In this paper,focus has been given to design and implement signed binary subtraction in quantum logic.Since the type of operand may be positive or negative,therefore a novel algorithm has been developed to detect the ...In this paper,focus has been given to design and implement signed binary subtraction in quantum logic.Since the type of operand may be positive or negative,therefore a novel algorithm has been developed to detect the type of operand and as per the selection of the type of operands,separate design techniques have been developed to make the circuit compact and work very efficiently.Two separate methods have been shown in the paper to perform the signed subtraction.The results show promising for the second method in respect of ancillary input count and garbage output count but at the cost of quantum cost.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
Due to the increase in the types of business and equipment in telecommunications companies,the performance index data collected in the operation and maintenance process varies greatly.The diversity of index data makes...Due to the increase in the types of business and equipment in telecommunications companies,the performance index data collected in the operation and maintenance process varies greatly.The diversity of index data makes it very difficult to perform high-precision capacity prediction.In order to improve the forecasting efficiency of related indexes,this paper designs a classification method of capacity index data,which divides the capacity index data into trend type,periodic type and irregular type.Then for the prediction of trend data,it proposes a capacity index prediction model based on Recurrent Neural Network(RNN),denoted as RNN-LSTM-LSTM.This model includes a basic RNN,two Long Short-Term Memory(LSTM)networks and two Fully Connected layers.The experimental results show that,compared with the traditional Holt-Winters,Autoregressive Integrated Moving Average(ARIMA)and Back Propagation(BP)neural network prediction model,the mean square error(MSE)of the proposed RNN-LSTM-LSTM model are reduced by 11.82%and 20.34%on the order storage and data migration,which has greatly improved the efficiency of trend-type capacity index prediction.展开更多
The utilization of quantum states for the representation of information and the advances in machine learning is considered as an efficient way of modeling the working of complex systems.The states of mind or judgment ...The utilization of quantum states for the representation of information and the advances in machine learning is considered as an efficient way of modeling the working of complex systems.The states of mind or judgment outcomes are highly complex phenomena that happen inside the human body.Decoding these states is significant for improving the quality of technology and providing an impetus to scientific research aimed at understanding the functioning of the human mind.One of the key advantages of quantum wave-functions over conventional classical models is the existence of configurable hidden variables,which provide more data density due to its exponential state-space growth.These hidden variables correspond to the amplitudes of each probable state of the system and allow for the modeling of various intricate aspects of measurable and observable physical quantities.This makes the quantum wave-functions powerful and felicitous to model cognitive states of the human mind,as it inherits the ability to efficiently couple the current context with past experiences temporally and spatially to approach an appropriate future cognitive state.This paper implements and compares some techniques like Variational Quantum Classifiers(VQC),quantum annealing classifiers,and hybrid quantum-classical neural networks,to harness the power of quantum computing for processing cognitive states of the mind by making use of EEG data.It also introduces a novel pipeline by logically combining some of the aforementioned techniques,to predict future cognitive responses.The preliminary results of these approaches are presented and are very encouraging with upto 61.53%validation accuracy.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
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 communi...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.展开更多
In the field of public security,the standardized use of police equipment can better assist the public security police in performing their duties.With the advancement of science and technology of the times,police equip...In the field of public security,the standardized use of police equipment can better assist the public security police in performing their duties.With the advancement of science and technology of the times,police equipment is also constantly developing,and more and more new types of police equipment have appeared.Nowadays,there are a large number and variety of police equipment,and public security police are facing the challenge of mastering and updating equipment knowledge.This article builds a knowledge base of police equipment based on the knowledge of opening source data on the Internet,uses a variety of databases to store knowledge,and presents knowledge of police equipment in the formof knowledge queries,innovatively applying the concept of knowledge base to police Knowledge of equipment in science.Knowledge is presented in three modules:encyclopedia knowledge,question answering knowledge,and product knowledge,which can answer the description questions and experience questions of police equipment.The query accuracy rate for the physical problems of police equipment is 70%,and the query accuracy rate of the experience problems of police equipment is 90%.The design and implementation of the system solves the problem of knowledge fusion in the field of police equipment in opening source data,and at the same time can help the police to sort out the knowledge structure of police equipment,fully understand the police equipment,and better serve public security Field related work.展开更多
In order to improve the anti-noise performance of quantum teleportation,this paper proposes a novel dynamic quantum anti-noise scheme based on the quantum teleportation which transmits single qubit state using Bell st...In order to improve the anti-noise performance of quantum teleportation,this paper proposes a novel dynamic quantum anti-noise scheme based on the quantum teleportation which transmits single qubit state using Bell state.Considering that quantum noise only acts on the transmitted qubit,i.e.,the entangled state that Alice and Bob share in advance is affected by the noise,thus affecting the final transmission result.In this paper,a method for dynamically adjusting the shared entangled state according to the noise environment is proposed.By calculating the maximum fidelity of the output state to determine the shared entangled state,which makes the quantum teleportation be affected by the noise as little as possible.This paper calculates the fidelity of teleportation under four kinds of channel noise(amplitude damping,phase damping,bit flip and depolarizing noise).The results show that the scheme has a suppression effect on phase damping,bit flip and depolarizing noise under certain conditions.When the noise intensity is larger,the optimized efficiency is better.展开更多
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 transmissi...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.展开更多
In the detection process of classic radars such as radar/lidar,the detection performance will be weakened due to the presence of background noise and loss.The quantum illumination protocol can use the spatial correlat...In the detection process of classic radars such as radar/lidar,the detection performance will be weakened due to the presence of background noise and loss.The quantum illumination protocol can use the spatial correlation between photon pairs to improve image quality and enhance radar detection performance,even in the presence of loss and noise.Based on this quantum illumination LIDAR,a theoretic scheme is developed for the detection and tracking of moving targets,and the trajectory of the object is analyzed.Illuminated by the quantum light source as Spontaneous Parametric Down-Conversion(SPDC),an opaque target can be identified from the background in the presence of strong noise.The static objects obtained by classical and quantum illumination are compared,respectively,and the advantages of quantum illumination are verified.The moving objects are taken at appropriate intervals to obtain the images of the moving objects,then the images are visualized as dynamic images,and the three-frame difference method is used to obtain the target contour.Finally,the image is performed by a series of processing on to obtain the trajectory of the target object.Several different motion situations are analyzed separately,and compared with the set object motion trajectory,which proves the effectiveness of the scheme.This scheme has potential practical application value.展开更多
With the accelerating process of social informatization,our personal information security and Internet sites,etc.,have been facing a series of threats and challenges.Recently,well-developed neural network has seen gre...With the accelerating process of social informatization,our personal information security and Internet sites,etc.,have been facing a series of threats and challenges.Recently,well-developed neural network has seen great advancement in natural language processing and computer vision,which is also adopted in intrusion detection.In this research,a hybrid model integrating Multi-Scale Convolutional Neural Network and Long Short-term Memory Network(MSCNN-LSTM)is designed to conduct the intrusion detection.Multi-Scale Convolutional Neural Network(MSCNN)is used to extract the spatial characteristics of data sets.And Long Short-term Memory Network(LSTM)is responsible for processing the temporal characteristics.The data set used in this experiment is KDDCUP99 with different probability distributions in the training set and test set involving some newly emerging attack types,making the data more realistic.As a result,this type of data set is widely applied in the simulation experiment of intrusion detection.In this experiment,the assessment indices such as the accuracy rate,recall rate and F1 score are introduced to check the performance of this model.展开更多
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 ...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.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
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
To solve the problem of hiding quantum information in simplified subsystems,Modi et al.[1]introduced the concept of quantum masking.Quantum masking is the encoding of quantum information by composite quantum states in...To solve the problem of hiding quantum information in simplified subsystems,Modi et al.[1]introduced the concept of quantum masking.Quantum masking is the encoding of quantum information by composite quantum states in such a way that the quantum information is hidden to the subsystem and spreads to the correlation of the composite systems.The concept of quantum masking was developed along with a new quantum impossibility theorem,the quantum no-masking theorem.The question of whether a quantum state can be masked has been studied by many people from the perspective of the types of quantum states,the number of masking participants,and error correction codes.Others have studied the relationships between maskable quantum states,the deterministic and probabilistic masking of quantum states,and the problem of probabilistic masking.Quantum masking techniques have been shown to outperform previous strategies in quantum bit commitment,quantum multi-party secret sharing,and so on.展开更多
Quantum algorithms for unstructured search problems rely on the preparation of a uniform superposition,traditionally achieved through Hadamard gates.However,this incidentally creates an auxiliary search space consisti...Quantum algorithms for unstructured search problems rely on the preparation of a uniform superposition,traditionally achieved through Hadamard gates.However,this incidentally creates an auxiliary search space consisting of nonsensical answers that do not belong in the search space and reduce the efficiency of the algorithm due to the need to neglect,un-compute,or destructively interfere with them.Previous approaches to removing this auxiliary search space yielded large circuit depth and required the use of ancillary qubits.We have developed an optimized general solver for a circuit that prepares a uniformsuperposition of any N states whileminimizing depth andwithout the use of ancillary qubits.We showthat this algorithmis efficient,especially in its use of two wire gates,and that it has been verified on an IonQ quantum computer and through application to a quantum unstructured search algorithm.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
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
Language detection models based on system calls suffer from certain false negatives and detection blind spots.Hence,the normal behavior sequences of some malware applications for a short period can become malicious be...Language detection models based on system calls suffer from certain false negatives and detection blind spots.Hence,the normal behavior sequences of some malware applications for a short period can become malicious behavior within a certain time window.To detect such behaviors,we extract a multidimensional time distribution feature matrix on the basis of statistical analysis.This matrix mainly includes multidimensional time distribution features,multidimensional word pair correlation features,and multidimensional word frequency distribution features.A multidimensional time distribution model based on neural networks is built to detect the overall abnormal behavior within a given time window.Experimental evaluation is conducted using the ADFA-LD dataset.Accuracy,precision,and recall are used as the measurement indicators of the model.An accuracy rate of 95.26%and a recall rate of 96.11%are achieved.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
文摘In this paper,focus has been given to design and implement signed binary subtraction in quantum logic.Since the type of operand may be positive or negative,therefore a novel algorithm has been developed to detect the type of operand and as per the selection of the type of operands,separate design techniques have been developed to make the circuit compact and work very efficiently.Two separate methods have been shown in the paper to perform the signed subtraction.The results show promising for the second method in respect of ancillary input count and garbage output count but at the cost of quantum cost.
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supported by Research on Big Data Technology for New Generation Internet Operators(H04W180609)the second batch of Sichuan Science and Technology Service Industry Development Fund Projects in 2018(18KJFWSF0388).
文摘Due to the increase in the types of business and equipment in telecommunications companies,the performance index data collected in the operation and maintenance process varies greatly.The diversity of index data makes it very difficult to perform high-precision capacity prediction.In order to improve the forecasting efficiency of related indexes,this paper designs a classification method of capacity index data,which divides the capacity index data into trend type,periodic type and irregular type.Then for the prediction of trend data,it proposes a capacity index prediction model based on Recurrent Neural Network(RNN),denoted as RNN-LSTM-LSTM.This model includes a basic RNN,two Long Short-Term Memory(LSTM)networks and two Fully Connected layers.The experimental results show that,compared with the traditional Holt-Winters,Autoregressive Integrated Moving Average(ARIMA)and Back Propagation(BP)neural network prediction model,the mean square error(MSE)of the proposed RNN-LSTM-LSTM model are reduced by 11.82%and 20.34%on the order storage and data migration,which has greatly improved the efficiency of trend-type capacity index prediction.
文摘The utilization of quantum states for the representation of information and the advances in machine learning is considered as an efficient way of modeling the working of complex systems.The states of mind or judgment outcomes are highly complex phenomena that happen inside the human body.Decoding these states is significant for improving the quality of technology and providing an impetus to scientific research aimed at understanding the functioning of the human mind.One of the key advantages of quantum wave-functions over conventional classical models is the existence of configurable hidden variables,which provide more data density due to its exponential state-space growth.These hidden variables correspond to the amplitudes of each probable state of the system and allow for the modeling of various intricate aspects of measurable and observable physical quantities.This makes the quantum wave-functions powerful and felicitous to model cognitive states of the human mind,as it inherits the ability to efficiently couple the current context with past experiences temporally and spatially to approach an appropriate future cognitive state.This paper implements and compares some techniques like Variational Quantum Classifiers(VQC),quantum annealing classifiers,and hybrid quantum-classical neural networks,to harness the power of quantum computing for processing cognitive states of the mind by making use of EEG data.It also introduces a novel pipeline by logically combining some of the aforementioned techniques,to predict future cognitive responses.The preliminary results of these approaches are presented and are very encouraging with upto 61.53%validation accuracy.
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.
基金Supported by the National Natural Science Foundation of Chinaunder Grant Nos. 61501247, 61373131 and 61702277the Six Talent Peaks Project ofJiangsu Province (Grant No. 2015-XXRJ-013)+2 种基金Natural Science Foundation of JiangsuProvince (Grant No. BK20171458)he Natural Science Foundation of the HigherEducation Institutions of Jiangsu Province (China under Grant No. 16KJB520030)theNUIST Research Foundation for Talented Scholars under Grant No. 2015r014, PAPDand CICAEET funds.
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.
基金This work was supported by Ministry of public security technology research program[Grant No.2020JSYJC22ok]Fundamental Research Funds for the Central Universities(No.2021JKF215)+1 种基金Open Research Fund of the Public Security Behavioral Science Laboratory,People’s Public Security University of China(2020SYS03)Police and people build/share a smart community(PJ13-201912-0525).
文摘In the field of public security,the standardized use of police equipment can better assist the public security police in performing their duties.With the advancement of science and technology of the times,police equipment is also constantly developing,and more and more new types of police equipment have appeared.Nowadays,there are a large number and variety of police equipment,and public security police are facing the challenge of mastering and updating equipment knowledge.This article builds a knowledge base of police equipment based on the knowledge of opening source data on the Internet,uses a variety of databases to store knowledge,and presents knowledge of police equipment in the formof knowledge queries,innovatively applying the concept of knowledge base to police Knowledge of equipment in science.Knowledge is presented in three modules:encyclopedia knowledge,question answering knowledge,and product knowledge,which can answer the description questions and experience questions of police equipment.The query accuracy rate for the physical problems of police equipment is 70%,and the query accuracy rate of the experience problems of police equipment is 90%.The design and implementation of the system solves the problem of knowledge fusion in the field of police equipment in opening source data,and at the same time can help the police to sort out the knowledge structure of police equipment,fully understand the police equipment,and better serve public security Field related work.
基金This work was supported in part by the National Natural Science Foundation of China under Grant Nos.61373131,61671087,61601358,61501247,61672290,61303039,and Grant 61232016in part by the Six Talent Peaks Project of Jiangsu Province under Grant 2015-XXRJ-013+4 种基金in part by the Natural Science Foundation of Jiangsu Province under Grant BK20171458in part by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province,China,under Grant 16KJB520030in part by the Sichuan Youth Science and Technique Foundation under Grant 2017JQ0048in part by the NUIST Research Foundation for Talented Scholars under Grant 2015r014in part by the PAPD,and in part by the CICAEET funds.
文摘In order to improve the anti-noise performance of quantum teleportation,this paper proposes a novel dynamic quantum anti-noise scheme based on the quantum teleportation which transmits single qubit state using Bell state.Considering that quantum noise only acts on the transmitted qubit,i.e.,the entangled state that Alice and Bob share in advance is affected by the noise,thus affecting the final transmission result.In this paper,a method for dynamically adjusting the shared entangled state according to the noise environment is proposed.By calculating the maximum fidelity of the output state to determine the shared entangled state,which makes the quantum teleportation be affected by the noise as little as possible.This paper calculates the fidelity of teleportation under four kinds of channel noise(amplitude damping,phase damping,bit flip and depolarizing noise).The results show that the scheme has a suppression effect on phase damping,bit flip and depolarizing noise under certain conditions.When the noise intensity is larger,the optimized efficiency is better.
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.
基金supported by the National Key R&D Program of China,Grant No.2018YFA0306703.
文摘In the detection process of classic radars such as radar/lidar,the detection performance will be weakened due to the presence of background noise and loss.The quantum illumination protocol can use the spatial correlation between photon pairs to improve image quality and enhance radar detection performance,even in the presence of loss and noise.Based on this quantum illumination LIDAR,a theoretic scheme is developed for the detection and tracking of moving targets,and the trajectory of the object is analyzed.Illuminated by the quantum light source as Spontaneous Parametric Down-Conversion(SPDC),an opaque target can be identified from the background in the presence of strong noise.The static objects obtained by classical and quantum illumination are compared,respectively,and the advantages of quantum illumination are verified.The moving objects are taken at appropriate intervals to obtain the images of the moving objects,then the images are visualized as dynamic images,and the three-frame difference method is used to obtain the target contour.Finally,the image is performed by a series of processing on to obtain the trajectory of the target object.Several different motion situations are analyzed separately,and compared with the set object motion trajectory,which proves the effectiveness of the scheme.This scheme has potential practical application value.
文摘With the accelerating process of social informatization,our personal information security and Internet sites,etc.,have been facing a series of threats and challenges.Recently,well-developed neural network has seen great advancement in natural language processing and computer vision,which is also adopted in intrusion detection.In this research,a hybrid model integrating Multi-Scale Convolutional Neural Network and Long Short-term Memory Network(MSCNN-LSTM)is designed to conduct the intrusion detection.Multi-Scale Convolutional Neural Network(MSCNN)is used to extract the spatial characteristics of data sets.And Long Short-term Memory Network(LSTM)is responsible for processing the temporal characteristics.The data set used in this experiment is KDDCUP99 with different probability distributions in the training set and test set involving some newly emerging attack types,making the data more realistic.As a result,this type of data set is widely applied in the simulation experiment of intrusion detection.In this experiment,the assessment indices such as the accuracy rate,recall rate and F1 score are introduced to check the performance of this model.
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.
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.
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 was supported by the innovation and entrepreneurship training program of Nanjing University of Information Science&Technology(No.202010300212).
文摘To solve the problem of hiding quantum information in simplified subsystems,Modi et al.[1]introduced the concept of quantum masking.Quantum masking is the encoding of quantum information by composite quantum states in such a way that the quantum information is hidden to the subsystem and spreads to the correlation of the composite systems.The concept of quantum masking was developed along with a new quantum impossibility theorem,the quantum no-masking theorem.The question of whether a quantum state can be masked has been studied by many people from the perspective of the types of quantum states,the number of masking participants,and error correction codes.Others have studied the relationships between maskable quantum states,the deterministic and probabilistic masking of quantum states,and the problem of probabilistic masking.Quantum masking techniques have been shown to outperform previous strategies in quantum bit commitment,quantum multi-party secret sharing,and so on.
文摘Quantum algorithms for unstructured search problems rely on the preparation of a uniform superposition,traditionally achieved through Hadamard gates.However,this incidentally creates an auxiliary search space consisting of nonsensical answers that do not belong in the search space and reduce the efficiency of the algorithm due to the need to neglect,un-compute,or destructively interfere with them.Previous approaches to removing this auxiliary search space yielded large circuit depth and required the use of ancillary qubits.We have developed an optimized general solver for a circuit that prepares a uniformsuperposition of any N states whileminimizing depth andwithout the use of ancillary qubits.We showthat this algorithmis efficient,especially in its use of two wire gates,and that it has been verified on an IonQ quantum computer and through application to a quantum unstructured search algorithm.
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.
基金supported by the National Key Research and Development Program of China(No.2017YFB0801900).
文摘Language detection models based on system calls suffer from certain false negatives and detection blind spots.Hence,the normal behavior sequences of some malware applications for a short period can become malicious behavior within a certain time window.To detect such behaviors,we extract a multidimensional time distribution feature matrix on the basis of statistical analysis.This matrix mainly includes multidimensional time distribution features,multidimensional word pair correlation features,and multidimensional word frequency distribution features.A multidimensional time distribution model based on neural networks is built to detect the overall abnormal behavior within a given time window.Experimental evaluation is conducted using the ADFA-LD dataset.Accuracy,precision,and recall are used as the measurement indicators of the model.An accuracy rate of 95.26%and a recall rate of 96.11%are achieved.
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.
