Conceptual aspects of geometric quantum computation

Geometric quantum computation is the idea that geometric phases can be used to implement quantum gates, i.e., the basic elements of the Boolean network that forms a quantum computer. Although originally thought to be limited to adiabatic evolution, controlled by slowly changing parameters, this form of quantum computation can as well be realized at high speed by using nonadiabatic schemes. Recent advances in quantum gate technology have allowed for experimental demonstrations of different types of geometric gates in adiabatic and nonadiabatic evolution. Here, we address some conceptual issues that arise in the realizations of geometric gates. We examine the appearance of dynamical phases in quantum evolution and point out that not all dynamical phases need to be compensated for in geometric quantum computation. We delineate the relation between Abelian and non-Abelian geometric gates and find an explicit physical example where the two types of gates coincide. We identify differences and similarities between adiabatic and nonadiabatic realizations of quantum computation based on non-Abelian geometric phases.     

Multigrid gradient vector flow computation on the GPU

Gradient vector flow (GVF) is a feature-preserving spatial diffusion of image gradients. It was introduced to overcome the limited capture range in traditional active contour segmentation. However, the original iterative solver for GVF, using Euler’s method, converges very slowly. Thus, many iterations are needed to achieve the desired capture range. Several groups have investigated the use of graphic processing units (GPUs) to accelerate the GVF computation. Still, this does not reduce the number of iterations needed. Multigrid methods, on the other hand, have been shown to provide a much better capture range using considerable less iterations. However, non-GPU implementations of the multigrid method are not as fast as the Euler method when executed on the GPU. In this paper, a novel GPU implementation of a multigrid solver for GVF written in OpenCL is presented. The results show that this implementation converges and provides a better capture range about 2–5 times faster than the conventional iterative GVF solver on the GPU.     

A simple approach to neutral atom microscopy

Scanning surfaces using a beam of noncharged atoms or molecules allows for especially nondestructive and low-energy surface imaging, with the potential to obtain new information about surfaces that cannot be easily obtained otherwise. We have developed a new approach, operating with the sample at a close working distance from an aperture, the need for optics to focus the beam is obviated. Compared to more complex approaches, the theoretical performance has no other disadvantage than the short working distance. Resolution of 1.5 μm has been achieved, and submicron resolution appears to be practical. Construction of the microscope and results are presented, including first images done in reflection mode, theory for optimization of the design and avenues for future improvement.     

Note: The intermodulation lockin analyzer

Nonlinear systems can be probed by driving them with two or more pure tones while measuring the intermodulation products of the drive tones in the response. We describe a digital lockin analyzer which is designed explicitly for this purpose. The analyzer is implemented on a field-programmable gate array, providing speed in analysis, real-time feedback, and stability in operation. The use of the analyzer is demonstrated for intermodulation atomic force microscopy. A generalization of the intermodulation spectral technique to arbitrary drive waveforms is discussed.     

An optimization algorithm for multimodal functions inspired by collective animal behavior

Interest in multimodal function optimization is expanding rapidly as real-world optimization problems often demand locating multiple optima within a search space. This article presents a new multimodal optimization algorithm named as the collective animal behavior. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, searcher agents are a group of animals which interact with each other based on the biologic laws of collective motion. Experimental results demonstrate that the proposed algorithm is capable of finding global and local optima of benchmark multimodal optimization problems with a higher efficiency in comparison with other methods reported in the literature.     

Topology and thickness optimization of laminated composites including manufacturing constraints

This paper presents a gradient based optimization method for large-scale topology and thickness optimization of fiber reinforced monolithic laminated composite structures including certain manufacturing constraints to attain industrial relevance. This facilitates application of predefined fiber mats and reduces the risk of failure such as delamination and matrix cracking problems. The method concerns simultaneous determination of the optimum thickness and fiber orientation throughout a laminated structure with fixed outer geometry. The laminate thickness may vary as an integer number of plies, and possible fiber orientations are limited to a finite set. The conceptual combinatorial problem is relaxed to a continuous problem and solved on basis of interpolation schemes with penalization through the so-called Discrete Material Optimization method, explicitly including manufacturing constraints as a large number of sparse linear constraints. The methodology is demonstrated on several numerical examples.     

Water quality in the southern Tibetan Plateau: chemical evaluation of the Yarlung Tsangpo (Brahmaputra)

Yarlung Tsangpo (Brahmaputra) is the largest river system draining the northern slopes of the Himalayan ranges on the southern Tibetan Plateau. It remains one of only two large non‐regulated rivers in China. In this paper the chemical composition of Yarlung Tsangpo and its major tributaries (Raga Tsangpo, Nyangchu and Lhasa River) are studied. Water samples (n = 55) were collected and measured for major ions, trace elements and nutrients in order to: (1) define the present chemical quality of this water course; (2) address possible mechanisms governing the water chemical compositions, and (3) identify potential sources for contaminants. Multivariable analysis shows that geology and climate are the major explanatory variables for the spatial variation in water chemistry in this river system. In general, water chemistry is mainly controlled by carbonate weathering, with Ca²⁺ and HCO being the dominant ions. In addition, runoff from brackish/saline lakes and geothermal waters, enriched in Na⁺, Cl^?, SO, Mg²⁺ and Li, are major contributors of elevated concentrations of these solutes in the headwater regions resulting in a relatively high loading of total dissolved solids (TDS, 146–397 mg L^?1). Levels of most heavy metals and total dissolved nutrients were generally found to be low. However, elevated As concentration (avg. 95 μg L^?1) in the headwaters and additions from untreated wastewater were evident at some locations. Copyright © 2009 John Wiley & Sons, Ltd.     

An Empirical Study on Views of Importance of Change Impact Analysis Issues

Change impact analysis is a change management activity that previously has been studied much from a technical perspective. For example, much work focuses on methods for determining the impact of a change. In this paper, we present results from a study on the role of impact analysis in the change management process. In the study, impact analysis issues were prioritised with respect to criticality by software professionals from an organisational perspective and a self-perspective. The software professionals belonged to three organisational levels: operative, tactical and strategic. Qualitative and statistical analyses with respect to differences between perspectives as well as levels are presented. The results show that important issues for a particular level are tightly related to how the level is defined. Similarly, issues important from an organisational perspective are more holistic than those important from a self-perspective. However, our data indicate that the self-perspective colours the organisational perspective, meaning that personal opinions and attitudes cannot easily be disregarded. In comparing the perspectives and the levels, we visualise the differences in a way that allow us to discuss two classes of issues: high-priority and medium-priority. The most important issues from this point of view concern fundamental aspects of impact analysis and its execution.     

Grid resource brokering algorithms enabling advance reservations and resource selection based on performance predictions

We present algorithms, methods, and software for a Grid resource manager, that performs resource brokering and job scheduling in production Grids. This decentralized broker selects computational resources based on actual job requirements, job characteristics, and information provided by the resources, with the aim to minimize the total time to delivery for the individual application. The total time to delivery includes the time for program execution, batch queue waiting, and transfer of executable and input/output data to and from the resource. The main features of the resource broker include two alternative approaches to advance reservations, resource selection algorithms based on computer benchmark results and network performance predictions, and a basic adaptation facility. The broker is implemented as a built-in component of a job submission client for the NorduGrid/ARC middleware.     

Optimally Adaptive Integration of Univariate Lipschitz Functions

We consider the problem of approximately integrating a Lipschitz function f (with a known Lipschitz constant) over an interval. The goal is to achieve an additive error of at most ε using as few samples of f as possible. We use the adaptive framework: on all problem instances an adaptive algorithm should perform almost as well as the best possible algorithm tuned for the particular problem instance. We distinguish between and , the performances of the best possible deterministic and randomized algorithms, respectively. We give a deterministic algorithm that uses samples and show that an asymptotically better algorithm is impossible. However, any deterministic algorithm requires samples on some problem instance. By combining a deterministic adaptive algorithm and Monte Carlo sampling with variance reduction, we give an algorithm that uses at most samples. We also show that any algorithm requires samples in expectation on some problem instance (f,ε), which proves that our algorithm is optimal.