In Situ Acquisition of Equivalent Circuit Parameters of Crystal Resonance during Copper Deposition and Dissolution in Acidic Solution by Electrochemical Quartz Crystal Impedance System

Electrochemical quartz crystal impedance system (EQCIS) which allows in situ dynamic quartz crystal impedance measurement in an electrochemical experiment was developed by combining an HP 4395A Network/Spectrum/Impedance analyzer with an EG&G M283 potentiostat. Equivalent circuit parameters of crystal resonance change significantly during electrodeposition and dissolution of copper in 0.1 mol/L H2SO4 aqueous solution in a cyclic potential sweep experiment, which is explained with an overall picture of mass loading, solution density and viscosity, etc..

Density Matrix for Mesoscopic Distributed Parameter Circuits

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.

一种构建参数化量子线路的区块环拓扑结构

在变分量子算法中,参数化量子线路拓扑结构的选择对算法性能具有重要意义.目前已有的拓扑结构存在一些问题,如全连接拓扑结构所需量子门数量较多,环型拓扑结构的表达能力与纠缠能力略有欠缺.为了解决以上问题,本文提出了一种新型的区块环(Block-Ring,BR)拓扑结构,在保障良好性能的同时减少参数规模(即量子门数量),降低线路复杂度.在BR拓扑中,n个量子比特被等分为多个区块,每个区块包含m个量子比特,区块内部所有量子比特两两连接,区块之间采用环型结构进行连接.为了构造BR拓扑结构的参数化量子线路,设计了一种多层线路生成算法,可自动生成由单量子比特门Rx、Rz和双量子比特门CRx或CRz构成的量子线路.IBM Q模拟实验表明,相较于环型拓扑结构,无论单层、双层以及三层BR拓扑结构的表达能力和纠缠能力均有不同程度的提升;相较于拥有最高表达能力与纠缠能力的全连接拓扑结构,BR拓扑结构呈现接近的性能指标,且线路复杂度显著降低,即参数数量与双量子比特门数量均从O(n^(2))降低为O(mn),线路深度从O(n^(2))降低为O(n/m+m^(2)).

电路参数对石墨加热元件发热特性的影响规律研究

电路参数是石墨加热元件发热性能的决定性因素,对电路参数的影响规律研究是高性能、高可靠性石墨加热器设计的基础。对石墨加热装置的失效过程进行研究,确定发热元件可靠性量化指标——温度均匀度;在此基础上,确定对加热元件热输出性能进行量化的四个电路参数:加热元件厚度、发热条宽度、发热条缝隙、缝隙处的倒圆半径;对发热电路进行参数化建模,并采用有限元电热耦合仿真对加热元件电路参数对加热能力和可靠性的影响机理进行研究。结果表明:加热元件的厚度决定了其热输出能力,而其余三个电路参数决定了加热元件的可靠性。本文的研究成果对高性能石墨加热装置的研制具有重要的支撑作用。

A Temperature-Autocompensated Detecting Circuit for the Capacitance Fuze

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.

Analysis of learnability of a novel hybrid quantum-classical convolutional neural network in image classification

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.

基于国产FPGA的移动通信网信令设计与实现

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。

An Efficient Approach for the Numerical Identification of R and L Parameters of High Frequency Multi-winding Transformers

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.

Variational quantum semi-supervised classifier based on label propagation

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.

基于量子卷积神经网络的图像识别新模型

为了解决卷积神经网络对内存和时间效率要求越来越高的问题,提出一种面向数字图像分类的新模型,该模型为基于强纠缠参数化线路的量子卷积神经网络。首先对经典图像进行预处理和量子比特编码,提取图像的特征信息,并将其制备为量子态作为量子卷积神经网络模型的输入。通过设计模型量子卷积层、量子池化层、量子全连接层结构,高效提炼主要特征信息,最后对模型输出执行Z基测量,根据期望值完成图像分类。实验数据集为MNIST数据,{0,1}分类和{2,7}分类准确率均达到了100%。对比结果表明,采用平均池化下采样的三层网络结构的QCNN模型具有更高的测试精度。

一种量子条件生成对抗网络算法

量子生成对抗网络是量子机器学习算法领域研究热点之一,但其生成过程具有较大的随机性,不太适用于现实场景.为了解决该问题,提出了一种生成过程可控的量子条件生成对抗网络(Quantum Conditional Generative Adversarial Network,QCGAN)算法,其中条件信息采用one-hot形式进行多粒子W态编码,并通过向生成器和判别器输入条件信息达到稳定模型生成过程的目的.性能评估表明,与经典GAN、CGAN相比,本算法可生成离散数据,且将时间复杂度从O(N^(2))降为O(N);与带条件约束的量子生成对抗网络QuGAN相比,QCGAN消耗更少的量子资源.最后,以BAS(3,3)数据集和量子混合态生成为例,选用PennyLane平台进行仿真实验,结果表明QCGAN算法经过训练可有效收敛到Nash均衡点,进而验证了算法的实验可行性.

基于图像处理技术的真空电弧参数化研究

真空电弧的演变规律对于真空断路器的设计具有重要意义。本文结合图像处理技术,利用拆除屏蔽罩的玻璃真空灭弧室,对交流电弧和直流开断电弧进行了拍摄及数字化分析。并采用改进的双阈值灰度图像分割算法,将电弧图像分割为三个特征区域,并计算出电弧直径、高灰度值区面积、高灰度值区面积率三个特征参数。结果表明,在纵向磁场下的作用下,交流电弧在2 ms左右的初始扩散过程后,会继续稳定燃烧直至电流自然过零熄灭;而直流开断电弧在被引燃后,存在1 ms左右的高灰度值区面积率较高的过程,随后经历短暂的扩散过程后,由于换流电流的投入而快速熄灭。

Quantum state measurement in double quantum dots with a radio-frequency quantum point contact

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.

A Study on a Partially Immersed Gold Electrode Using an Electrochemical Quartz Crystal Impedance System

The electrochemical quartz crystal impedance system (EQCIS) has been used for the study of a partially immersed Au electrode in 0.2 mol/L NaClO4 aqueous solution. The influences of the immersed area and height of the electrode on the EQCIS responses were evaluated, showing the highest response sensitivity to liquid loading at the center of the piezoelectric quartz crystal electrode. The increase in the immersed height of the Au electrode at oxygen reduction potentials during potential cycling was measured by this technique.

基于FPGA的通用卷积层IP核设计

针对目前卷积神经网络在小型化、并行化过程中遇到的计算速度不够、可移植性差的问题,根据卷积神经网络和FPGA器件的特点,提出了一种利用VHDL语言参数化高速通用卷积层IP核的设计方法。利用卷积层的计算方式,将卷积核心设计为全并行化、流水线的计算模块,通过在卷积核心的每一行连接FIFO的方式改善数据流入的方式,减少地址跳转的操作,并加入控制核心使其可以随图像和卷积窗口大小调整卷积层参数,生成不同的卷积层,最后将卷积层与AXIS协议结合并封装成IP核。结果表明,在50 MHz的工作频率下,使用2×2大小的卷积核对100×100的图像进行卷积计算,各项资源利用率不超过1%,耗时204μs,计算速度理论上可以达到最高5 MF/s。因此,设计方案在增加卷积模块可移植性的同时又保证了计算速度,为卷积神经网络在小型化器件上的实现提供了一种可行的方法。

基于VHDL语言的参数化设计方法

随着FPGA制造工艺的不断进步,越来越多的应用可以在FPGA中实现。虽然用于FPGA设计的VHDL语言具有很好的可移植性,但是FPGA芯片的可用资源不尽相同,因此对设计的规模进行参数化才能实现设计的可移植及充分利用FPGA的资源。此外,同一算法在不同的应用领域中,也会需要对其规模进行改变。设计的参数化是指只需要对参数进行设定就可以自动生成相应规模设计的技术。首先提出了一种基于综合工具的VHDL参数化设计方法,其次以多路奇偶校验生成器为例,详细说明了参数化的基本过程,最后在HMMer的FPGA实现中应用所提出的方法,从而实现对运算单元数量的控制。所提出的参数化方法具有操作简单、代码变动小、无需要第三方代码支持等优点。实验表明,该方法是VHDL设计中成本小、效果好的参数化设计方案。

标准单元电路-版图设计自动优化技术

提出了一种标准单元的电路-版图设计自动优化技术。根据目标电学性能等确定电路中器件的参数值,然后根据参数变化微调现有的标准单元版图,快速自动生成符合设计规则的新版图。该技术可从新版图中获取电路寄生参数,对电路的电学性能进行评估,进一步提高电路-版图设计自动优化速度。测试表明,该技术既可以用于加速标准单元建库和设计移植,也可以支持对更高层的SOC设计进行延时和功耗的在位优化,加速设计收敛,可缩短开发周期。

Quantum classifier with parameterized quantum circuit based on the isolated quantum system

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.

SHTW1(DW45)系列智能型万能式低压断路器

介绍低压电器中目前新产品技术性能最先进的SHTW1(DW45)系列智能型万能式断路器。它的产品结构、主要技术参数。

Towards an efficient variational quantum algorithm for solving linear equations

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.