In view of drastic possible changes in fuze environment tempera- ture,a kind of temperature autocompensated detecting circuit for the capaci- tance fuze is proposed.It provides a steady detected output when the envi- ...In view of drastic possible changes in fuze environment tempera- ture,a kind of temperature autocompensated detecting circuit for the capaci- tance fuze is proposed.It provides a steady detected output when the envi- ronment temperature varies from-50℃ to 65℃ and keeps a stable detecting sensitivity.Based on an analysis of the circuit,influence of the major param- eters of the oscillating circuit on the amplitude are explored.A few impor- tant controllable parameters affecting the circuit feature are found out.A parameter-control method is given in order to improve the circuit perfor- mance.展开更多
Under the Born-von-Karmann periodic boundary condition, we propose a quantization scheme for non-dissipative distributed parameter circuits (i.e. a uniform periodic transmission line). We find the unitary operator for...Under the Born-von-Karmann periodic boundary condition, we propose a quantization scheme for non-dissipative distributed parameter circuits (i.e. a uniform periodic transmission line). We find the unitary operator for diagonalizing the Hamiltonian of the uniform periodic transmission line. The unitary operator is expressed in a coordinate representation that brings convenience to deriving the density matrix rho(q,q',beta). The quantum fluctuations of charge and current at a definite temperature have been studied. It is shown that quantum fluctuations of distributed parameter circuits, which also have distributed properties, are related to both the circuit parameters and the positions and the mode of signals and temperature T. The higher the temperature is, the stronger quantum noise the circuit exhibits.展开更多
We study the dynamics of two electron spins in coupled quantum dots （CQDs） monitored by a quantum point contact （QPC） detector. Their quantum state can be measured by embedding the QPC in an LC circuit. We derive ...We study the dynamics of two electron spins in coupled quantum dots （CQDs） monitored by a quantum point contact （QPC） detector. Their quantum state can be measured by embedding the QPC in an LC circuit. We derive the Bloch-type rate equations of the reduced density matrix for CQDs. Special attention is paid to the numerical results for the weak measurement condintion under a strong Coulomb interaction. It is shown that the evolution of QPC current always follows that of electron occupation in the right dot. In addition, we find that the output voltage of the circuit can reflect the evolution of QPC current when the circuit and QPC are approximately equal in frequency. In particular, the wave shape of the output voltage can be improved by adjusting the circuit resonance frequency and bandwidth.展开更多
We report a resonant method to measure the wall capacitance（C_w） and solution resistance（R_S） in a capacitively coupled contactless conductivity detector（C^4D）.Under the typical operating conditions in capillary...We report a resonant method to measure the wall capacitance（C_w） and solution resistance（R_S） in a capacitively coupled contactless conductivity detector（C^4D）.Under the typical operating conditions in capillary electrophoresis（I.D.50μm,O.D. 360μm,electrode length of 4 mm,electrode gap of 1 mm,frequency of 200 kHz）,the values of C_w measured in 1 and 20 mmol/L NaCl solutions are 2.8 and 32 fF,which are only 1.1%and 12%of prediction by the equation in references,respectively.The value of R_S is less than the prediction in solutions withκ〈0.02 S/m.The response current of C^4D is due to the change in C_w because the total impedance of a C^4D is composed mainly by the impedance from C_w.展开更多
Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data mining.This paper proposes a quantum semi-supervised classif...Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data mining.This paper proposes a quantum semi-supervised classifier based on label propagation.Considering the difficulty of graph construction,we develop a variational quantum label propagation(VQLP)method.In this method,a locally parameterized quantum circuit is created to reduce the parameters required in the optimization.Furthermore,we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement,which has a shallower circuit depth and is more suitable for implementation on near-term quantum devices.We demonstrate the performance of the quantum semi-supervised classifier on the Iris data set,and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test classifier.This work opens a new path to quantum machine learning based on graphs.展开更多
Variational quantum algorithms are promising methods with the greatest potential to achieve quantum advantage,widely employed in the era of noisy intermediate-scale quantum computing.This study presents an advanced va...Variational quantum algorithms are promising methods with the greatest potential to achieve quantum advantage,widely employed in the era of noisy intermediate-scale quantum computing.This study presents an advanced variational hybrid algorithm(EVQLSE)that leverages both quantum and classical computing paradigms to address the solution of linear equation systems.Initially,an innovative loss function is proposed,drawing inspiration from the similarity measure between two quantum states.This function exhibits a substantial improvement in computational complexity when benchmarked against the variational quantum linear solver.Subsequently,a specialized parameterized quantum circuit structure is presented for small-scale linear systems,which exhibits powerful expressive capabilities.Through rigorous numerical analysis,the expressiveness of this circuit structure is quantitatively assessed using a variational quantum regression algorithm,and it obtained the best score compared to the others.Moreover,the expansion in system size is accompanied by an increase in the number of parameters,placing considerable strain on the training process for the algorithm.To address this challenge,an optimization strategy known as quantum parameter sharing is introduced,which proficiently minimizes parameter volume while adhering to exacting precision standards.Finally,EVQLSE is successfully implemented on a quantum computing platform provided by IBM for the resolution of large-scale problems characterized by a dimensionality of 220.展开更多
We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum circuits.In this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in cl...We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum circuits.In this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in classical convolutional neural networks,forming a new quantum convolutional layer to achieve unitary transformation of quantum states,enabling the model to more accurately extract hidden information from images.At the same time,we combine the classical fully connected layer with PQCs to form a new hybrid quantum-classical fully connected layer to further improve the accuracy of classification.Finally,we use the MNIST dataset to test the potential of the HQCCNN.The results indicate that the HQCCNN has good performance in solving classification problems.In binary classification tasks,the classification accuracy of numbers 5 and 7 is as high as 99.71%.In multivariate classification,the accuracy rate also reaches 98.51%.Finally,we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed.展开更多
In this paper, equivalent circuits for high frequency multi-winding magnetic components are derived from finite element （FE） computations. Lumped parameter models are first presented, based on previously published w...In this paper, equivalent circuits for high frequency multi-winding magnetic components are derived from finite element （FE） computations. Lumped parameter models are first presented, based on previously published work. All parameters of these circuits can be interpreted as the results of open and short-circuit tests on the transformer. Based on this consideration, numerical procedures are then proposed to derive frequency-dependent lumped parameters from FE simulations. By using an adequate formulation, parameters are directly obtained from the FE model degrees of freedom, without performing any volume integration in post-processing, which can be source of numerical errors. In this contribution, attention is paid on the modeling of magnetic coupling using inductances, and dissipative effects （winding and core losses） using resistances. The impact of conductor eddy currents on the circuit parameters is moreover studied in details. Instead of an analysis of the impact conductor eddy currents may have on the circuit parameters is moreover carried through.展开更多
It is a critical challenge for quantum machine learning to classify the datasets accurately.This article develops a quantum classifier based on the isolated quantum system(QC-IQS)to classify nonlinear and multidimensi...It is a critical challenge for quantum machine learning to classify the datasets accurately.This article develops a quantum classifier based on the isolated quantum system(QC-IQS)to classify nonlinear and multidimensional datasets.First,a model of QC-IQS is presented by creating parameterized quantum circuits(PQCs)based on the decomposing of unitary operators with the Hamiltonian in the isolated quantum system.Then,a parameterized quantum classification algorithm(QCA)is designed to calculate the classification results by updating the loss function until it converges.Finally,the experiments on nonlinear random number datasets and Iris datasets are designed to demonstrate that the QC-IQS model can handle and generate accurate classification results on different kinds of datasets.The experimental results reveal that the QC-IQS is adaptive and learnable to handle different types of data.Moreover,QC-IQS compensates the issue that the accuracy of previous quantum classifiers declines when dealing with diverse datasets.It promotes the process of novel data processing with quantum machine learning and has the potential for more comprehensive applications in the future.展开更多
The campontely structured inductive waveguide elements are analyzed by making use ofthe equivalent source method in this paper The number and cross section of the inductive posts are arbi-trary, and they are of rither...The campontely structured inductive waveguide elements are analyzed by making use ofthe equivalent source method in this paper The number and cross section of the inductive posts are arbi-trary, and they are of rither pure metallic or pure dielectric lype , or composed of both metallic and di-electric ones. A number of inductive waveguide elements are numerically analyzed and a good agree-ment is achieved in comiparison with the measured data or results available in published literalures.展开更多
文摘In view of drastic possible changes in fuze environment tempera- ture,a kind of temperature autocompensated detecting circuit for the capaci- tance fuze is proposed.It provides a steady detected output when the envi- ronment temperature varies from-50℃ to 65℃ and keeps a stable detecting sensitivity.Based on an analysis of the circuit,influence of the major param- eters of the oscillating circuit on the amplitude are explored.A few impor- tant controllable parameters affecting the circuit feature are found out.A parameter-control method is given in order to improve the circuit perfor- mance.
文摘Under the Born-von-Karmann periodic boundary condition, we propose a quantization scheme for non-dissipative distributed parameter circuits (i.e. a uniform periodic transmission line). We find the unitary operator for diagonalizing the Hamiltonian of the uniform periodic transmission line. The unitary operator is expressed in a coordinate representation that brings convenience to deriving the density matrix rho(q,q',beta). The quantum fluctuations of charge and current at a definite temperature have been studied. It is shown that quantum fluctuations of distributed parameter circuits, which also have distributed properties, are related to both the circuit parameters and the positions and the mode of signals and temperature T. The higher the temperature is, the stronger quantum noise the circuit exhibits.
基金Project supported by the National Natural Science Foundation of China (Grant No.11174358)the National Basic Research Program of China (Grant No.2010CB833102)
文摘We study the dynamics of two electron spins in coupled quantum dots （CQDs） monitored by a quantum point contact （QPC） detector. Their quantum state can be measured by embedding the QPC in an LC circuit. We derive the Bloch-type rate equations of the reduced density matrix for CQDs. Special attention is paid to the numerical results for the weak measurement condintion under a strong Coulomb interaction. It is shown that the evolution of QPC current always follows that of electron occupation in the right dot. In addition, we find that the output voltage of the circuit can reflect the evolution of QPC current when the circuit and QPC are approximately equal in frequency. In particular, the wave shape of the output voltage can be improved by adjusting the circuit resonance frequency and bandwidth.
基金supported by the National Natural Science Foundation of China(nos.20975062 and 21175084)
文摘We report a resonant method to measure the wall capacitance（C_w） and solution resistance（R_S） in a capacitively coupled contactless conductivity detector（C^4D）.Under the typical operating conditions in capillary electrophoresis（I.D.50μm,O.D. 360μm,electrode length of 4 mm,electrode gap of 1 mm,frequency of 200 kHz）,the values of C_w measured in 1 and 20 mmol/L NaCl solutions are 2.8 and 32 fF,which are only 1.1%and 12%of prediction by the equation in references,respectively.The value of R_S is less than the prediction in solutions withκ〈0.02 S/m.The response current of C^4D is due to the change in C_w because the total impedance of a C^4D is composed mainly by the impedance from C_w.
基金Project supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(Grant No.SKLACSS-202108)the National Natural Science Foundation of China(Grant No.U162271070)Scientific Research Fund of Zaozhuang University(Grant No.102061901).
文摘Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data mining.This paper proposes a quantum semi-supervised classifier based on label propagation.Considering the difficulty of graph construction,we develop a variational quantum label propagation(VQLP)method.In this method,a locally parameterized quantum circuit is created to reduce the parameters required in the optimization.Furthermore,we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement,which has a shallower circuit depth and is more suitable for implementation on near-term quantum devices.We demonstrate the performance of the quantum semi-supervised classifier on the Iris data set,and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test classifier.This work opens a new path to quantum machine learning based on graphs.
基金supported by the National Natural Science Foundation of China under Grant Nos.62172268 and 62302289the Shanghai Science and Technology Project under Grant Nos.21JC1402800 and 23YF1416200。
文摘Variational quantum algorithms are promising methods with the greatest potential to achieve quantum advantage,widely employed in the era of noisy intermediate-scale quantum computing.This study presents an advanced variational hybrid algorithm(EVQLSE)that leverages both quantum and classical computing paradigms to address the solution of linear equation systems.Initially,an innovative loss function is proposed,drawing inspiration from the similarity measure between two quantum states.This function exhibits a substantial improvement in computational complexity when benchmarked against the variational quantum linear solver.Subsequently,a specialized parameterized quantum circuit structure is presented for small-scale linear systems,which exhibits powerful expressive capabilities.Through rigorous numerical analysis,the expressiveness of this circuit structure is quantitatively assessed using a variational quantum regression algorithm,and it obtained the best score compared to the others.Moreover,the expansion in system size is accompanied by an increase in the number of parameters,placing considerable strain on the training process for the algorithm.To address this challenge,an optimization strategy known as quantum parameter sharing is introduced,which proficiently minimizes parameter volume while adhering to exacting precision standards.Finally,EVQLSE is successfully implemented on a quantum computing platform provided by IBM for the resolution of large-scale problems characterized by a dimensionality of 220.
基金Project supported by the Natural Science Foundation of Shandong Province,China (Grant No.ZR2021MF049)the Joint Fund of Natural Science Foundation of Shandong Province (Grant Nos.ZR2022LLZ012 and ZR2021LLZ001)。
文摘We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum circuits.In this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in classical convolutional neural networks,forming a new quantum convolutional layer to achieve unitary transformation of quantum states,enabling the model to more accurately extract hidden information from images.At the same time,we combine the classical fully connected layer with PQCs to form a new hybrid quantum-classical fully connected layer to further improve the accuracy of classification.Finally,we use the MNIST dataset to test the potential of the HQCCNN.The results indicate that the HQCCNN has good performance in solving classification problems.In binary classification tasks,the classification accuracy of numbers 5 and 7 is as high as 99.71%.In multivariate classification,the accuracy rate also reaches 98.51%.Finally,we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed.
文摘In this paper, equivalent circuits for high frequency multi-winding magnetic components are derived from finite element （FE） computations. Lumped parameter models are first presented, based on previously published work. All parameters of these circuits can be interpreted as the results of open and short-circuit tests on the transformer. Based on this consideration, numerical procedures are then proposed to derive frequency-dependent lumped parameters from FE simulations. By using an adequate formulation, parameters are directly obtained from the FE model degrees of freedom, without performing any volume integration in post-processing, which can be source of numerical errors. In this contribution, attention is paid on the modeling of magnetic coupling using inductances, and dissipative effects （winding and core losses） using resistances. The impact of conductor eddy currents on the circuit parameters is moreover studied in details. Instead of an analysis of the impact conductor eddy currents may have on the circuit parameters is moreover carried through.
基金supported by the National Natural Science Foundation of China(61972418,61872390)the Natural Science Foundation of Hunan Province(2020JJ4750)+2 种基金the Special Foundation for Distinguished Young Scientists of Changsha(kq1905058)China Computer Federation(CCF)-Baidu Open Fund(CCF-BAIDUOF2021031)the Fundamental Research Funds for the Central Universities of Central South University,China(2022XQLH014)
文摘It is a critical challenge for quantum machine learning to classify the datasets accurately.This article develops a quantum classifier based on the isolated quantum system(QC-IQS)to classify nonlinear and multidimensional datasets.First,a model of QC-IQS is presented by creating parameterized quantum circuits(PQCs)based on the decomposing of unitary operators with the Hamiltonian in the isolated quantum system.Then,a parameterized quantum classification algorithm(QCA)is designed to calculate the classification results by updating the loss function until it converges.Finally,the experiments on nonlinear random number datasets and Iris datasets are designed to demonstrate that the QC-IQS model can handle and generate accurate classification results on different kinds of datasets.The experimental results reveal that the QC-IQS is adaptive and learnable to handle different types of data.Moreover,QC-IQS compensates the issue that the accuracy of previous quantum classifiers declines when dealing with diverse datasets.It promotes the process of novel data processing with quantum machine learning and has the potential for more comprehensive applications in the future.
文摘The campontely structured inductive waveguide elements are analyzed by making use ofthe equivalent source method in this paper The number and cross section of the inductive posts are arbi-trary, and they are of rither pure metallic or pure dielectric lype , or composed of both metallic and di-electric ones. A number of inductive waveguide elements are numerically analyzed and a good agree-ment is achieved in comiparison with the measured data or results available in published literalures.
