Track finding is a complex optimization problem,originally introduced in particle physics for the reconstruction of the trajectories of particles.A track is typically composed of several consecutive segments,which tog...Track finding is a complex optimization problem,originally introduced in particle physics for the reconstruction of the trajectories of particles.A track is typically composed of several consecutive segments,which together form a smooth curve without any bifurcations.In this paper,we investigate various modeling approaches to assess their effectiveness and impact when applied to track finding,using both quantum and classical methods.We present implementations of three classical models using CPLEX,two quantum models on actual D-Wave quantum computers,and one quantummodel on a D-Wave simulator.The results show that,while CPLEX provides better results than D-Wave on small instances,D-Wave is able to propose solutions in shorter computation times for large instances,although the gap with the optimal solution tends to increase.To the best of our knowledge,this is the first numerical study comparing a non-quantum approach based on classical algorithms(Simplex and Branch and Bound)used in commercial software with a quantum approach offered by D-Wave.The results do not show the quantum supremacy typically expected,but they do demonstrate that quantum solutions can be competitive with classical approaches,and even more efficient than some classical modeling and solving methods.展开更多
Quantum search has emerged as one of the most promising fields in quantum computing.Stateof-the-art quantum search algorithms enable the search for specific elements in a distribution by monotonically increasing the d...Quantum search has emerged as one of the most promising fields in quantum computing.Stateof-the-art quantum search algorithms enable the search for specific elements in a distribution by monotonically increasing the density of these elements relative to the rest of the distribution.These kinds of algorithms demonstrate a theoretical quadratic speed-up on the number of queries compared to classical search algorithms in unstructured spaces.Unfortunately,the major part of the existing literature applies quantum search to problems whose size grows exponentially with the input size without exploiting any specific problem structure,rendering this kind of approach not exploitable in real industrial problems.In contrast,this work proposes exploiting specific constraints of an outage planning problem,consisting in setting outage dates of production units under specific fuel management constraints and resource constraints limiting the number of outages in parallel,to build an initial superposition of states with size almost quadratically increasing as a function of the problem size.This state space reduction,inspired by the quantum walk algorithm,constructs a state superposition corresponding to all paths in a state-graph,embedding spacing constraints between outages.Our numerical results on quantum emulators highlight the potential of the statespace reduction approach.In our simplified use case,the number of iterations required to reach a 90% probability of measuring a feasible solution is reduced by a factor between 2 and 4.More importantly,the squared ratio between the number of possible configurations and the number of valid solutions shifts from exponential to linear behavior,demonstrating that the quadratic speedup offered by Grover-based algorithms becomes sufficient in this setting.While these results are based on a simplified scenario and further investigation is needed to generalize them to large-scale industrial problems,they illustrate the promise of structure-aware initialization in significantly improving the efficiency of quantum search by focusing on a smaller,more relevant solution space.展开更多
Distributed Quantum Computing(DQC)provides a means for scaling available quantum computation by interconnecting multiple quantum processor units(QPUs).A key challenge in this domain is efficiently allocating logical q...Distributed Quantum Computing(DQC)provides a means for scaling available quantum computation by interconnecting multiple quantum processor units(QPUs).A key challenge in this domain is efficiently allocating logical qubits from quantum circuits to the physical qubits within QPUs,a task known to be NP-hard.Traditional approaches,primarily focused on graph partitioning strategies,have sought to reduce the number of required Bell pairs for executing non-local CNOT operations,a form of gate teleportation.However,these methods have limitations in terms of efficiency and scalability.Addressing this,our work jointly considers gate and qubit teleportations introducing a novel meta-heuristic algorithm to minimise the network cost of executing a quantum circuit.By allowing dynamic reallocation of qubits along with gate teleportations during circuit execution,our method significantly enhances the overall efficacy and potential scalability of DQC frameworks.In our numerical analysis,we demonstrate that integrating qubit teleportations into our genetic algorithm for optimizing circuit blocking reduces the required resources,specifically the number of EPR pairs,compared to traditional graph partitioning methods.Our results,derived fromboth benchmark and randomly generated circuits,show that as circuit complexity increases—demanding more qubit teleportations—our approach effectively optimises these teleportations throughout the execution,thereby enhancing performance through strategic circuit partitioning.This is a step forward in the pursuit of a global quantum compiler which will ultimately enable the efficient use of a‘quantum data center’in the future.展开更多
This paper investigates Windfarm Layout Optimization(WFLO),where we formulate turbine placement considering wake effects as a Quadratic Unconstrained Binary Optimization(QUBO)problem.Wind energy plays a critical role ...This paper investigates Windfarm Layout Optimization(WFLO),where we formulate turbine placement considering wake effects as a Quadratic Unconstrained Binary Optimization(QUBO)problem.Wind energy plays a critical role in the transition toward sustainable power systems,but the optimal placement of turbines remains a challenging combinatorial problem due to complex wake interactions.With recent advances in quantum computing,there is growing interest in exploring whether hybrid quantum-classical methods can provide advantages for such computationally intensive tasks.We investigate solving the resulting QUBO problem using the Variational Quantum Eigensolver(VQE)implemented onQiskit’s quantum computer simulator,employing a quantum noise-free,gate-based circuit model.Three classical optimizers are discussed,with a detailed analysis of the two most effective approaches:Constrained Optimization BY Linear Approximation(COBYLA)and Bayesian Optimization(BO).We compare these simulated quantum results with two established classical optimization methods:Simulated Annealing(SA)and the Gurobi solver.The study focuses on 4×4 grid configurations(requiring 16 qubits),providing insights into near-term quantum algorithm applicability for renewable energy optimization.展开更多
In the rapidly evolving domain of quantum computing,Shor’s algorithm has emerged as a groundbreaking innovation with far-reaching implications for the field of cryptographic security.However,the efficacy of Shor’s a...In the rapidly evolving domain of quantum computing,Shor’s algorithm has emerged as a groundbreaking innovation with far-reaching implications for the field of cryptographic security.However,the efficacy of Shor’s algorithm hinges on the critical step of determining the period,a process that poses a substantial computational challenge.This article explores innovative quantum optimization solutions that aim to enhance the efficiency of Shor’s period finding algorithm.The article focuses on quantum development environments,such as Qiskit and Cirq.A detailed analysis is conducted on three notable tools:Qiskit Transpiler,BQSKit,and Mitiq.The performance of these tools is evaluated in terms of execution time,precision,resource utilization,the number of quantum gates,circuit synthesis optimization,error mitigation,and qubit fidelity.Through rigorous case studies,we highlight the strengths and limitations of these tools,shedding light on their potential impact on integer factorization and cybersecurity.Our findings underscore the importance of quantum optimization and lay the foundation for future developments in quantum algorithmic enhancements,particularly within the Qiskit and Cirq quantum development environments.展开更多
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 b...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.展开更多
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 prepara...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.展开更多
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 th...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.展开更多
AGRASP-based algorithm called T_GRASP for avoiding typhoon route optimization is suggested to increase the security and effectiveness of ship navigation.One of the worst natural calamities that can disrupt a ship’s n...AGRASP-based algorithm called T_GRASP for avoiding typhoon route optimization is suggested to increase the security and effectiveness of ship navigation.One of the worst natural calamities that can disrupt a ship’s navigation and result in numerous safety mishaps is a typhoon.Currently,the captains manually review the collected weather data and steer clear of typhoons using their navigational expertise.The distribution of heavy winds andwaves produced by the typhoon also changes dynamically as a result of the surrounding large-scale air pressure distribution,which significantly enhances the challenge of the captain’s preparation for avoiding typhoon navigation.It is now necessary to find a solution to the challenge of designing a highsafety and effective ship navigation path to avoid typhoons.The T_GRASP algorithm is suggested to optimize the candidate set’s structure based on the GRASP algorithm’s frame.The algorithm can guarantee the safety of the ship to avoid typhoons and assure high route efficiency by using the lowest risk function,the shortest sailing time,and the least fuel consumption as the objective functions and the ship speed and highest safety as the model constraints.The outcomes of the simulation demonstrate the superiority of the suggested T_GRASP algorithm over both the conventional A∗algorithm and the ant colony algorithm.In addition to addressing issues with the traditional A∗algorithm,like its wide search space and poor efficiency,the proposed algorithm also addresses issues with the ant colony algorithm,like its excessive iterations and sluggish convergence.展开更多
In this paper,we propose a new protocol designed for quantum private comparison(QPC).This new protocol utilizes the hyperentanglement as the quantum resource and introduces a semi-honest third party(TP)to achieve the ...In this paper,we propose a new protocol designed for quantum private comparison(QPC).This new protocol utilizes the hyperentanglement as the quantum resource and introduces a semi-honest third party(TP)to achieve the objective.This protocol’s quantum carrier is a hyperentangled three-photon GHZ state in 2 degrees of freedom(DOF),which could have 64 combinations.The TP can decide which combination to use based on the shared key information provided from a quantum key distribution(QKD)protocol.By doing so,the security of the protocol can be improved further.Decoy photon technology is also used as another means of security and checks if the transmission in the quantum channel is secure or not before sending the quantum carrier.The proposed protocol is proved to be able to fend off various kinds of eavesdropping attacks.In addition,the new QPC protocol also can compare secret inputs securely and efficiently.展开更多
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...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.展开更多
Most existing blockchain schemes are based on the design concept“openness and transparency”to realize data security,which usually require transaction data to be presented in the form of plaintext.However,it inevitab...Most existing blockchain schemes are based on the design concept“openness and transparency”to realize data security,which usually require transaction data to be presented in the form of plaintext.However,it inevitably brings the issues with respect to data privacy and operating performance.In this paper,we proposed a novel blockchain scheme called Cipherchain,which can process and maintain transaction data in the form of ciphertext while the characteristics of immutability and auditability are guaranteed.Specifically in our scheme,transactions can be encrypted locally based on a searchable encryption scheme called multi-user public key encryption with conjunctive keyword search(mPECK),and can be accessed by multiple specific participants after appended to the globally consistent distributed ledger.By introducing execution-consensus-update paradigm of transaction flow,Cipherchain cannot only make it possible for transaction data to exist in the form of ciphertext,but also guarantee the overall system performance not greatly affected by cryptographic operations and other local execution work.In addition,Cipherchain is a promising scheme to realize the technology combination of“blockchain+cloud computing”and“permissioned blockchain+public blockchain”.展开更多
Due to the huge amount of increasing data, the requirements of people forelectronic products such as mobile phones, tablets, and notebooks are constantlyimproving. The development and design of various software applic...Due to the huge amount of increasing data, the requirements of people forelectronic products such as mobile phones, tablets, and notebooks are constantlyimproving. The development and design of various software applications attach greatimportance to users’ experiences. The rationalized UI design should allow a user not onlyenjoy the visual design experience of the new product but also operating it morepleasingly. This process is to enhance the attractiveness and performance of the newproduct and thus to promote the active usage and consuming conduct of users. In thispaper, an UI design optimization strategy for general APP in the big data environment isproposed to get better user experience while effectively obtaining information. Anexperimental example of a library APP is designed to optimize the user experience. Theexperimental results show that the user-centered UI design is the core of optimization,and user portrait based on big data platforms is the key to UI design.展开更多
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,...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.展开更多
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 us...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.展开更多
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)...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.展开更多
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 clu...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.展开更多
In recent years,machine learning technology has been widely used for timely network attack detection and classification.However,due to the large number of network traffic and the complex and variable nature of malicio...In recent years,machine learning technology has been widely used for timely network attack detection and classification.However,due to the large number of network traffic and the complex and variable nature of malicious attacks,many challenges have arisen in the field of network intrusion detection.Aiming at the problem that massive and high-dimensional data in cloud computing networks will have a negative impact on anomaly detection,this paper proposes a Bi-LSTM method based on attention mechanism,which learns by transmitting IDS data to multiple hidden layers.Abstract information and high-dimensional feature representation in network data messages are used to improve the accuracy of intrusion detection.In the experiment,we use the public data set KDD-Cup 99 for verification.The experimental results show that the model can effectively detect unpredictable malicious behaviors under the current network environment,improve detection accuracy and reduce false positive rate compared with traditional intrusion detection methods.展开更多
In the era of big data,the general public is more likely to access big data,but they wouldn’t like to analyze the data.Therefore,the traditional data visualization with certain professionalism is not easy to be accep...In the era of big data,the general public is more likely to access big data,but they wouldn’t like to analyze the data.Therefore,the traditional data visualization with certain professionalism is not easy to be accepted by the general public living in the fast pace.Under this background,a new general visualization method for dynamic time series data emerges as the times require.Time series data visualization organizes abstract and hard-to-understand data into a form that is easily understood by the public.This method integrates data visualization into short videos,which is more in line with the way people get information in modern fast-paced lifestyles.The modular approach also facilitates public participation in production.This paper summarizes the dynamic visualization methods of time series data ranking,studies the relevant literature,shows its value and existing problems,and gives corresponding suggestions and future research prospects.展开更多
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...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.展开更多
文摘Track finding is a complex optimization problem,originally introduced in particle physics for the reconstruction of the trajectories of particles.A track is typically composed of several consecutive segments,which together form a smooth curve without any bifurcations.In this paper,we investigate various modeling approaches to assess their effectiveness and impact when applied to track finding,using both quantum and classical methods.We present implementations of three classical models using CPLEX,two quantum models on actual D-Wave quantum computers,and one quantummodel on a D-Wave simulator.The results show that,while CPLEX provides better results than D-Wave on small instances,D-Wave is able to propose solutions in shorter computation times for large instances,although the gap with the optimal solution tends to increase.To the best of our knowledge,this is the first numerical study comparing a non-quantum approach based on classical algorithms(Simplex and Branch and Bound)used in commercial software with a quantum approach offered by D-Wave.The results do not show the quantum supremacy typically expected,but they do demonstrate that quantum solutions can be competitive with classical approaches,and even more efficient than some classical modeling and solving methods.
文摘Quantum search has emerged as one of the most promising fields in quantum computing.Stateof-the-art quantum search algorithms enable the search for specific elements in a distribution by monotonically increasing the density of these elements relative to the rest of the distribution.These kinds of algorithms demonstrate a theoretical quadratic speed-up on the number of queries compared to classical search algorithms in unstructured spaces.Unfortunately,the major part of the existing literature applies quantum search to problems whose size grows exponentially with the input size without exploiting any specific problem structure,rendering this kind of approach not exploitable in real industrial problems.In contrast,this work proposes exploiting specific constraints of an outage planning problem,consisting in setting outage dates of production units under specific fuel management constraints and resource constraints limiting the number of outages in parallel,to build an initial superposition of states with size almost quadratically increasing as a function of the problem size.This state space reduction,inspired by the quantum walk algorithm,constructs a state superposition corresponding to all paths in a state-graph,embedding spacing constraints between outages.Our numerical results on quantum emulators highlight the potential of the statespace reduction approach.In our simplified use case,the number of iterations required to reach a 90% probability of measuring a feasible solution is reduced by a factor between 2 and 4.More importantly,the squared ratio between the number of possible configurations and the number of valid solutions shifts from exponential to linear behavior,demonstrating that the quadratic speedup offered by Grover-based algorithms becomes sufficient in this setting.While these results are based on a simplified scenario and further investigation is needed to generalize them to large-scale industrial problems,they illustrate the promise of structure-aware initialization in significantly improving the efficiency of quantum search by focusing on a smaller,more relevant solution space.
文摘Distributed Quantum Computing(DQC)provides a means for scaling available quantum computation by interconnecting multiple quantum processor units(QPUs).A key challenge in this domain is efficiently allocating logical qubits from quantum circuits to the physical qubits within QPUs,a task known to be NP-hard.Traditional approaches,primarily focused on graph partitioning strategies,have sought to reduce the number of required Bell pairs for executing non-local CNOT operations,a form of gate teleportation.However,these methods have limitations in terms of efficiency and scalability.Addressing this,our work jointly considers gate and qubit teleportations introducing a novel meta-heuristic algorithm to minimise the network cost of executing a quantum circuit.By allowing dynamic reallocation of qubits along with gate teleportations during circuit execution,our method significantly enhances the overall efficacy and potential scalability of DQC frameworks.In our numerical analysis,we demonstrate that integrating qubit teleportations into our genetic algorithm for optimizing circuit blocking reduces the required resources,specifically the number of EPR pairs,compared to traditional graph partitioning methods.Our results,derived fromboth benchmark and randomly generated circuits,show that as circuit complexity increases—demanding more qubit teleportations—our approach effectively optimises these teleportations throughout the execution,thereby enhancing performance through strategic circuit partitioning.This is a step forward in the pursuit of a global quantum compiler which will ultimately enable the efficient use of a‘quantum data center’in the future.
文摘This paper investigates Windfarm Layout Optimization(WFLO),where we formulate turbine placement considering wake effects as a Quadratic Unconstrained Binary Optimization(QUBO)problem.Wind energy plays a critical role in the transition toward sustainable power systems,but the optimal placement of turbines remains a challenging combinatorial problem due to complex wake interactions.With recent advances in quantum computing,there is growing interest in exploring whether hybrid quantum-classical methods can provide advantages for such computationally intensive tasks.We investigate solving the resulting QUBO problem using the Variational Quantum Eigensolver(VQE)implemented onQiskit’s quantum computer simulator,employing a quantum noise-free,gate-based circuit model.Three classical optimizers are discussed,with a detailed analysis of the two most effective approaches:Constrained Optimization BY Linear Approximation(COBYLA)and Bayesian Optimization(BO).We compare these simulated quantum results with two established classical optimization methods:Simulated Annealing(SA)and the Gurobi solver.The study focuses on 4×4 grid configurations(requiring 16 qubits),providing insights into near-term quantum algorithm applicability for renewable energy optimization.
文摘In the rapidly evolving domain of quantum computing,Shor’s algorithm has emerged as a groundbreaking innovation with far-reaching implications for the field of cryptographic security.However,the efficacy of Shor’s algorithm hinges on the critical step of determining the period,a process that poses a substantial computational challenge.This article explores innovative quantum optimization solutions that aim to enhance the efficiency of Shor’s period finding algorithm.The article focuses on quantum development environments,such as Qiskit and Cirq.A detailed analysis is conducted on three notable tools:Qiskit Transpiler,BQSKit,and Mitiq.The performance of these tools is evaluated in terms of execution time,precision,resource utilization,the number of quantum gates,circuit synthesis optimization,error mitigation,and qubit fidelity.Through rigorous case studies,we highlight the strengths and limitations of these tools,shedding light on their potential impact on integer factorization and cybersecurity.Our findings underscore the importance of quantum optimization and lay the foundation for future developments in quantum algorithmic enhancements,particularly within the Qiskit and Cirq quantum development environments.
基金supported byNational Natural Science Foundation of China (Grant Nos. 61502101, 61501247,61672290 and 71461005)Natural Science Foundation of Jiangsu Province (Grant No.BK20171458)+2 种基金the Six Talent Peaks Project of Jiangsu Province (Grant No. 2015-XXRJ-013)the Practice Innovation Training Program Projects for Jiangsu College Students(Grant No. 201810300016Z)the Priority Academic Program Development ofJiangsu Higher Education Institutions (PAPD).
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.
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.
基金This work is supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(19KJB520028)the Collaborative Innovation Center of Jiangsu Maritime Institute。
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.
文摘AGRASP-based algorithm called T_GRASP for avoiding typhoon route optimization is suggested to increase the security and effectiveness of ship navigation.One of the worst natural calamities that can disrupt a ship’s navigation and result in numerous safety mishaps is a typhoon.Currently,the captains manually review the collected weather data and steer clear of typhoons using their navigational expertise.The distribution of heavy winds andwaves produced by the typhoon also changes dynamically as a result of the surrounding large-scale air pressure distribution,which significantly enhances the challenge of the captain’s preparation for avoiding typhoon navigation.It is now necessary to find a solution to the challenge of designing a highsafety and effective ship navigation path to avoid typhoons.The T_GRASP algorithm is suggested to optimize the candidate set’s structure based on the GRASP algorithm’s frame.The algorithm can guarantee the safety of the ship to avoid typhoons and assure high route efficiency by using the lowest risk function,the shortest sailing time,and the least fuel consumption as the objective functions and the ship speed and highest safety as the model constraints.The outcomes of the simulation demonstrate the superiority of the suggested T_GRASP algorithm over both the conventional A∗algorithm and the ant colony algorithm.In addition to addressing issues with the traditional A∗algorithm,like its wide search space and poor efficiency,the proposed algorithm also addresses issues with the ant colony algorithm,like its excessive iterations and sluggish convergence.
文摘In this paper,we propose a new protocol designed for quantum private comparison(QPC).This new protocol utilizes the hyperentanglement as the quantum resource and introduces a semi-honest third party(TP)to achieve the objective.This protocol’s quantum carrier is a hyperentangled three-photon GHZ state in 2 degrees of freedom(DOF),which could have 64 combinations.The TP can decide which combination to use based on the shared key information provided from a quantum key distribution(QKD)protocol.By doing so,the security of the protocol can be improved further.Decoy photon technology is also used as another means of security and checks if the transmission in the quantum channel is secure or not before sending the quantum carrier.The proposed protocol is proved to be able to fend off various kinds of eavesdropping attacks.In addition,the new QPC protocol also can compare secret inputs securely and efficiently.
基金supported by the National Key R&D Program of China,Grant No.2018YFA0306703.
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.
基金This work is supported by the NSFC(Grant Nos.61671087,61962009,61003287)the Fok Ying Tong Education Foundation(Grant No.131067)+4 种基金the Major Scientific and Technological Special Project of Guizhou Province(Grant No.20183001)the Foundation of State Key Laboratory of Public Big Data(Grant No.2018BDKFJJ018)CCF-Tencent Open Fund WeBank Special Funding(CCF-WebankRAGR20180104)the High-quality and Cutting-edge Disciplines Construction Project for Universities in Beijing(Internet Information,Communication University of China)the Fundamental Research Funds for the Central Universities,and the Fundamental Research Funds for the Central Universities No.2019XD-A02.
文摘Most existing blockchain schemes are based on the design concept“openness and transparency”to realize data security,which usually require transaction data to be presented in the form of plaintext.However,it inevitably brings the issues with respect to data privacy and operating performance.In this paper,we proposed a novel blockchain scheme called Cipherchain,which can process and maintain transaction data in the form of ciphertext while the characteristics of immutability and auditability are guaranteed.Specifically in our scheme,transactions can be encrypted locally based on a searchable encryption scheme called multi-user public key encryption with conjunctive keyword search(mPECK),and can be accessed by multiple specific participants after appended to the globally consistent distributed ledger.By introducing execution-consensus-update paradigm of transaction flow,Cipherchain cannot only make it possible for transaction data to exist in the form of ciphertext,but also guarantee the overall system performance not greatly affected by cryptographic operations and other local execution work.In addition,Cipherchain is a promising scheme to realize the technology combination of“blockchain+cloud computing”and“permissioned blockchain+public blockchain”.
基金Hunan Provincial Education Science 13th Five-Year Plan (Grant No.XJK016BXX001)Social Science Foundation of Hunan Province (Grant No.17YBA049)+1 种基金Open Foundation for the University Innovation Platform in the HunanProvince, grant number 16K013. This research work is implemented at the 2011Collaborative Innovation Center for Development and Utilization of Finance andEconomics Big Data Property, Universities of Hunan Province. Open project (Grant Nos.20181901CRP03, 20181901CRP04, 20181901CRP05)National Social Science Fund Project: Research on the Impact Mechanism of China’sCapital Space Flow on Regional Economic Development (Project No. 14BJL086).
文摘Due to the huge amount of increasing data, the requirements of people forelectronic products such as mobile phones, tablets, and notebooks are constantlyimproving. The development and design of various software applications attach greatimportance to users’ experiences. The rationalized UI design should allow a user not onlyenjoy the visual design experience of the new product but also operating it morepleasingly. This process is to enhance the attractiveness and performance of the newproduct and thus to promote the active usage and consuming conduct of users. In thispaper, an UI design optimization strategy for general APP in the big data environment isproposed to get better user experience while effectively obtaining information. Anexperimental example of a library APP is designed to optimize the user experience. Theexperimental results show that the user-centered UI design is the core of optimization,and user portrait based on big data platforms is the key to UI design.
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.
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.
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.
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.
基金This work is supported by the National Key R&D Program of China(2017YFB0802703)Major Scientific and Technological Special Project of Guizhou Province(20183001)+1 种基金Open Foundation of Guizhou Provincial Key VOLUME XX,2019 Laboratory of Public Big Data(2018BDKFJJ014)Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data(2018BDKFJJ019,2018BDKFJJ022).
文摘In recent years,machine learning technology has been widely used for timely network attack detection and classification.However,due to the large number of network traffic and the complex and variable nature of malicious attacks,many challenges have arisen in the field of network intrusion detection.Aiming at the problem that massive and high-dimensional data in cloud computing networks will have a negative impact on anomaly detection,this paper proposes a Bi-LSTM method based on attention mechanism,which learns by transmitting IDS data to multiple hidden layers.Abstract information and high-dimensional feature representation in network data messages are used to improve the accuracy of intrusion detection.In the experiment,we use the public data set KDD-Cup 99 for verification.The experimental results show that the model can effectively detect unpredictable malicious behaviors under the current network environment,improve detection accuracy and reduce false positive rate compared with traditional intrusion detection methods.
基金This research is funded by the Open Foundation for the University Innovation Platform in the Hunan Province,Grant No.18K103Hunan Provincial Natural Science Foundation of China,Grant No.2017JJ20162016 Science Research Project of Hunan Provincial Department of Education,Grant No.16C0269.This research work is implemented at the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province.Open project,Grant Nos.20181901CRP03,20181901CRP04,20181901CRP05 National Social Science Fund Project:Research on the Impact Mechanism of China’s Capital Space Flow on Regional Economic Development(Project No.14BJL086).
文摘In the era of big data,the general public is more likely to access big data,but they wouldn’t like to analyze the data.Therefore,the traditional data visualization with certain professionalism is not easy to be accepted by the general public living in the fast pace.Under this background,a new general visualization method for dynamic time series data emerges as the times require.Time series data visualization organizes abstract and hard-to-understand data into a form that is easily understood by the public.This method integrates data visualization into short videos,which is more in line with the way people get information in modern fast-paced lifestyles.The modular approach also facilitates public participation in production.This paper summarizes the dynamic visualization methods of time series data ranking,studies the relevant literature,shows its value and existing problems,and gives corresponding suggestions and future research prospects.
基金This work is supported by the National Key R&D Program of China(2017YFB0802703)Major Scientific and Technological Special Project of Guizhou Province(20183001)+2 种基金Open Foundation of Guizhou Provincial Key VOLUME XX,2019 Laboratory of Public Big Data(2018BDKFJJ014)Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data(2018BDKFJJ019)Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data(2018BDKFJJ022).
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.
