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Synthesis of Decoherence-Free Modes in Linear Quantum Passive Systems via Robust Pole Placement

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Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Miao, Zibo Pan, Yu Gao, Qing School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Guangdong Key Laboratory of Intelligent Morphing Mechanisms Shenzhen518055 China The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique. © 2024 IEEE.

关键词： Poles

A Simulation Method to Play with Porosity and Particle Size Distribution for Characterizing Porous Media

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arXiv 2024年

作者： Wang, Yuhe National & Local Joint Engineering Laboratory for Big Data Analysis and Computing Technology Beijing100190 China Institute for Scientific Computation Texas A&M University College StationTX77843 United States

This note presents a simulation method for investigating the relationship between porosity and particle size distribution in porous media characterization. The method simulates particle packing based on particle size distributions, incorporating descent and collision rules to mimic the natural deposition process while minimizing boundary-induced porosity distortions. By systematically varying the mean particle size and standard deviation, multiple packed structures are generated, and their corresponding porosities are calculated. This approach offers a flexible and scalable tool for exploring the impact of particle size distribution on porosity, providing both qualitative and quantitative insights into porous media properties. © 2024, CC BY.

关键词： Porosity

Timeseria: an object-oriented time series processing library

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arXiv 2024年

作者： Russo, Stefano Alberto Taffoni, Giuliano Bortolussi, Luca Italian National Center For HPC Big Data and Quantum Computing Bologna Italy INAF - Italian National Institute for Astrophysics Observatory of Trieste Italy University of Trieste Department of Mathematics Informatics and Geosciences Trieste Italy

Timeseria is an object-oriented time series processing library implemented in Python, which aims at making it easier to manipulate time series data and to build statistical and machine learning models on top of it. Unlike common data analysis frameworks, it builds up from well defined and reusable logical units (objects), which can be easily combined together in order to ensure a high level of consistency. Thanks to this approach, Timeseria can address by design several non-trivial issues which are often underestimated, such as handling data losses, non-uniform sampling rates, differences between aggregated data and punctual observations, time zones, daylight saving times, and more. Timeseria comes with a comprehensive set of base data structures, data transformations for resampling and aggregation, common data manipulation operations, and extensible models for data reconstruction, forecasting and anomaly detection. It also integrates a fully featured, interactive plotting engine capable of handling even millions of data points. © 2024, CC BY-SA.

关键词： Time series

Analysis on n-level quantum systems by means of a coordinate transformation

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Analysis on n-level quantum systems by means of a coordinate...

IEEE Conference on Decision and Control

作者： Huilong Xu Zibo Miao Qing Gao School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The structural decomposition of two-level quantum systems has recently been established, related to various applications in quantum information science. As an extension to previous work on two-level quantum systems, this paper aims to investigate the structural properties of n-level quantum systems. We develop algebraic conditions under which the attainment of quantum back-action evading (BAE) measurements, the generation of decoherence-free (DF) subspaces and quantum non-demolition (QND) variables, as well as the existence of steadystate solutions for the Lindblad equations, can be realized. In particular, a specific coordinate transformation is explicitly constructed to facilitate the achievement of above-mentioned objectives, accompanied by an illustrative numerical example.

关键词： Quantum system Atmospheric measurements Quantum information science Particle measurements

Bayesian Functional Analysis for Untargeted Metabolomics data with Matching Uncertainty and Small Sample Sizes

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arXiv 2023年

作者： Ma, Guoxuan Kang, Jian Yu, Tianwei Department of Biostatistics University of Michigan Ann ArborMI48109 United States School of Data Science The Chinese University of Hong Kong – Shenzhen Guangdong Shenzhen518172 China Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China Guangdong Provincial Key Laboratory of Big Data Computing Shenzhen518172 China

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application given its ability to depict the global metabolic pattern in biological samples. However, the data is noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection, and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible. © 2023, CC BY.

关键词： Functional analysis

Enhancing Recommendation with Denoising Auxiliary Task

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arXiv 2024年

作者： Liu, Pengsheng Zheng, Linan Chen, Jiale Zhang, Guangfa Xu, Yang Fang, Jinyun College of Big Data and Information Engineering Guizhou University Guiyang550025 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of the Chinese Academy of Sciences Beijing100049 China

The historical interaction sequences of users plays a crucial role in training recommender systems that can accurately predict user preferences. However, due to the arbitrariness of user behavior, the presence of noise in these sequences poses a challenge to predicting their next actions in recommender systems. To address this issue, our motivation is based on the observation that training noisy sequences and clean sequences (sequences without noise) with equal weights can impact the performance of the model. We propose a novel self-supervised Auxiliary Task Joint Training (ATJT) method aimed at more accurately reweighting noisy sequences in recommender systems. Specifically, we strategically select subsets from users’ original sequences and perform random replacements to generate artificially replaced noisy sequences. Subsequently, we perform joint training on these artificially replaced noisy sequences and the original sequences. Through effective reweighting, we incorporate the training results of the noise recognition model into the recommender model. We evaluate our method on three datasets using a consistent base model. Experimental results demonstrate the effectiveness of introducing self-supervised auxiliary task to enhance the base model’s performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

Reservoir Static Property Estimation Using Nearest-Neighbor Neural Network

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arXiv 2024年

This note presents an approach for estimating the spatial distribution of static properties in reservoir modeling using a nearest-neighbor neural network. The method leverages the strengths of neural networks in approximating complex, non-linear functions, particularly for tasks involving spatial interpolation. It incorporates a nearest-neighbor algorithm to capture local spatial relationships between data points and introduces randomization to quantify the uncertainty inherent in the interpolation process. This approach addresses the limitations of traditional geostatistical methods, such as Inverse Distance Weighting (IDW) and Kriging, which often fail to model the complex non-linear dependencies in reservoir data. By integrating spatial proximity and uncertainty quantification, the proposed method can improve the accuracy of static property predictions like porosity and permeability. © 2024, CC BY-NC-SA.

关键词： Interpolation

Paid User data Mining Approach for a System to Assist with Volunteering for the College Entrance Examination

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Paid User Data Mining Approach for a System to Assist with V...

Information and Communication Technology (ICTech), International Conference of

作者： Cen Gao Sheng Wang Lijun Fu Yonghong Zhang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Laboratory of Big Data and Artificial Intelligence Technology Shandong University Jinan China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China Institute of Future Artificial Intelligence Technology and Innovative Applications Beijing China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

Addressing the dilemma that the current paid user mining techniques are complex and unsuitable for some specific applications, the present study suggests a rule-based paid user mining approach to streamline the implementation process. The main process consists of a buried design to produce JSON format logs detailing user usage. These logs are obtained as raw data in topics created with Kafka. Real-time user behavior data is then analyzed, using Spark Streaming computation engine, and matching calculation is executed based on predefined rules to determine a likelihood threshold for the conversion of nonpaying users to paying users. If the conversion probability threshold surpasses the preset level, the system identifies the user as a potential paying customer. Consequently, a message is delivered to the customer based on the outcome. The system might configure the coupon amount contingent upon different thresholds and distinct specified indications. Additionally, it persists in updating the customer's labeling status, ensuring adequately responsive, targeted advertising strategies are present at each customer visit. This method doesn't require intricate machine learning algorithms, making implementation more straightforward and providing crucial technical support for subsequent user operations. The implementation of this design improves the likelihood of user payments and boosts product profits.

关键词： Training Costs Computational modeling Distributed databases Training data Machine learning data models data mining Sparks Engines

Research on the purchase decision of family cars 5

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Research on the purchase decision of family cars

2020 5th International Seminar on Computer Technology, Mechanical and Electrical Engineering, ISCME 2020

作者： Wu, Xinlin Chen, Congli Department of Mathematics Hubei University of Education Wuhan430205 China Institute of Big Data Modeling and Intelligent Computing Hubei University of Education Wuhan430205 China

Faced with a wide variety of cars in the automobile market, many consumers often find it difficult to choose. In order to solve the decision-making problem in the purchase of private cars, this paper takes several compact family cars as the research object. The price, power, fuel consumption, configuration and space of the car are selected as the research indicators. The relevant mathematical model is established based on analytic hierarchy process combined with relevant data information of automobile market. The results of this paper can provide consumers with some decision references and suggestions in reality. © Published under licence by IOP Publishing Ltd.

关键词： Decision making