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
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
为提高电信网设备应对异常信令访问的检测能力,需对64K信令进行分析并处理。为了提高解析效率并满足近年来相关产品对自主可控越来越高的要求,设计了一种基于国产现场可编程门阵列(Field Programmable Gate Array, FPGA)的信令解析方案...为提高电信网设备应对异常信令访问的检测能力,需对64K信令进行分析并处理。为了提高解析效率并满足近年来相关产品对自主可控越来越高的要求,设计了一种基于国产现场可编程门阵列(Field Programmable Gate Array, FPGA)的信令解析方案,给出了方案的总体设计思路,并对FPGA实现的功能模块进行详细说明。对系统进行设计时,采用模块化参数化方法以及在关键环节添加状态参数,提高了可扩展性并可以对模块内部运行状态进行监控,最终实现了对信令高效且灵活的解析,主要器件等均为国产。经过测试,可以实现STM-1(STM-Synchronous Transfer Module-1)数据的接入、串并转换、HDLC(High-level Data Link Control)解帧等功能,完成32路64K信令的并发处理,模块运行状态可查可看,达到了预期的效果。以STM-1为例,基于现有功能的模块化设计,可以平滑地扩展到STM-4、STM-16的应用。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
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
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.
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.
文摘为提高电信网设备应对异常信令访问的检测能力,需对64K信令进行分析并处理。为了提高解析效率并满足近年来相关产品对自主可控越来越高的要求,设计了一种基于国产现场可编程门阵列(Field Programmable Gate Array, FPGA)的信令解析方案,给出了方案的总体设计思路,并对FPGA实现的功能模块进行详细说明。对系统进行设计时,采用模块化参数化方法以及在关键环节添加状态参数,提高了可扩展性并可以对模块内部运行状态进行监控,最终实现了对信令高效且灵活的解析,主要器件等均为国产。经过测试,可以实现STM-1(STM-Synchronous Transfer Module-1)数据的接入、串并转换、HDLC(High-level Data Link Control)解帧等功能,完成32路64K信令的并发处理,模块运行状态可查可看,达到了预期的效果。以STM-1为例,基于现有功能的模块化设计,可以平滑地扩展到STM-4、STM-16的应用。
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.
