By using swap test,a quantum private comparison(QPC) protocol of arbitrary single qubit states with a semi-honest third party is proposed.The semi-honest third party(TP) is required to help two participants perform th...By using swap test,a quantum private comparison(QPC) protocol of arbitrary single qubit states with a semi-honest third party is proposed.The semi-honest third party(TP) is required to help two participants perform the comparison.She can record intermediate results and do some calculations in the whole process of the protocol execution,but she cannot conspire with any of participants.In the process of comparison,the TP cannot get two participants’ private information except the comparison results.According to the security analysis,the proposed protocol can resist both outsider attacks and participants’ attacks.Compared with the existing QPC protocols,the proposed one does not require any entanglement swapping technology,but it can compare two participants’ qubits by performing swap test,which is easier to implement with current technology.Meanwhile,the proposed protocol can compare secret integers.It encodes secret integers into the amplitude of quantum state rather than transfer them as binary representations,and the encoded quantum state is compared by performing the swap test.Additionally,the proposed QPC protocol is extended to the QPC of arbitrary single qubit states by using multi-qubit swap test.展开更多
In the objective world,how to deal with the complexity and uncertainty of big data efficiently and accurately has become the premise and key to machine learning.Fuzzy support vector machine(FSVM)not only deals with th...In the objective world,how to deal with the complexity and uncertainty of big data efficiently and accurately has become the premise and key to machine learning.Fuzzy support vector machine(FSVM)not only deals with the classifi-cation problems for training samples with fuzzy information,but also assigns a fuzzy membership degree to each training sample,allowing different training samples to contribute differently in predicting an optimal hyperplane to separate two classes with maximum margin,reducing the effect of outliers and noise,Quantum computing has super parallel computing capabilities and holds the pro-mise of faster algorithmic processing of data.However,FSVM and quantum com-puting are incapable of dealing with the complexity and uncertainty of big data in an efficient and accurate manner.This paper research and propose an efficient and accurate quantum fuzzy support vector machine(QFSVM)algorithm based on the fact that quantum computing can efficiently process large amounts of data and FSVM is easy to deal with the complexity and uncertainty problems.The central idea of the proposed algorithm is to use the quantum algorithm for solving linear systems of equations(HHL algorithm)and the least-squares method to solve the quadratic programming problem in the FSVM.The proposed algorithm can deter-mine whether a sample belongs to the positive or negative class while also achiev-ing a good generalization performance.Furthermore,this paper applies QFSVM to handwritten character recognition and demonstrates that QFSVM can be run on quantum computers,and achieve accurate classification of handwritten characters.When compared to FSVM,QFSVM’s computational complexity decreases expo-nentially with the number of training samples.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant No.62076042)the Key Research and Development Project of Sichuan Province,China(Grant Nos.2020YFG0307 and 2021YFSY0012)+2 种基金the Key Research and Development Project of Chengdu Municipality,China(Grant No.2019-YF05-02028-GX)the Innovation Team of Quantum Security Communication of Sichuan Province,China(Grant No.17TD0009)the Academic and Technical Leaders Training Funding Support Projects of Sichuan Province,China(Grant No.2016120080102643)。
文摘By using swap test,a quantum private comparison(QPC) protocol of arbitrary single qubit states with a semi-honest third party is proposed.The semi-honest third party(TP) is required to help two participants perform the comparison.She can record intermediate results and do some calculations in the whole process of the protocol execution,but she cannot conspire with any of participants.In the process of comparison,the TP cannot get two participants’ private information except the comparison results.According to the security analysis,the proposed protocol can resist both outsider attacks and participants’ attacks.Compared with the existing QPC protocols,the proposed one does not require any entanglement swapping technology,but it can compare two participants’ qubits by performing swap test,which is easier to implement with current technology.Meanwhile,the proposed protocol can compare secret integers.It encodes secret integers into the amplitude of quantum state rather than transfer them as binary representations,and the encoded quantum state is compared by performing the swap test.Additionally,the proposed QPC protocol is extended to the QPC of arbitrary single qubit states by using multi-qubit swap test.
基金supported by the National Natural Science Foundation of China(No.62076042)the Key Research and Development Project of Sichuan Province(No.2021YFSY0012,No.2020YFG0307,No.2021YFG0332)+3 种基金the Science and Technology Innovation Project of Sichuan(No.2020017)the Key Research and Development Project of Chengdu(No.2019-YF05-02028-GX)the Innovation Team of Quantum Security Communication of Sichuan Province(No.17TD0009)the Academic and Technical Leaders Training Funding Support Projects of Sichuan Province(No.2016120080102643).
文摘In the objective world,how to deal with the complexity and uncertainty of big data efficiently and accurately has become the premise and key to machine learning.Fuzzy support vector machine(FSVM)not only deals with the classifi-cation problems for training samples with fuzzy information,but also assigns a fuzzy membership degree to each training sample,allowing different training samples to contribute differently in predicting an optimal hyperplane to separate two classes with maximum margin,reducing the effect of outliers and noise,Quantum computing has super parallel computing capabilities and holds the pro-mise of faster algorithmic processing of data.However,FSVM and quantum com-puting are incapable of dealing with the complexity and uncertainty of big data in an efficient and accurate manner.This paper research and propose an efficient and accurate quantum fuzzy support vector machine(QFSVM)algorithm based on the fact that quantum computing can efficiently process large amounts of data and FSVM is easy to deal with the complexity and uncertainty problems.The central idea of the proposed algorithm is to use the quantum algorithm for solving linear systems of equations(HHL algorithm)and the least-squares method to solve the quadratic programming problem in the FSVM.The proposed algorithm can deter-mine whether a sample belongs to the positive or negative class while also achiev-ing a good generalization performance.Furthermore,this paper applies QFSVM to handwritten character recognition and demonstrates that QFSVM can be run on quantum computers,and achieve accurate classification of handwritten characters.When compared to FSVM,QFSVM’s computational complexity decreases expo-nentially with the number of training samples.
