlation sorting algorithms. A shortcoming of these methods is that only a small number of features are considered. To overcome it, we propose a convolution neural network-based feature transformation method, which considers all features and extracts the abstract representations of the omics data. Based on the representations, we predict the five-year survival status of NSCLC patients. The results show that our method achieves a more precise prediction than previous work, with the areas under the curve improved from 0.8163 to 0.8508 and the accuracy improved from 0.7544 to 0.8096. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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