混合量子差分进化算法及应用

量子进化算法基于量子旋转门更新量子比特状态影响了算法搜索性能．提出一种差分进化（DE）与和声搜索（Hs）相结合更新量子比特状态的混合量子差分进化算法（HQDE）．该方法采用实数量子角形式编码染色体,设计一种由差分进化计算更新量子位状态的量子差分进化算法（QDE）和一种由和声搜索更新量子位状态的量子和声搜索（QHS）,并相互机制融合,采用两种不同进化策略共同作用产生种群新量子个体以克服常规算法中早熟及收敛速度慢等缺陷;在此基础上,算法还引入量子非门算子对当前最劣个体以一定概率选中的量子比特位进行变异操作增强算法跳出局部最优解能力．理论分析证明该算法收敛于全局最优解．0／1背包问题及旅行商问题实例测试结果验证了该方法有效性．     

协同进化扩展紧致量子进化算法

为了提高量子进化算法的全局收敛性能, 基于协同进化的思想, 并结合扩展紧致遗传算法, 提出了协同进化扩展紧致量子进化算法(CECQEA). 该算法利用多粒度机制进行量子染色体的旋转, 并依据边缘积模块(MPM) 进行交叉和变异以避免优良模式的破坏; 在每一个子种群内对个体依据MPM进行自调整操作, 同时进行种群的分裂、合并及优良个体的迁移操作. 通过对算法收敛性的分析可看出, CECQEA 能够收敛到满意解集; 经基准函数以及背包问题的仿真测试分析可看出, 算法收敛效果更加明显.     

求解多目标优化问题的分级变异量子进化算法

分析量子进化算法和免疫算子的特点,提出一种分级变异的量子进化算法,用于求解多目标优化问题,算法主要基于两个策略:首先,利用快速非受控排序和密度距离计算种群抗原-抗体的亲和度;然后,基于亲和度排序将个体进行分级,最优分级中的个体作为算法中的最优个体,大部分实施量子旋转更新和免疫操作,而剩余分级中的个体实施免疫交叉操作以获得新的个体补充种群,求解多目标0/1背包问题的实验结果表明了该算法的有效性.     

分段式量子-单纯形进化算法及函数优化

量子进化算法在高维复杂函数优化上存在容易陷入局部最优解、进化后期收敛速度慢的问题,为进一步提高其搜索性能,提出了一种带单纯形搜索算子的分段式量子进化算法。该方法将搜索过程分为3个阶段,首先用量子进化算法搜索到一定代数,然后将种群分为若干个子种群,每个子种群中的个体作为单纯形法的初始顶点,并行地用单纯形法进行搜索,将搜索后的子种群再合并,继续用量子进化算法进行最后的搜索。对几个典型的高维函数进行仿真的结果表明,该算法具有更快的收敛速度和更高的求解精度。     

量子驱动的克隆选择在入侵检测中的应用

将量子计算技术与克隆选择算法结合,通过对量子编码的检测器进行测量来生成二进制检测器（测量解）在得到的多个测量解中只保存亲和力最高的。既提高了种群多样性避免了早熟,又减少了检测器冗余。在变异过程中,通过对父代检测器和子代检测器进行比较来对变异进行指导,使其向着亲和力更高的方向进行。加快了种群的进化速度。仿真实验表明,同传统克隆选择算法相比,该算法避免了种群进化过程出现的早熟和进化速度慢以及检测器冗余的问题,提高了系统的检测率。     

面向布局优化问题的多量子态量子进化算法及其应

针对演化算法在求解带平衡约束的圆形布局问题上所出现的早熟现象,提出一种有利于保持种群多样性的多量子态量子进化算法,并结合高效的定位定序启发式方法进行求解。为了高效优化布局顺序,在量子进化算法的基础上：引入多量子态编码和基于平均收敛概率的收敛标准以提高求解速度;引入基于禁忌策略和启发信息的观测方法,使其所得到的n进制解为互不相同的整数串,同时保证优先布局质量大、半径大的小圆;引入动态量子进化策略,有效地引导种群向最优个体进化。在定位规则中引入定位概率函数提高解的精度,数值实验结果表明,该算法能够有效求解带平衡约束的圆形布局问题。     

一种新的求解TSP的混合量子进化算法

在分析量子进化基本概念的基础上,提出了一种新的求解TSP的混合量子进化算法（MQEA）。该算法将三段优化局部搜索算法融入量子进化机制,采用一种基于边的编码方法,应用最近邻规则设置初始参数,并设计了排序交叉算子以扩展种群的搜索范围。通过选取国际通用旅行商问题（TSP）实例库(TSPLIB)中的多个实例进行测试,表明新算法具有高的精确度和鲁棒性,即使对于中大规模问题（城市数大于500）,也能以很小的种群和微小的相对误差求得满意解。     

一种求解同等并行机调度的混合量子衍生进化规划算法

针对带顺序相关建立时间的同等并行机调度问题的求解,提出一种新的混合量子衍生进化规划算法.该算法通过定义新的量子个体来表示调度问题中的工件排序,并定义了针对调度问题的量子旋转角,使个体向更好的解靠近.同时,针对并行机问题本身,改进了个体的编码方式和新的变异方法.为了验证算法的有效性和收敛性,采用不同规模的算例进行仿真实验.结果表明,即使在小种群情况下,算法所得解均优于基本进化规划求得的解.     

改进量子遗传算法及其应用

针对量子遗传算法在多维复杂函数优化中迭代次数多、易陷入局部极值等缺点,提出新的量子遗传算法。通过搜索各种群中各染色体的最优个体,组成一个新的种群,并以此种群作为当前最优种群来确定量子门的全局最优搜索方向。引入小生境协同进化策略初始化量子种群,使量子染色体均匀分布于初值空间。以非线性连续优化问题为例所进行的仿真结果表明,该方法具有收敛速度快、寻优能力强等优点。最后,将该算法应用于化工过程的优化,取得良好的效果。     

基于云模型的量子免疫优化算法

采用云模型对量子免疫算法进行了改进,采用量子种群基因云对种群进化进行定性控制,基于云模型的量子旋转门自适应调整策略进行更新操作,使算法在定性知识的指导下能够自适应控制搜索空间范围,使其能在较大搜索空间条件下避开局部最优解。典型函数对比实验表明该算法可以避免陷入局部最优解,能提高全局寻优能力,能以更快的速度收敛于全局最优解,具有较好的应用价值。     

量子计算机的研究与应用

介绍了量子计算的最新研究方向,简述了量子计算和量子信息技术在保密通信、量子算法、数据库搜索等重要领域的应用。分析了量子计算机与经典计算机相比所具有的优点和目前制约量子计算机应用发展的主要因素,最后展望了其未来发展趋势。     

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.     

A Survey on quantum computing technology

The power of quantum computing technologies is based on the fundamentals of quantum mechanics, such as quantum superposition, quantum entanglement, or the no-cloning theorem. Since these phenomena have no classical analogue, similar results cannot be achieved within the framework of traditional computing. The experimental insights of quantum computing technologies have already been demonstrated, and several studies are in progress. Here we review the most recent results of quantum computation technology and address the open problems of the field.     

量子力学和量子计算机

In this paper,the relationship between computation and physics and the application of the principle of Quantum mechanics to Quantum Computing and Quantum Computers was reviewed     

Quantum estimation,control and learning: Opportunities and challenges

The development of estimation and control theories for quantum systems is a fundamental task for practical quantum technology. This vision article presents a brief introduction to challenging problems and potential opportunities in the emerging areas of quantum estimation, control and learning. The topics cover quantum state estimation, quantum parameter identification, quantum filtering, quantum open-loop control, quantum feedback control, machine learning for estimation and control of quantum systems, and quantum machine learning.     

Self-protected quantum algorithms based on quantum state tomography

Only a few classes of quantum algorithms are known which provide a speed-up over classical algorithms. However, these and any new quantum algorithms provide important motivation for the development of quantum computers. In this article new quantum algorithms are given which are based on quantum state tomography. These include an algorithm for the calculation of several quantum mechanical expectation values and an algorithm for the determination of polynomial factors. These quantum algorithms are important in their own right. However, it is remarkable that these quantum algorithms are immune to a large class of errors. We describe these algorithms and provide conditions for immunity.      

Mathematical models of quantum computation

In this paper, we introduce two mathematical models of realistic quantum computation. First, we develop a theory of bulk quantum computation such as NMR (Nuclear Magnetic Resonance) quantum computation. For this purpose, we define bulk quantum Turing machine (BQTM for short) as a model of bulk quantum computation. Then, we define complexity classes EBQP, BBQP and ZBQP as counterparts of the quantum complexity classes EQP, BQP and ZQP, respectively, and show that EBQP=EQP, BBQP=BQP and ZBQP=ZQP. This implies that BQTMs are polynomially related to ordinary QTMs as long as they are used to solve decision problems. We also show that these two types of QTMs are also polynomially related when they solve a function problem which has a unique solution. Furthermore, we show that BQTMs can solve certain instances of NP-complete problems efficiently. On the other hand, in the theory of quantum computation, only feed-forward quantum circuits are investigated, because a quantum circuit represents a sequence of applications of time evolution operators. But, if a quantum computer is a physical device where the gates are interactions controlled by a current computer such as laser pulses on trapped ions, NMR and most implementation proposals, it is natural to describe quantum circuits as ones that have feedback loops if we want to visualize the total amount of the necessary hardware. For this purpose, we introduce a quantum recurrent circuit model, which is a quantum circuit with feedback loops. LetC be a quantum recurrent circuit which solves the satisfiability problem for a blackbox Boolean function includingn variables with probability at least 1/2. And lets be the size ofC (i.e. the number of the gates inC) andt be the number of iterations that is needed forC to solve the satisfiability problem. Then, we show that, for those quantum recurrent circuits, the minimum value ofmax(s, t) isO(n ²2 ^n/3). Tetsuro Nishino, D.Sc.: He is presently an Associate Professor in the Department of Information and Communication Engineering, The University of Electro-Communications. He received the B.S., M.S. and D.Sc degrees in mathematics from Waseda University, in 1982, 1984 and 1991 respectively. From 1984 to 1987, he joined Tokyo Research Laboratory, IBM Japan. From 1987 to 1992, he was a Research Associate of Tokyo Denki University, and from 1992 to 1994, he was an Associate Professor of Japan Advanced Institute of Science and Technology, Hokuriku. His main interests are circuit complexity theory, computational learning theory and quantum complexity theory.     

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.     

Combating Quantum Burst-errors with Quantum Interleaving Technique

Based on the interleaving technique, a kn-qubit code is constructed in this paper with more error-correcting ability than one n-qubit quantum error-correcting code without introducing the redundant qubits. By converting quantum bursts of errors into quantum random errors with the help of the quantum interleaving of the several states of the same quantum code, the proposed technique becomes an effective means to combat quantum bursts of errors. It is much simple and applicable for the quantum interleaving techniques to be used in the optical-fiber communications.     

Quantum covariance and filtering

We give a tutorial exposition of the analogue of the filtering equation for quantum systems focusing on the quantum probabilistic framework and developing the ideas from the classical theory. Quantum covariances and conditional expectations on von Neumann algebras play an essential part in the presentation.