基于改进量子遗传算法的有源噪声控制方法

提出了一种基于改进量子遗传算法的格型IIR滤波器结构的有源噪声控制方法——改进的量子遗传算法（IQGA）。其核心是在对量子门更新过程进行改进的基础上，引入群体灾变和自适应搜索网格的策略。IQGA不仅收敛速度快，还可以改变基本遗传算法（GA）的局部搜索能力，克服基本遗传算法存在的未成熟收敛问题。仿真结果表明，该算法可以有效地实现噪声控制。     

基于蛙跳思想的量子编码遗传算法

量子门旋转相位、变异概率大小的确定,是目前制约量子遗传算法效率的两个主要问题。本文提出一种基于蛙跳思想的量子编码遗传算法(QRGA),该算法采用自适应的方式对量子旋转门旋转角进行调整,并基于模糊逻辑将蛙跳的步长进行量化以指导变异概率调整,保证进化的方向性和提高算法效率,对比实验结果表明算法可以避免陷入局部最优解,并能快速收敛到全局最优解,在运行时间和解的性能上都取得了较好的效果。     

基于Bloch量子遗传算法的倒立摆模糊控制器优化设计

为解决倒立摆模糊控制器的优化设计问题,提出一种基于Bloch量子遗传算法(BQGA)的优化设计方案.该方案将量子位的3个Bloch坐标都看作基因位,每条染色体包含3条并列的基因链,每条基因链代表一个优化解,即一组控制器参数,在与普通量子遗传算法(CQGA)染色体数目相同时可加速优化进程.以模糊神经网络控制器(FNNC)...     

基于量子遗传算法的配电网重构

本文针对传统普通遗传算法在配电网重构中易产生不可行解和过早收敛的问题,提出了一种配电网重构的量子遗传算法。采用本文算法对IEEE16节点和IEEE69节点两个标准算例进行仿真计算,仿真结果表明了所提算法的可行性、正确性和有效性。     

运用改进的量子遗传算法进行结构损伤识别

利用振动得到的模态参数进行结构的损伤识别,是当前结构损伤识别领域中采用较为普遍的方法之一。文中引入量子遗传算法处理模态参数,为了让量子遗传算法更适用于复杂结构的损伤识别,还提出了改进的动态策略调整量子门旋转角,并应用于某框架结构的损伤识别。在识别过程中,以结构损伤前后柔度矩阵差的1范数为基础建立改进的量子遗传算法的目标函数,进行结构的损伤识别。结果表明该方法能准确、有效地判断框架结构的损伤发生、损伤位置以及损伤程度,并且具有一定的抗噪能力。     

量子计算和量子信息理论研究取得进展

<正>量子计算和量子信息处理是人们利用物质的量子行为而设计的量子信息处理技术。量子算法显示现今金融系统和互联网所普遍采用的公钥保密算法并不是安全的,它可以被量子计算机容易的破解。同时量子信息技术也提供了建立在量子力学基本原理基础上的量子密码协约。     

基于量子遗传算法的圆度误差测量研究

提出采用量子遗传算法,以提高圆度测量精度。首先用最小二乘法拟合获得建模数据中圆度图像的圆心坐标和半径;再通过圆度计算剔除不符合要求的圆度;然后用量子遗传算法进行多进制编码,量子旋转门非固定步长调整更新;最后给出圆度误差测量流程。实验仿真显示该算法获得了精确的测量数值,与三坐标测量机测量结果误差相差小于0.005 8 mm,半径相对误差小于0.19%,测量最大误差均在0.01%以内,同时最大误差波动比较平稳,测量不确定度比其它方法值较低。     

基于子结构和遗传神经网络的递推模型修正方法

根据实际动力响应对结构有限元模型进行修正,是实现损伤识别和健康监测的必要前提。针对基于神经网络的模型修正方法的不足,选用均匀设计法构造样本从而有效减少所需样本数量,而且计算效率高。采用遗传算法优化神经网络权值,提高了运算速度。基于上述研究,提出了基于子结构和神经网络的递推模型修正方法。该方法将结构分解成多层次的子结构,选取适当的损伤因素逐步实现逐级的修正。应用该方法对一网壳结构进行了模型修正,修正中首先采用固有频率作为损伤因素,结果表明遗传算法明显地提高了神经网络的计算速度,最后的递推修正效果令人满意;其次提出了采用小波包频带能量作为损伤因素的修正方法,该方法同样准确有效,并且不再依赖传统的模态分析技术,更为实用便捷。     

基于遗传算法和灰色神经网络的电力机车产品需求预测方法

目的 应对快速多变的市场,提前预知市场发展,制定相应的排产计划,使企业在竞争中占据先发优势。方法 目前基于灰色神经网络的预测算法,准确地预测产品需求通常需要连续且大量的样本数据,对小数据非线性系统的预测结果精确度低、可靠性差,针对这一问题,提出一种耦合遗传算法的灰色神经网络预测方法,综合灰色模型和神经网络理论,构建了面向产品订单量需求预测的灰色神经网络模型;通过电力机车产品实例分析了模型的预测性能;为解决预测过程中模型早熟收敛的问题,利用遗传算法对训练网络的权重和阈值进行了迭代优化。结论 研究结果表明,优化后产品预测模型的精确性和鲁棒性得到提高,验证了所设计方法的可行性。     

量子网络系统研究进展与关键技术分析

量子信息领域的迅速发展为现代信息技术带来了新的机遇与挑战，其中的热门研究方向之一即量子网络，旨在利用量子力学的基本特性实现长距离的（安全）通信任务，或通过分布式计算提供优于经典计算网络的计算能力，相关研究对推动量子信息的实用化具有重要意义。本文根据量子网络应用场景和技术手段的差异性，从量子密码网络、量子云计算网络、量子隐形传态网络3个细分类别出发，全面梳理了国内外的研究进展及发展挑战，以便掌握量子网络系统的最新发展态势；结合量子网络的实施情况，阐述了量子网络系统发展中亟待攻克的链路建立、信息传输、网络协议、物理硬件等关键技术。综合来看，量子网络仍处于初级发展阶段，当前需积极应对挑战并把握机遇，以增强我国前沿领域的科技硬实力。研究建议，加强基础硬件设施研发投入、重视量子网络理论研究、加强交叉学科研究和相关人才培养，以促进我国量子网络系统的发展。     

Quantum memories and error correction

Quantum states are inherently fragile, making their storage a major concern for many practical applications and experimental tests of quantum mechanics. The field of quantum memories is concerned with how this storage may be achieved, covering everything from the physical systems best suited to the task to the abstract methods that may be used to increase performance. This review concerns itself with the latter, giving an overview of error correction and self-correction, and how they may be used to achieve fault-tolerant quantum computation. The planar code is presented as a concrete example, both as a quantum memory and as a framework for quantum computation.     

基于量子关联的显微成像研究进展

简要回顾了量子关联成像的基本原理和发展历程,从量子光源和经典光源的角度详细介绍了量子关联成像在显微成像中的研究进展。做出了基于经典源的量子关联成像因易于实施、成本较低,在显微成像中更具应用前景的判断。     

量子精密测量参数估计优化研究进展

量子精密测量是利用量子叠加与纠缠、量子相互作用过程与量子测量等方式增强参数估计精度与灵敏度的技术,是在短中期内最具前景的量子技术之一。本文从量子精密测量的最优化研究方案出发,通过对大量的相关文献进行梳理归纳,分析出量子精密测量三大优化方案：量子态制备与测量最优方案、量子演化过程调控方案与经典后处理优化方案,并对三个基本优化方案进行总结分析。同时介绍了国内外量子精密测量技术的最新理论与实验进展。最后,总结了量子精密测量存在的问题与挑战,并对未来工作进行了展望。     

Entanglement dynamics and quasi-periodicity in discrete quantum walks

We study the entanglement dynamics of discrete time quantum walks acting on bounded finite sized graphs. We demonstrate that, depending on system parameters, the dynamics may be monotonic, oscillatory but highly regular, or quasi-periodic. While the dynamics of the system are not chaotic since the system comprises linear evolution, the dynamics often exhibit some features similar to chaos such as high sensitivity to the system's parameters, irregularity and infinite periodicity. Our observations are of interest for entanglement generation, which is one primary use for the quantum walk formalism. Furthermore, we show that the systems we model can easily be mapped to optical beamsplitter networks, rendering experimental observation of quasi-periodic dynamics within reach.     

Quantum technology: the second quantum revolution

We are currently in the midst of a second quantum revolution. The first quantum revolution gave us new rules that govern physical reality. The second quantum revolution will take these rules and use them to develop new technologies. In this review we discuss the principles upon which quantum technology is based and the tools required to develop it. We discuss a number of examples of research programs that could deliver quantum technologies in coming decades including: quantum information technology, quantum electromechanical systems, coherent quantum electronics, quantum optics and coherent matter technology.     

Prospective applications of optical quantum memories

An optical quantum memory can be broadly defined as a system capable of storing a quantum state through interaction with light at optical frequencies. During the last decade, intense research was devoted to their development, mostly with the aim of fulfilling the requirements of their first two applications, namely quantum repeaters and linear-optical quantum computation. A better understanding of those requirements then motivated several different experimental approaches. Along the way, other exciting applications emerged, such as as quantum metrology, single-photon detection, tests of the foundations of quantum physics, device-independent quantum information processing and nonlinear processing of quantum information. Here we review several prospective applications of optical quantum memories, as well as recent experimental achievements pertaining to these applications. This review highlights that optical quantum memories have become essential for the development of optical quantum information processing.     

Convergence Properties of Genetic Algorithmsin a Wide Variety of Noisy Environments

Random noise perturbs objective functions in practical optimization problems, and genetic algorithms (GAs) have been proposed as an effective optimization tool for dealing with noisy objective functions. In this paper, we investigate GAs in a variety of noisy environments where fitness perturbation can occur in any form-for example, fitness evaluations can be concurrently disturbed by additive and multiplicative noise. We reveal the convergence properties of GAs by constructing and analyzing a Markov chain that explicitly models the evolution of the algorithms in noisy environments. We compute the one-step transition probabilities of the Markov chain and show that the chain has only one positive recurrent communication class, which is also aperiodic. Based on this property, we establish a condition that is both necessary and sufficient for GAs to eventually (i.e., as the number of iterations goes to infinity) find a globally optimal solution with probability 1. We also identify a condition that is both necessary and sufficient for GAs to eventually with probability 1 fail to find any globally optimal solution. Furthermore, in all the noisy environments, our analysis shows that the chain has a stationary distribution that is also its steady-state distribution. Based on this property and the transition probabilities of the chain, we examine the number of iterations sufficient to ensure with at least a specified probability that GAs select a globally optimal solution upon termination.     

Kansei knowledge extraction based on evolutionary genetic algorithm: an application to e-commerce web appearance design

Considering the overall consumer preference based on Kansei engineering, this paper focuses on optimising the appearance design of e-commerce web. In the beginning, we have used iView X RED Eye Tracking Systems experimental apparatus produced by SensoMotoric Instruments (SMI) in Germany to extract web design elements, and then several representative webs are designed based on orthogonal test design, following by surveys we have made. Furthermore, structural equation models are established in order to obtain a single preference factor on the influence of e-commerce web design. Finally, based on the neural networks (NNs) and evolutionary genetic algorithm approach, the global optimisation appearance design of the e-commerce web is fetched by simulation on computer, providing the effective suggestions for the e-commerce web designer. This research paper presents a systematic approach that convert consumer's Kansei knowledge into usable product multi-dimensional design variables.     

遗传算法用于电子枪发射系统的全局最优化设计

常用的优化设计方法 ,如单纯形法、Powell法等 ,易陷入局部最优解。而遗传算法是一种新兴的直接搜索最优化算法 ,它模拟达尔文遗传选择与自然进化的理论 ,根据“适者生存”和“优胜劣汰”的原则 ,借助“复制”、“交换”、“突变”等操作可以得到全局最优解。本文将遗传算法运用于电子枪发射系统的最优化设计 ,得到了使交叠点半径尽可能小的发射系统的最佳结构和相应电参量     

Petri nets and genetic algorithms for complex manufacturing systems scheduling

In this paper we propose the GAPN (genetic algorithms and Petri nets) approach, which combines the modelling power of Petri nets with the optimisation capability of genetic algorithms (GAs) for manufacturing systems scheduling. This approach uses both Petri nets to formulate the scheduling problem and GAs for scheduling. Its primary advantage is its ability to model a wide variety of manufacturing systems with no modifications either in the net structure or in the chromosomal representation. In this paper we tested the performance on both classical scheduling problems and on a real life setting of a manufacturer of car seat covers. In particular, such a manufacturing system involves features such as complex project-like routings, assembly operations, and workstations with unrelated parallel machines. The implementation of the algorithm at the company is also discussed. Experiments show the validity of the proposed approach.