基于LonWorks总线型智能有毒有害气体变送器的设计

介绍一种基于LonWorks总线型智能有毒有害气体变送器的工作原理、硬件电路设计和软件设计。这种智能变送器不仅具有大屏幕液晶显示、声光报警功能,而且带有LonWorks总线接口,能够广泛应用于LonWorks总线系统或其他DCS系统中。     

一种基于改进的量子神经网络的语音降噪方法

利用一种改进的量子神经网络（IPSO-QNN）在时域上对含噪的语音信号进行降噪处理,文中重点改进了QNN所涉及到的学习算法。针对粒子群算法本身存在早熟的不足,提出了一种改进的粒子群优化算法（IPSO）。该算法通过对早熟粒子的速度和位置叠加随机数据,使其离开局部最优,从而使算法具有更强的寻优能力。利用IPSO对量子神经网络的参数进行训练和学习,建立了比较高效的基于改进的量子神经网的语音信号滤波器,并通过Matlab软件建立实验平台,实验结果表明,新算法充分利用了量子神经计算的快速性以及粒子群算法的全局寻优能力,从而使该语音信号滤波器,具有良好的降噪性能。     

一种量子神经网络模型及改进学习算法

提出一种量子BP网络模型及改进学习算法,该BP网络模型首先基于量子学中一位相移门和两位受控非门的通用性,构造出一种量子神经元,然后由该量子神经元构造隐含层,采用梯度下降法进行学习。输出层采用传统神经元构造,采用基于改进的带动量自适应学习率梯度下降法学习。在UCI两个数据集上采用该模型及算法,实验结果表明该方法比传统的BP网络具有较好的收敛速度和正确率。     

Quantum Algorithms for Weighing Matrices and Quadratic Residues

Abstract. In this article we investigate how we can employ the structure of combinatorial objects like Hadamard matrices and weighing matrices to devise new quantum algorithms. We show how the properties of a weighing matrix can be used to construct a problem for which the quantum query complexity is significantly lower than the classical one. It is pointed out that this scheme captures both Bernstein and Vazirani's inner-product protocol, as well as Grover's search algorithm. In the second part of the article we consider Paley's construction of Hadamard matrices, which relies on the properties of quadratic characters over finite fields. We design a query problem that uses the Legendre symbol χ (which indicates if an element of a finite field F _q is a quadratic residue or not). It is shown how for a shifted Legendre function f _s (i)=χ(i+s) , the unknown s ∈ F _q can be obtained exactly with only two quantum calls to f _s . This is in sharp contrast with the observation that any classical, probabilistic procedure requires more than log q + log ((1-ɛ )/2) queries to solve the same problem.     

Interference automata

We propose a computing model, the Two-Way Optical Interference Automata (2OIA), that makes use of the phenomenon of optical interference. We introduce this model to investigate the increase in power, in terms of language recognition, of a classical Deterministic Finite Automaton (DFA) when endowed with the facility of interference. The question is in the spirit of Two-Way Finite Automata With Quantum and Classical States (2QCFA) [A. Ambainis, J. Watrous, Two-way finite automata with quantum and classical states, Theoret. Comput. Sci. 287 (1) (2002) 299–311] wherein the classical DFA is augmented with a quantum component of constant size. We test the power of 2OIA against the languages mentioned in the above paper. We give efficient 2OIA algorithms to recognize languages for which 2QCFA machines have been shown to exist, as well as languages whose status vis-a-vis 2QCFA has been posed as open questions. Having a DFA as a component, it trivially recognizes regular languages. We show that our model can recognize all languages recognized by 1-way deterministic blind counter automata. Finally we show the existence of a language that cannot be recognized by a 2OIA but which can be recognized by an O(n³)$O\left(n^3\right)$ space Turing machine.     

Optimal computation with non-unitary quantum walks

Quantum versions of random walks on the line and the cycle show a quadratic improvement over classical random walks in their spreading rates and mixing times, respectively. Non-unitary quantum walks can provide a useful optimisation of these properties, producing a more uniform distribution on the line, and faster mixing times on the cycle. We investigate the interplay between quantum and random dynamics by comparing the resources required, and examining numerically how the level of quantum correlations varies during the walk. We show numerically that the optimal non-unitary quantum walk proceeds such that the quantum correlations are nearly all removed at the point of the final measurement. This requires only O(logT)$O(logT)$ random bits for a quantum walk of T$T$ steps.     

量子小波变换算法及线路实现

随着小波理论研究的深入,以及小波分析在信号分析和图像处理等领域的广泛应用,小波分析在量子计算领域中也越来越受到重视.应用置换矩阵、W-H变换矩阵和量子傅立叶变换矩阵来对Haar小波及D(4)小波变换矩阵进行分解,给出其算法,然后得出其完整的量子逻辑线路图,最后分析其复杂度.     

SHOR量子算法的优化及应用研究

Shor算法是目前最常用的算法之一.主要是基于Shor量子算法分解效率不高的问题作出的一些算法上的优化.通过对Shor算法的进一步分析,提出优化思想及对优化过程进行理论论证来论证算法分解效率的提高.最后讨论了Shor算法的现实意义以及应用研究.     

一种实数编码量子进化算法及其收敛性

基于量子计算理论和进化理论,提出一种新的量子进化算法--基于实数编码的量子进化算法(RQEA).不同于传统进化算法的单点编码和量子进化算法的量子比特编码,该算法以实数矩形区域表示基因,一条染色体携带多个个体信息,利用量子态叠加和相干机理,通过叠加、变异及自学习来完成进化过程,理论分析证明了算法具有全局收敛性,实验结果表明,该算法在函数优化上具有优异的性能.     

基于改进量子遗传算法的过程神经元网络训练

针对过程神经元网络由于模型参数较多BP算法不易收敛的问题,提出一种基于量子位Bloch坐标的量子遗传算法.将该算法融合于过程神经网络的训练.按权值参数的个数确定量子染色体上的基因数并完成种群编码,通过新的量子旋转门完成个体的更新.算法中的每条染色体携带3条基因链,因此可扩展对解空间的遍历性,加速优化进程.以两组二维三角函数的模式分类问题为例,仿真结果表明该方法不仅收敛速度快,而且寻优能力强.