NONLOCALITY AND THE TIME-ORDERING OF EVENTS

A realistic solution of the "relativistic EPR dilemma" is proposed. The stated dilemma refers to the fact that a realistic account of nonlocal correlations in quantum theory as in EPR-type experiments, for example is faced with the problem of an apparently ambiguous choice for the time-ordering of events. However, if one takes into consideration Arno Bohm's concept of a "preparation time" for any quantum mechanical setup, and if one does so for all world-lines of the experimental setup, one can show that a realistic account of events is perfectly feasible. In this way, a unique and time-irreversible ordering of events is guaranteed for each reference frame.     

量子萤火虫算法及在无等待流水调度上的应用

针对无等待流水车间调度问题,提出了一种新颖的量子萤火虫优化算法用于最小化总完工时间．首先,将量子进化机制嵌入萤火虫算法中,并设计一种快速的局部邻域搜索方法,在每次迭代时只搜索部分邻域,同时采用目标增量计算邻域解变化,这样极大地加快了算法迭代速度,加速了算法收敛．最后,应用Taillard基准测试实例仿真,与目前较优的启发式算法IHA（improved heuristic algorithm）和群智能算法DGSO（discrete glowworm swarm optimization）、 GA-VNS（genetic algorithm-variable neighborhood search）及DHS（discrete harmony search）相比较,产生最好解的平均百分比偏差均下降了40％以上．实验结果验证了所提算法在求解无等待流水调度中的优越性．     

求解旅行商问题的多尺度量子自由粒子优化算法

针对目前元启发式算法在求解组合优化问题中的旅行商问题(TSP)时求解缓慢的问题,受量子理论中波函数的启发提出一种多尺度自适应的量子自由粒子优化算法。首先,在可行域中随机初始化表示城市序列的粒子,作为初始的搜索中心;然后,以每个粒子为中心进行当前尺度下的均匀分布函数的采样,并交换采样位置上的城市编号产生新解;最后,根据新解相较上一次迭代中最优解的优劣进行搜索尺度的自适应调整,并在不同的尺度下进行迭代搜索直到满足算法结束条件。将该算法和混合粒子群优化(HPSO)算法、模拟退火(SA)算法、遗传算法(GA)和蚁群优化算法应用在TSP上进行性能测试,实验结果表明自由粒子模型算法适合求解组合优化问题,在TSP数据集上相比目前较优算法在求解速度上平均提升50%以上。     

求解QoS组播路由问题的改进型量子进化算法

QoS组播路由问题是一个非线性的组合优化问题,已证明了该问题是NP完全问题。提出一种将基于量子计算原理的量子进化算法用于此类问题求解的算法,该算法对基本的量子进化算法进行改进,采用进化方程对量子门进行调整,采用量子变异阻止未成熟收敛,使之更适合于QoS组播路由的求解。仿真结果显示,该算法能快速搜索并收敛到全局（近似）最优解,且随着网络规模的增大算法保持了良好的特性,在寻优速度上与解的质量上优于其他进化算法与基本的量子进化算法。     

A quantum Jensen–Shannon graph kernel for unattributed graphs

In this paper, we use the quantum Jensen–Shannon divergence as a means of measuring the information theoretic dissimilarity of graphs and thus develop a novel graph kernel. In quantum mechanics, the quantum Jensen–Shannon divergence can be used to measure the dissimilarity of quantum systems specified in terms of their density matrices. We commence by computing the density matrix associated with a continuous-time quantum walk over each graph being compared. In particular, we adopt the closed form solution of the density matrix introduced in Rossi et al. (2013) 27 and 28 to reduce the computational complexity and to avoid the cumbersome task of simulating the quantum walk evolution explicitly. Next, we compare the mixed states represented by the density matrices using the quantum Jensen–Shannon divergence. With the quantum states for a pair of graphs described by their density matrices to hand, the quantum graph kernel between the pair of graphs is defined using the quantum Jensen–Shannon divergence between the graph density matrices. We evaluate the performance of our kernel on several standard graph datasets from both bioinformatics and computer vision. The experimental results demonstrate the effectiveness of the proposed quantum graph kernel.     

基于量子遗传谱聚算法的聚类

在处理大数据集聚类问题上,谱聚算法因存在占用存储空间大、时间复杂度高的缺陷而难以推广,针对此问题,提出采用多次分割、向上向下双向收缩的QR算法求得特征值对应的特征向量来实现降维,并在此基础上构造映射空间上的样本来实现量子遗传谱聚算法的聚类。该方法通过映射为后续的量子遗传谱聚算法聚类提供低维的输入,而量子遗传算法具有快速收敛到全局最优并且对初始化不敏感的特性,从而可以获得良好的聚类结果。实验结果显示,使用该算法的聚类比谱聚算法、K-means算法、NJW算法等单一方法具有更好的收敛性、稳定性和更高的全局最优。     

Quantum cooperative search algorithm for 3-SAT

Grover's search algorithm, one of the most popular quantum algorithms, provides a good solution to solve NP complexity problems, but requires a large number of quantum bits (qubits) for its functionality. In this paper, a novel algorithm called quantum cooperative search is proposed to make Grover's search algorithm work on 3-SAT problems with a small number of qubits. The proposed algorithm replaces some qubits with classical bits and finds assignments to these classical bits using the traditional 3-SAT algorithms including evolutionary algorithms and heuristic local search algorithms. In addition, the optimal configuration of the proposed algorithm is suggested by mathematical analysis. The experimental results show that the quantum cooperative search algorithm composed by Grover's search and heuristic local search performs better than other pure traditional 3-SAT algorithms in most cases. The mathematical analysis of the appropriate number of qubits is also verified by the experiments.     

Experimental Demonstration of Quantum Lattice Gas Computation

We report an ensemble nuclear magnetic resonance (NMR) implementation of a quantum lattice gas algorithm for the diffusion equation. The algorithm employs an array of quantum information processors sharing classical information, a novel architecture referred to as a type-II quantum computer. This concrete implementa-tion provides a test example from which to probe the strengths and limitations of this new computation paradigm. The NMR experiment consists of encoding a mass density onto an array of 16 two-qubit quantum information processors and then following the computation through 7 time steps of the algorithm. The results show good agreement with the analytic solution for diffusive dynamics. We also describe numerical simulations of the NMR implementation. The simulations aid in determining sources of experimental errors, and they help define the limits of the implementation. PACS: 03.67.Lx; 47.11.+j; 05.60.-k     

Design of a universal logic block for fault-tolerant realization of any logic operation in trapped-ion quantum circuits

This paper presents a physical mapping tool for quantum circuits, which generates the optimal universal logic block (ULB) that can, on average, perform any logical fault-tolerant (FT) quantum operations with the minimum latency. The operation scheduling, placement, and qubit routing problems tackled by the quantum physical mapper are highly dependent on one another. More precisely, the scheduling solution affects the quality of the achievable placement solution due to resource pressures that may be created as a result of operation scheduling, whereas the operation placement and qubit routing solutions influence the scheduling solution due to resulting distances between predecessor and current operations, which in turn determines routing latencies. The proposed flow for the quantum physical mapper captures these dependencies by applying (1) a loose scheduling step, which transforms an initial quantum data flow graph into one that explicitly captures the no-cloning theorem of the quantum computing and then performs instruction scheduling based on a modified force-directed scheduling approach to minimize the resource contention and quantum circuit latency, (2) a placement step, which uses timing-driven instruction placement to minimize the approximate routing latencies while making iterative calls to the aforesaid force-directed scheduler to correct scheduling levels of quantum operations as needed, and (3) a routing step that finds dynamic values of routing latencies for the qubits. In addition to the quantum physical mapper, an approach is presented to determine the single best ULB size for a target quantum circuit by examining the latency of different FT quantum operations mapped onto different ULB sizes and using information about the occurrence frequency of operations on critical paths of the target quantum algorithm to weigh these latencies. Experimental results show an average latency reduction of about 40 % compared to previous work.     

Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming

We have designed efficient quantum circuits for the three-qubit Toffoli (controlled–controlled-NOT) and the Fredkin (controlled-SWAP) gate, optimized via genetic programming methods. The gates thus obtained were experimentally implemented on a three-qubit NMR quantum information processor, with a high fidelity. Toffoli and Fredkin gates in conjunction with the single-qubit Hadamard gates form a universal gate set for quantum computing and are an essential component of several quantum algorithms. Genetic algorithms are stochastic search algorithms based on the logic of natural selection and biological genetics and have been widely used for quantum information processing applications. We devised a new selection mechanism within the genetic algorithm framework to select individuals from a population. We call this mechanism the “Luck-Choose” mechanism and were able to achieve faster convergence to a solution using this mechanism, as compared to existing selection mechanisms. The optimization was performed under the constraint that the experimentally implemented pulses are of short duration and can be implemented with high fidelity. We demonstrate the advantage of our pulse sequences by comparing our results with existing experimental schemes and other numerical optimization methods.     

基于通用量子门的量子遗传算法及应用

为加快量子遗传算法的参数更新速度,简化遗传操作步骤,提出了一种基于通用量子门的量子遗传算法（Quantum Genetic Algorithm with Universal Quantum Gate,UQGA）。该方法以通用量子门为逻辑计算单位,对染色体进行遗传操作。利用Hadamard门进行基础变换;通用量子门通过新的旋转角度函数,对各个基因位进行选择、变异操作;通过求解适应度函数,得到全局最优解;同时,算法经数学证明是收敛的。该算法应用到函数极值搜索和Iris数据集特征选择中。实验结果表明,UQGA具有较好的全局搜索和特征选择性能,尤其是在收敛速度、运算时间和分类准确率方面明显优于普通量子遗传算法和普通遗传算法。     

Quantum walks: a comprehensive review

Quantum walks, the quantum mechanical counterpart of classical random walks, is an advanced tool for building quantum algorithms that has been recently shown to constitute a universal model of quantum computation. Quantum walks is now a solid field of research of quantum computation full of exciting open problems for physicists, computer scientists and engineers. In this paper we review theoretical advances on the foundations of both discrete- and continuous-time quantum walks, together with the role that randomness plays in quantum walks, the connections between the mathematical models of coined discrete quantum walks and continuous quantum walks, the quantumness of quantum walks, a summary of papers published on discrete quantum walks and entanglement as well as a succinct review of experimental proposals and realizations of discrete-time quantum walks. Furthermore, we have reviewed several algorithms based on both discrete- and continuous-time quantum walks as well as a most important result: the computational universality of both continuous- and discrete-time quantum walks.     

求解多目标job-shop生产调度问题的量子进化算法*

基于量子计算理论和进化理论,提出了用于多目标job-shop优化的量子进化算法（QEA-MOJSP）。QEA-MOJSP采用量子比特来表示工序对加工顺序的优先概率,利用量子叠加和相干机理,通过更新和交叉操作完成进化过程。对所有机器上工序对优先概率进行观测可得到一个调度方案,修补算子被用于不可行调度方案的修补。设计了局部搜索算子用于开采当代最优个体周围的解空间,以提高算法的收敛速度。实验结果表明,对于测试算例,QEA-MOJSP的解接近Pareto最优解集前沿,并具有很好的多样性。     

基于自适应机制的多宇宙并行量子衍生进化算法

进化参量的选取对量子衍生进化算法(QEA)的优化性能有极大的影响,传统QEA在选择进化参量时并未考虑种群中个体间的差异,种群中所有个体采用相同的进化参量完成更新,导致算法在解决组合优化问题中存在收敛速度慢、容易陷入局部最优解等问题。针对这一问题,采用自适应机制调整QEA的旋转角步长和量子变异概率,算法中任意一代的任一个体的进化参量均由该个体自身适应度确定,从而保证尽可能多的进化个体能够朝着最优解方向不断靠近。此外,由于自适应量子进化算法需要评估个体的适应度,导致运算时间较长,针对这一问题则采用多宇宙机制将算法分布于多个宇宙中并行实现,从而提高算法的执行效率。通过搜索多峰函数最优解和求解背包问题测试算法性能,结果表明,与传统QEA相比,所提出算法在收敛速度、搜索全局最优解及执行速度方面具有较好的表现。     

NEQR量子图像下的差分扩展可逆数据隐藏算法

量子图像安全处理是一个新兴的研究领域,而量子图像数据隐藏是量子图像安全处理技术的一种,在不损害载体的情况下可用于保护量子图像的版权和认证量子图像是否完整。目前尚缺乏对量子图像可逆数据隐藏的详细技术研究。结合差值扩展技术,本文提出了一种量子图像可逆数据隐藏算法：1）选用NEQR量子图像表示法来表示图像;2）借鉴经典的差值扩展算法,在NEQR量子图像上对量子比特进行处理,可逆嵌入数据;3）设计了信息嵌入、信息提取和载体无损恢复的量子线路图,并进行了仿真。基于经典图像的实验结果表明,本文算法是可逆的,可用于将来对量子图像的认证和保护。     

Deep Web查询优化算法研究

Deep Web查询是在指分析接口属性及其丰富的语义信息后构造的用于向数据源请求特定数据的语句,其质量将影响查询结果相关度的高低和查询代价的大小.为优化查询,提出一种基于量子遗传算法的优化算法,以Deep Web查询的实数二进制串为输入进行量子编码,引入了球面解空间多子群并行寻优机制、群间染色体置换操作和量子变异算子以丰富种群多样性、提高算法的寻优效率.实验结果表明,该算法在R-Precision、覆盖率上具有一定的优势,能够有效地减少查询次数.     

Quantum Solution of Coordination Problems

We present a quantum solution to coordination problems that can be implemented with existing technologies. Using the properties of entangled states, this quantum mechanism allows participants to rapidly find suitable correlated choices as an alternative to conventional approaches relying on explicit communication, prior commitment or trusted third parties. Unlike prior proposals for quantum games our approach retains the same choices as in the classical game and instead utilizes quantum entanglement as an extra resource to aid the participants in their choices.     

量子位Bloch坐标的量子人工蜂群优化算法

为了改善人工蜂群(ABC)算法在解决多变量优化问题时存在的收敛速度较慢、容易陷入局部最优的不足,结合量子理论和人工蜂群算法提出一种新的量子优化算法。算法首先采用量子位Bloch坐标对蜂群算法中食物源进行编码,扩展了全局最优解的数量,提高了蜂群算法获得全局最优解的概率;然后用量子旋转门实现搜索过程中的食物源更新。对于量子旋转门的转角关系的确定,提出了一种新的方法。从理论上证明了蜂群算法在Bloch球面每次以等面积搜索时,量子旋转门的两个旋转相位大小近似于反比例关系,避免了固定相位旋转的不均等性,使得搜索呈现规律性。在典型函数优化问题的实验中,所提算法在搜索能力和优化效率两个方面优于普通量子人工蜂群(QABC)算法和单一人工蜂群算法。     

A Quantum Theory of Consciousness

The relationship between quantum collapse and consciousness is reconsidered under the assumption that quantum collapse is an objective dynamical process. We argue that the conscious observer can have a distinct role from the physical measuring device during the process of quantum collapse owing to the intrinsic nature of consciousness; the conscious observer can know whether he is in a definite state or a quantum superposition of definite states, while the physical measuring device cannot “know”. As a result, the consciousness observer can distinguish the definite states and their quantum superposition, while the physical measuring device without consciousness cannot do. This provides a possible quantum physical method to distinguish man and machine. The new result also implies that consciousness has causal efficacies in the physical world when considering the existence of quantum collapse. Accordingly consciousness is not reducible or emergent, but a new fundamental property of matter. This may establish a quantum basis for panpsychism, and make it be a promising solution to the hard problem of consciousness. Furthermore, it is suggested that a unified theory of matter and consciousness includes two parts: one is the psychophysical principle or corresponding principle between conscious content and matter state, and the other is the complete quantum evolution of matter state, which includes the definite nonlinear evolution element introduced by consciousness and relating to conscious content. Lastly, some experimental schemes are presented to test the proposed quantum theory of consciousness.

A study of heuristic guesses for adiabatic quantum computation

Adiabatic quantum computation (AQC) is a universal model for quantum computation which seeks to transform the initial ground state of a quantum system into a final ground state encoding the answer to a computational problem. AQC initial Hamiltonians conventionally have a uniform superposition as ground state. We diverge from this practice by introducing a simple form of heuristics: the ability to start the quantum evolution with a state which is a guess to the solution of the problem. With this goal in mind, we explain the viability of this approach and the needed modifications to the conventional AQC (CAQC) algorithm. By performing a numerical study on hard-to-satisfy 6 and 7 bit random instances of the satisfiability problem (3-SAT), we show how this heuristic approach is possible and we identify that the performance of the particular algorithm proposed is largely determined by the Hamming distance of the chosen initial guess state with respect to the solution. Besides the possibility of introducing educated guesses as initial states, the new strategy allows for the possibility of restarting a failed adiabatic process from the measured excited state as opposed to restarting from the full superposition of states as in CAQC. The outcome of the measurement can be used as a more refined guess state to restart the adiabatic evolution. This concatenated restart process is another heuristic that the CAQC strategy cannot capture.