Quantum computing implementations with neutral particles

We review quantum information processing with cold neutral particles, that is, atoms or polar molecules. First, we analyze the best suited degrees of freedom of these particles for storing quantum information, and then we discuss both single- and two-qubit gate implementations. We focus our discussion mainly on collisional quantum gates, which are best suited for atom-chip-like devices, as well as on gate proposals conceived for optical lattices. Additionally, we analyze schemes both for cold atoms confined in optical cavities and hybrid approaches to entanglement generation, and we show how optimal control theory might be a powerful tool to enhance the speed up of the gate operations as well as to achieve high fidelities required for fault tolerant quantum computation.     

Visualizing and fuzzy filtering for discovering temporal trajectories of association rules

We propose a user-centric rule filtering method that allows to identify association rules that exhibit a certain user-specified temporal behavior with respect to rule evaluation measures. The method can considerably reduce the number of association rules that have to be assessed manually after a rule induction. This is especially necessary if the rule set contains many rules as it is the case for the task of finding rare patterns inside the data. For the proposed method, we will reuse former work on the visualization of association rules [M. Steinbrecher, R. Kruse, Visualization of possibilistic potentials, in: Foundations of Fuzzy Logic and Soft Computing, in: Lecture Notes in Comput. Sci., vol. 4529, Springer-Verlag, Berlin/Heidelberg, 2007, pp. 295–303] and use an extension of it to motivate and assess the presented filtering technique. We put the focus on rules that are induced from a data set that contains a temporal variable and build our approach on the requirement that temporally ordered sets of association rules are available, i.e., one set for every time frame. To illustrate this, we propose an ad-hoc learning method along the way. The actual rule filtering is accomplished by means of fuzzy concepts. These concepts use linguistic variables to partition rule-related domains of interest, such as the confidence change rate. The original rule sets are then matched against these user concepts and result in only those rules that match the respective concepts to a predefined extent. We provide empirical evidence by applying the proposed methods to hand-crafted as well as real-world data sets and critically discuss the current state and further prospects.     

Monte Carlo techniques for phrase-based translation

Recent advances in statistical machine translation have used approximate beam search for NP-complete inference within probabilistic translation models. We present an alternative approach of sampling from the posterior distribution defined by a translation model. We define a novel Gibbs sampler for sampling translations given a source sentence and show that it effectively explores this posterior distribution. In doing so we overcome the limitations of heuristic beam search and obtain theoretically sound solutions to inference problems such as finding the maximum probability translation and minimum risk training and decoding.     

团分划问题的固定参数算法研究

图论中的团分划问题属于NP-完全问题,难以在多项式时间内解决。为此,对团分划问题的固定参数算法进行研究,提出一个针对K4-free图的新归约法则,结合深度限制搜索树技术对K4-free图中的团分划固定参数可解类算法做出改进。实验结果表明,与原算法相比,在稀疏图的情况下改进算法效率提高了30%。     

BAIS: A Bayesian Artificial Immune System for the effective handling of building blocks

Significant progress has been made in theory and design of Artificial Immune Systems (AISs) for solving hard problems accurately. However, an aspect not yet widely addressed by the research reported in the literature is the lack of ability of the AISs to deal effectively with building blocks (partial high-quality solutions coded in the antibody). The available AISs present mechanisms for evolving the population that do not take into account the relationship among the variables of the problem, potentially causing the disruption of high-quality partial solutions. This paper proposes a novel AIS with abilities to identify and properly manipulate building blocks in optimization problems. Instead of using cloning and mutation to generate new individuals, our algorithm builds a probabilistic model representing the joint probability distribution of the promising solutions and, subsequently, uses this model for sampling new solutions. The probabilistic model used is a Bayesian network due to its capability of properly capturing the most relevant interactions among the variables. Therefore, our algorithm, called Bayesian Artificial Immune System (BAIS), represents a significant attempt to improve the performance of immune-inspired algorithms when dealing with building blocks, and hence to solve efficiently hard optimization problems with complex interactions among the variables. The performance of BAIS compares favorably with that produced by contenders such as state-of-the-art Estimation of Distribution Algorithms.     

Pattern classification with mixtures of weighted least-squares support vector machine experts

Support Vector Machine (SVM) classifiers are high-performance classification models devised to comply with the structural risk minimization principle and to properly exploit the kernel artifice of nonlinearly mapping input data into high-dimensional feature spaces toward the automatic construction of better discriminating linear decision boundaries. Among several SVM variants, Least-Squares SVMs (LS-SVMs) have gained increased attention recently due mainly to their computationally attractive properties coming as the direct result of applying a modified formulation that makes use of a sum-squared-error cost function jointly with equality, instead of inequality, constraints. In this work, we present a flexible hybrid approach aimed at augmenting the proficiency of LS-SVM classifiers with regard to accuracy/generalization as well as to hyperparameter calibration issues. Such approach, named as Mixtures of Weighted Least-Squares Support Vector Machine Experts, centers around the fusion of the weighted variant of LS-SVMs with Mixtures of Experts models. After the formal characterization of the novel learning framework, simulation results obtained with respect to both binary and multiclass pattern classification problems are reported, ratifying the suitability of the novel hybrid approach in improving the performance issues considered.     

Nano-texturing of magnetic recording sliders via laser ablation

To increase the storage density of hard disk drives, the flying height of the slider needs to be reduced to <10 nm. This requires super-smooth surfaces of the disk and slider. As the roughness decreases, stiction and adhesion are found to increase substantially leading to failures of the head/disk interface. Texturing the slider surface is a well-known approach to this issue. In this study we investigated laser ablation as a potential process for texturing magnetic recording sliders. It was found that straight laser machining caused unwanted re-deposition of material. These deposits could be significantly reduced by using a chemical etching enhanced laser process.     

Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process

In the present work, a constructive learning algorithm was employed to design a near-optimal one-hidden layer neural network structure that best approximates the dynamic behavior of a bioprocess. The method determines not only a proper number of hidden neurons but also the particular shape of the activation function for each node. Here, the projection pursuit technique was applied in association with the optimization of the solvability condition, giving rise to a more efficient and accurate computational learning algorithm. As each activation function of a hidden neuron is defined according to the peculiarities of each approximation problem, better rates of convergence are achieved, guiding to parsimonious neural network architectures. The proposed constructive learning algorithm was successfully applied to identify a MIMO bioprocess, providing a multivariable model that was able to describe the complex process dynamics, even in long-range horizon predictions. The resulting identification model was considered as part of a model-based predictive control strategy, producing high-quality performance in closed-loop experiments.     

Continuous-time quantum Monte Carlo impurity solvers

Continuous-time quantum Monte Carlo impurity solvers are algorithms that sample the partition function of an impurity model using diagrammatic Monte Carlo techniques. The present paper describes codes that implement the interaction expansion algorithm originally developed by Rubtsov, Savkin, and Lichtenstein, as well as the hybridization expansion method developed by Werner, Millis, Troyer, et al. These impurity solvers are part of the ALPS-DMFT application package and are accompanied by an implementation of dynamical mean-field self-consistency equations for (single orbital single site) dynamical mean-field problems with arbitrary densities of states.

Program summary

Program title: dmftCatalogue identifier: AEIL_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEIL_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: ALPS LIBRARY LICENSE version 1.1No. of lines in distributed program, including test data, etc.: 899 806No. of bytes in distributed program, including test data, etc.: 32 153 916Distribution format: tar.gzProgramming language: C++Operating system: The ALPS libraries have been tested on the following platforms and compilers:

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  Linux with GNU Compiler Collection (g++ version 3.1 and higher), and Intel C++ Compiler (icc version 7.0 and higher)

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  MacOS X with GNU Compiler (g++ Apple-version 3.1, 3.3 and 4.0)

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  IBM AIX with Visual Age C++ (xlC version 6.0) and GNU (g++ version 3.1 and higher) compilers

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  Compaq Tru64 UNIX with Compq C++ Compiler (cxx)

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  SGI IRIX with MIPSpro C++ Compiler (CC)

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  HP-UX with HP C++ Compiler (aCC)

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  Windows with Cygwin or coLinux platforms and GNU Compiler Collection (g++ version 3.1 and higher)

RAM: 10 MB–1 GBClassification: 7.3External routines: ALPS [1], BLAS/LAPACK, HDF5Nature of problem: (See [2].) Quantum impurity models describe an atom or molecule embedded in a host material with which it can exchange electrons. They are basic to nanoscience as representations of quantum dots and molecular conductors and play an increasingly important role in the theory of “correlated electron” materials as auxiliary problems whose solution gives the “dynamical mean field” approximation to the self-energy and local correlation functions.Solution method: Quantum impurity models require a method of solution which provides access to both high and low energy scales and is effective for wide classes of physically realistic models. The continuous-time quantum Monte Carlo algorithms for which we present implementations here meet this challenge. Continuous-time quantum impurity methods are based on partition function expansions of quantum impurity models that are stochastically sampled to all orders using diagrammatic quantum Monte Carlo techniques. For a review of quantum impurity models and their applications and of continuous-time quantum Monte Carlo methods for impurity models we refer the reader to [2].Additional comments: Use of dmft requires citation of this paper. Use of any ALPS program requires citation of the ALPS [1] paper.Running time: 60 s–8 h per iteration.References:

• [1] 

  A. Albuquerque, F. Alet, P. Corboz, et al., J. Magn. Magn. Mater. 310 (2007) 1187.

• [2] 

  http://arxiv.org/abs/1012.4474, Rev. Mod. Phys., in press.

     

Two‐Level Grids for Ray Tracing on GPUs

We investigate the use of two‐level nested grids as acceleration structure for ray tracing of dynamic scenes. We propose a massively parallel, sort‐based construction algorithm and show that the two‐level grid is one of the structures that is fastest to construct on modern graphics processors. The structure handles non‐uniform primitive distributions more robustly than the uniform grid and its traversal performance is comparable to those of other high quality acceleration structures used for dynamic scenes. We propose a cost model to determine the grid resolution and improve SIMD utilization during ray‐triangle intersection by employing a hybrid packetization strategy. The build times and ray traversal acceleration provide overall rendering performance superior to previous approaches for real time rendering of animated scenes on GPUs.