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| 1 | On the Iterative Decoding of Sparse Quantum Codes显示文摘 | Poulin D Chung Yeojin | 2008 | International Journal of Quantum Information and computation2008，8，10： | 1 |
| 2 | Smart Contract Fuzzing Based on Taint Analysis and Genetic Algorithms显示文摘Smart contract has greatly improved the services and capabilities of blockchain,but it has become the weakest link of blockchain security because of its code nature.Therefore,efficient vulnerability detection of smart contract is the key to ensure the security of blockchain system.Oriented to Ethereum smart contract,the study solves the problems of redundant input and low coverage in the smart contract fuzz.In this paper,a taint analysis method based on EVM is proposed to reduce the invalid input,a dangerous operation database is designed to identify the dangerous input,and genetic algorithm is used to optimize the code coverage of the input,which construct the fuzzing framework for smart contract together.Finally,by comparing Oyente and ContractFuzzer,the performance and efficiency of the framework are proved. | Zaoyu Wei Jiaqi Wang Xueqi Shen Qun Luo | 2020 | Journal of Quantum Computing2020，2，1： | 1 |
| 3 | Analysis and Prediction of Regional Electricity Consumption Based on BP Neural Network显示文摘Electricity consumption forecasting is one of the most important tasks for power system workers,and plays an important role in regional power systems.Due to the difference in the trend of power load and the past in the new normal,the influencing factors are more diversified,which makes it more difficult to predict the current electricity consumption.In this paper,the grey system theory and BP neural network are combined to predict the annual electricity consumption in Jiangsu.According to the historical data of annual electricity consumption and the six factors affecting electricity consumption,the gray correlation analysis method is used to screen the important factors,and three factors with large correlation degree are selected as the input parameters of BP neural network.The power forecasting model uses nearly 18 years of data to train and validate the model.The results show that the gray correlation analysis and BP neural network method have higher accuracy in power consumption prediction,and the calculation is more convenient than traditional methods. | Pingping Xia Aihua Xu Tong Lian | 2020 | Journal of Quantum Computing2020，2，1： | 1 |
| 4 | Exponential stability analysis for uncertain neural networks with discrete and distributed time-varying delays显示文摘 | Yang M M Zhong S M | 2014 | World Academy of Science Engineering and Technology:International Journal of Mathematical Computational Physical and Quantum Engineering2014，8，1： | 1 |
| 5 | Weighted Particle Swarm Clustering Algorithm for Self-Organizing Maps显示文摘The traditional K-means clustering algorithm is difficult to determine the cluster number,which is sensitive to the initialization of the clustering center and easy to fall into local optimum.This paper proposes a clustering algorithm based on self-organizing mapping network and weight particle swarm optimization SOM&WPSO(Self-Organization Map and Weight Particle Swarm Optimization).Firstly,the algorithm takes the competitive learning mechanism of a self-organizing mapping network to divide the data samples into coarse clusters and obtain the clustering center.Then,the obtained clustering center is used as the initialization parameter of the weight particle swarm optimization algorithm.The particle position of the WPSO algorithm is determined by the traditional clustering center is improved to the sample weight,and the cluster center is the“food”of the particle group.Each particle moves toward the nearest cluster center.Each iteration optimizes the particle position and velocity and uses K-means and K-medoids recalculates cluster centers and cluster partitions until the end of the algorithm convergence iteration.After a lot of experimental analysis on the commonly used UCI data set,this paper not only solves the shortcomings of K-means clustering algorithm,the problem of dependence of the initial clustering center,and improves the accuracy of clustering,but also avoids falling into the local optimum.The algorithm has good global convergence. | Guorong Cui Hao Li Yachuan Zhang Rongjing Bu Yan Kang Jinyuan Li Yang Hu | 2020 | Journal of Quantum Computing2020，2，2： | 1 |
| 6 | Quantum Blockchain: A Decentralized, Encrypted and Distributed Database Based on Quantum Mechanics显示文摘Quantum blockchain can be understood as a decentralized, encrypted anddistributed database based on quantum computation and quantum information theory.Once the data is recorded in the quantum blockchain, it will not be maliciously tamperedwith. In recent years, the development of quantum computation and quantum informationtheory makes more and more researchers focus on the research of quantum blockchain. Inthis paper, we review the developments in the field of quantum blockchain, and brieflyanalyze its advantages compared with the classical blockchain. The construction and theframework of the quantum blockchain are introduced. Then we introduce the method ofapplying quantum technology to a certain part of the general blockchain. In addition, theadvantages of quantum blockchain compared with classical blockchain and itsdevelopment prospects are summarized. | Chuntang Li Yinsong Xu Jiahao Tang Wenjie Liu | 2019 | Journal of Quantum Computing2019，1，2： | 1 |
| 7 | Near Term Hybrid Quantum Computing Solution to the Matrix Riccati Equations显示文摘The well-known Riccati differential equations play a key role in many fields,including problems in protein folding,control and stabilization,stochastic control,and cybersecurity(risk analysis and malware propaga-tion).Quantum computer algorithms have the potential to implement faster approximate solutions to the Riccati equations compared with strictly classical algorithms.While systems with many qubits are still under development,there is significant interest in developing algorithms for near-term quantum computers to determine their accuracy and limitations.In this paper,we propose a hybrid quantum-classical algorithm,the Matrix Riccati Solver(MRS).This approach uses a transformation of variables to turn a set of nonlinear differential equation into a set of approximate linear differential equations(i.e.,second order non-constant coefficients)which can in turn be solved using a version of the Harrow-Hassidim-Lloyd(HHL)quantum algorithm for the case of Hermitian matrices.We implement this approach using the Qiskit language and compute near-term results using a 4 qubit IBM Q System quantum computer.Comparisons with classical results and areas for future research are discussed. | Augusto Gonzalez Bonorino Malick Ndiaye Casimer DeCusatis | 2022 | Journal of Quantum Computing2022，4，3： | 1 |
| 8 | Improved Prediction and Understanding of Glass-Forming Ability Based on Random Forest Algorithm显示文摘As an ideal material,bulk metallic glass(MG)has a wide range of applications because of its unique properties such as structural,functional and biomedical materials.However,it is difficult to predict the glass-forming ability(GFA)even given the criteria in theory and this problem greatly limits the application of bulk MG in industrial field.In this work,the proposed model uses the random forest classification method which is one of machine learning methods to solve the GFA prediction for binary metallic alloys.Compared with the previous SVM algorithm models of all features combinations,this new model is successfully constructed based on the random forest classification method with a new combination of features and it obtains better prediction results.Simultaneously,it further shows the degree of feature parameters influence on GFA.Finally,a normalized evaluation indicator of binary alloy for machine learning model performance is put forward for the first time.The result shows that the application of machine learning in MGs is valuable. | Chenjing Su Xiaoyu Li Mengru Li Qinsheng Zhu Hao Fu Shan Yang | 2021 | Journal of Quantum Computing2021，3，2： | 1 |
| 9 | Quantum Multi-User Detection Based on Coherent State Signals显示文摘Multi-user detection is one of the important technical problems for moderncommunications. In the field of quantum communication, the multi-access channel onwhich we apply the technology of quantum information processing is still an openquestion. In this work, we investigate the multi-user detection problem based on thebinary coherent-state signals whose communication way is supposed to be seen as aquantum channel. A binary phase shift keying model of this multi-access channel isstudied and a novel method of quantum detection proposed according to the conclusionof the quantum measurement theory. As a result, the average interference betweendeferent users is presented and the average error probability of the quantum detection isderived theoretically. Finally, we show the maximum channel capacity of this effectivedetection for a two-access quantum channel. | Wenbin Yu Yinsong Xu Wenjie Liu Alex Xiangyang Liu Baoyu Zheng | 2019 | Journal of Quantum Computing2019，1，2： | 0 |
| 10 | Anti-Noise Quantum Network Coding Protocol Based on Bell States and Butterfly Network Model显示文摘How to establish a secure and efficient quantum network coding algorithm isone of important research topics of quantum secure communications. Based on thebutterfly network model and the characteristics of easy preparation of Bell states, a novelanti-noise quantum network coding protocol is proposed in this paper. The new protocolencodes and transmits classical information by virtue of Bell states. It can guarantee thetransparency of the intermediate nodes during information, so that the eavesdropper Evedisables to get any information even if he intercepts the transmitted quantum states. Inview of the inevitability of quantum noise in quantum channel used, this paper analyzesthe influence of four kinds of noises on the new protocol in detail further, and verifies theefficiency of the protocol under different noise by mathematical calculation and analysis.In addition, based on the detailed mathematical analysis, the protocol has functioned wellnot only on improving the efficiency of information transmission, throughput and linkutilization in the quantum network, but also on enhancing reliability and antieavesdroppingattacks. | Zhexi Zhang Zhiguo Qu | 2019 | Journal of Quantum Computing2019，1，2： | 0 |
| 11 | Quantum Algorithm for Appointment Scheduling显示文摘Suppose a practical scene that when two or more parties want to schedule anappointment, they need to share their calendars with each other in order to make itpossible. According to the present result the whole communication cost to solve thisproblem should be their calendars’ length by using a classical algorithm. In this work, weinvestigate the appointment schedule issue made by N users and try to accomplish it inquantum information case. Our study shows that the total communication cost will bequadratic times smaller than the conventional case if we apply a quantum algorithm in theappointment-scheduling problem. | Wenbin Yu Yinsong Xu Wenjie Liu Alex Xiangyang Liu Baoyu Zheng | 2019 | Journal of Quantum Computing2019，1，2： | 0 |
| 12 | Impact Damage Identification for Composite Material Based on Transmissibility Function and OS-ELM Algorithm显示文摘A method is proposed based on the transmissibility function and the OnlineSequence Extreme Learning Machine (OS-ELM) algorithm, which is applied to theimpact damage of composite materials. First of all, the transmissibility functions of theundamaged signals and the damage signals at different points are calculated. Secondly,the difference between them is taken as the damage index. Finally, principal componentanalysis (PCA) is used to reduce the noise feature. And then, input to the online sequencelimit learning neural network classification to identify damage and confirm the damagelocation. Taking the amplitude of the transmissibility function instead of the accelerationresponse as the signal analysis for structural damage identification cannot be influencedby the excitation amplitude. The OS-ELM algorithm is based on the ELM (ExtremeLearning Machine) algorithm, in-creased training speed also increases the recognitionaccuracy. Experiment in the epoxy board shows that the method can effectively identifythe structural damage accurately. | Yajie Sun Yanqing Yuan Qi Wang Sai Ji Lihua Wang Shaoen Wu Jie Chen Qin Zhang | 2019 | Journal of Quantum Computing2019，1，1： | 0 |
| 13 | Hierarchical Geographically Weighted Regression Model显示文摘In spatial analysis, two problems of the scale effect and the spatial dependencehave been plagued scholars, the first law of geography presented to solve the spatialdependence has played a good role in the guidelines, forming the Geographical WeightedRegression (GWR). Based on classic statistical techniques, GWR model has ascertainsignificance in solving spatial dependence and spatial non-uniform problems, but it hasno impact on the integration of the scale effect. It does not consider the interactionbetween the various factors of the sampling scale observations and the numerous factorsof possible scale effects, so there is a loss of information. Crossing a two-stage analysisof “return of regression” to establish the model of Hierarchical Geographically WeightedRegression (HGWR), the first layer of regression analysis reflects the spatial dependenceof space samples and the second layer of the regression reflects the spatial relationshipsscaling. The combination of both solves the spatial scale effect analysis, spatialdependence and spatial heterogeneity of the combined effects. | Fengchang Xue | 2019 | Journal of Quantum Computing2019，1，1： | 0 |
| 14 | Protein Secondary Structure Prediction with Dynamic Self-Adaptation Combination Strategy Based on Entropy显示文摘The algorithm based on combination learning usually is superior to a singleclassification algorithm on the task of protein secondary structure prediction. However,the assignment of the weight of the base classifier usually lacks decision-makingevidence. In this paper, we propose a protein secondary structure prediction method withdynamic self-adaptation combination strategy based on entropy, where the weights areassigned according to the entropy of posterior probabilities outputted by base classifiers.The higher entropy value means a lower weight for the base classifier. The final structureprediction is decided by the weighted combination of posterior probabilities. Extensiveexperiments on CB513 dataset demonstrates that the proposed method outperforms theexisting methods, which can effectively improve the prediction performance. | Yuehan Du Ruoyu Zhang Xu Zhang Antai Ouyang Xiaodong Zhang Jinyong Cheng Wenpeng Lu | 2019 | Journal of Quantum Computing2019，1，1： | 0 |
| 15 | Analysis and Test on Influence Factors of Dew Drop Condensation in Dew Point Hygrometer显示文摘The condensation process of dew droplets is influenced by many factors. Adew point condensation image observation system was built to improve the responsespeed of dew point detector under different measuring conditions. The basic mechanismof dew drop condensation growth was studied and the influence of various factors on thedew drop growth rate were analyzed. And the accuracy of the influence results wasverified based on the improved Hough transform circle detection. The results show thatthe growth rate of dew droplets is affected by ambient temperature, dew pointtemperature, mirror temperature and air velocity. The observed variation of the averageradius of dew droplets is consistent with the theoretical calculations. The maximumradius error is less than 4 μm, the initial error is larger, and the error oscillates in themiddle and late stages of condensation. The establishment of condensation mechanism ishelpful to solve the problem in fast determination of dew point temperature under thecold start of dew point meter, and to improve the response speed. | Shijun Zhao Xiaoying Chen Xiaolei Wang Wenming Ji Zhonghua Dai | 2019 | Journal of Quantum Computing2019，1，1： | 0 |
| 16 | T Application of MES System in the Safety Management of Offshore Oil and Gas Fields显示文摘In order to solve the problem of data island in the safety management ofoffshore oil and gas fields, take full advantage of data for subsequent analysis anddevelopment, and support production safety management of oil and gas fields, the MES,which is maturely applied in manufacturing and downstream production of CNOOC(China National Offshore Oil Corporation), is introduced by the petroleum administrationat the eastern South China sea. The system adopts the real-time database and relationaldatabase to collect the scattered structured data, such as evidence information of offshoreoil and gas production facilities personnel, on-site hidden danger information andincident investigation report. Then a unified secure data center platform is established forevery operating area and production site, and the critical safety data of production sitescan be centrally managed. This system has the functions of lawful real-time supervisionof personnel qualification, online supervision and trend analysis of hidden dangers, andcentralized management and sharing of incident investigation report. By applying theMES system in security management, the process of safety service becomes standardizedand modularized, the management process becomes normalized, and the efficiency andeffect of overall management is improved. | Yong Chen Lei Cui Chong Wang | 2019 | Journal of Quantum Computing2019，1，1： | 0 |
| 17 | Online News Sentiment Classification Using DistilBERT显示文摘The ability of pre-trained BERT model to achieve outstanding performances on many Natural Language Processing(NLP)tasks has attracted the attention of researchers in recent times.However,the huge computational and memory requirements have hampered its widespread deployment on devices with limited resources.The concept of knowledge distillation has shown to produce smaller and faster distilled models with less trainable parameters and intended for resource-constrained environments.The distilled models can be fine-tuned with great performance on a wider range of tasks,such as sentiment classification.This paper evaluates the performance of DistilBERT model and other pre-canned text classifiers on a Covid-19 online news binary classification dataset.The analysis shows that despite having fewer trainable parameters than the BERT-based model,the DistilBERT model achieved an accuracy of 0.94 on the validation set after only two training epochs.The paper also highlights the usefulness of the ktrain library in facilitating the building,training,and application of state-of-the-art Machine Learning and Deep Learning models. | Samuel Kofi Akpatsa Hang Lei Xiaoyu Li Victor-Hillary Kofi Setornyo Obeng Ezekiel Mensah Martey Prince Clement Addo Duncan Dodzi Fiawoo | 2022 | Journal of Quantum Computing2022，4，1： | 0 |
| 18 | A Top-down Method of Extraction Entity Relationship Triples and Obtaining Annotated Data显示文摘The extraction of entity relationship triples is very important to build a knowledge graph(KG),meanwhile,various entity relationship extraction algorithms are mostly based on data-driven,especially for the current popular deep learning algorithms.Therefore,obtaining a large number of accurate triples is the key to build a good KG as well as train a good entity relationship extraction algorithm.Because of business requirements,this KG’s application field is determined and the experts’opinions also must be satisfied.Considering these factors we adopt the top-down method which refers to determining the data schema firstly,then filling the specific data according to the schema.The design of data schema is the top-level design of KG,and determining the data schema according to the characteristics of KG is equivalent to determining the scope of data’s collection and the mode of data’s organization.This method is generally suitable for the construction of domain KG.This article proposes a fast and efficient method to extract the topdown type KG’s triples in social media with the help of structured data in the information box on the right side of the related encyclopedia webpage.At the same time,based on the obtained triples,a data labeling method is proposed to obtain sufficiently high-quality training data,using in various Natural Language Processing(NLP)information extraction algorithms’training. | Zhiqiang Hu Zheng Ma Jun Shi Zhipeng Li Xun Shao Yangzhao Yang Yong Liao Zhenyuan Gao Jie Zhang | 2022 | Journal of Quantum Computing2022，4，1： | 0 |
| 19 | Research on Rainfall Estimation Based on Improved Kalman Filter Algorithm显示文摘In order to solve the rainfall estimation error caused by various noise factors such as clutter,super refraction,and raindrops during the detection process of Doppler weather radar.This paper proposes to improve the rainfall estimation model of radar combined with rain gauge which calibrated by common Kalman filter.After data preprocessing,the radar data should be classified according to the precipitation intensity.And then,they are respectively substituted into the improved filter for calibration.The state noise variance Q(k)and the measurement noise variance R(k)can be adaptively calculated and updated according to the input observation data during this process.Then the optimal parameter value of each type of precipitation intensity can be obtained.The state noise variance Q(k)and the measurement noise variance R(k)could be assigned optimal values when filtering the remaining data.This rainfall estimation based on semiadaptive Kalman filter calibration not only improves the accuracy of rainfall estimation,but also greatly reduces the amount of calculation.It avoids errors caused by repeated calculations,and improves the efficiency of the rainfall estimation at the same time. | Wen Zhang Wei Fang Xue leiJia Victor S.Sheng | 2022 | Journal of Quantum Computing2022，4，1： | 0 |
| 20 | Research on Service Function Chain Orchestrating Algorithm Based on SDN and NFV显示文摘Software defined network(SDN)and network function virtualization(NFV)have become a new paradigm of a new generation of network architecture.SDN and NFV can effectively improve the flexibility of deploying and managing service function chains(SFCs).By combining SDN and NFV and applying them to the resource orchestration problem of SFC deployment,the three-tier architecture consisting of SDN controller,network function virtualization and physical underlying computing resource layer in the process of heterogeneous network resource mapping is considered.And an optimization algorithm for active control resources based on SDN and NFV is proposed.Firstly,the user’s utility is modeled by the multistandard aggregated multi-criteria utility algorithm,and the optimization goal is transformed into the problem of maximizing the user’s utility.Then the controller,based on the algorithm’s prediction of the future state and realtime monitoring of the network utilization,makes decisions and issues control commands for the arriving SFC requests,based on which it occupies the underlying resources held by the virtualized network function(VNF).The simulation results show that,compared with the static timing resource allocation algorithm,the active control resource deployment algorithm proposed in the article has better performance in terms of resource utilization,acceptance rate,and user creation utility. | Yuning Jia Yu Gong Yifei Wei | 2022 | Journal of Quantum Computing2022，4，1： | 0 |
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