Digital Quantum Simulation of the Statistical Mechanics of a Frustrated Magnet

Many interesting problems in physics, chemistry, and computer science are equivalent to problems of interacting spins. However, most of these problems require computational resources that are out of reach by classical computers. A promising solution to overcome this challenge is to exploit the laws of quantum mechanics to perform simulation. Several "analog" quantum simulations of interacting spin systems have been realized experimentally. However, relying on adiabatic techniques, these simulations are limited to preparing ground states only. Here we report the first experimental results on a "digital" quantum simulation on thermal states; we simulated a three-spin frustrated magnet, a building block of spin ice, with an NMR quantum information processor, and we are able to explore the phase diagram of the system at any simulated temperature and external field. These results serve as a guide for identifying the challenges for performing quantum simulation on physical systems at finite temperatures, and pave the way towards large scale experimental simulations of open quantum systems in condensed matter physics and chemistry.Comment: 7 pages for the main text plus 6 pages for the supplementary material

Systems biology driven software design for the research enterprise

<p>Abstract</p> <p>Background</p> <p>In systems biology, and many other areas of research, there is a need for the interoperability of tools and data sources that were not originally designed to be integrated. Due to the interdisciplinary nature of systems biology, and its association with high throughput experimental platforms, there is an additional need to continually integrate new technologies. As scientists work in isolated groups, integration with other groups is rarely a consideration when building the required software tools.</p> <p>Results</p> <p>We illustrate an approach, through the discussion of a purpose built software architecture, which allows disparate groups to reuse tools and access data sources in a common manner. The architecture allows for: the rapid development of distributed applications; interoperability, so it can be used by a wide variety of developers and computational biologists; development using standard tools, so that it is easy to maintain and does not require a large development effort; extensibility, so that new technologies and data types can be incorporated; and non intrusive development, insofar as researchers need not to adhere to a pre-existing object model.</p> <p>Conclusion</p> <p>By using a relatively simple integration strategy, based upon a common identity system and dynamically discovered interoperable services, a light-weight software architecture can become the focal point through which scientists can both get access to and analyse the plethora of experimentally derived data.</p

Networking - A Statistical Physics Perspective

Efficient networking has a substantial economic and societal impact in a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption require new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with non-linear large scale systems. This paper aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.Comment: (Review article) 71 pages, 14 figure

Broadband Coupling into a Single-Mode, Electroactive Integrated Optical Waveguide for Spectroelectrochemical Analysis of Surface-Confined Redox Couples

Pushing the sensitivity of spectroelectrochemical techniques to routinely monitor changes in spectral properties of thin molecular films (i.e., monolayer or submonolayer) adsorbed on an electrode surface has been a goal of many investigators since the earliest developments in this field. 1 It was initially recognized that exploiting the evanescent field generated by total internal reflection at the interface of an optically transparent electrode (such as a thin film of tin oxide or indium tin oxide (ITO) on glass or quartz) has the inherent advantage of selectively probing only the near-surface region, as opposed to bulk sampling with transmission based techniques. Furthermore, by utilizing the multiple reflections in an attenuated total reflectance (ATR) geometry, an enhancement in sensitivity can be realized, and as the thickness of the ATR element is decreased, the number of reflections increases, yielding a substantial sensitivity enhancement. [2][3][4][5][6] Itoh and Fujishima were the first to show the advantages of reducing the thickness of an ATR element overcoated with a transparent conductive oxide to the integrated optical waveguide (IOW) regime. Using a four-mode, gradient index waveguide coated with a transparent, conductive tin oxide layer, they demonstrated large sensitivity enhancements, relative to a single pass transmission experiment, for spectroelectrochemical measurements of methylene blue. 7,8 Other research groups subsequently described similar gradient index, multilayer, electroactive waveguide structures, but they did not make use of the technology to explore the spectroelectrochemistry of (sub)monolayer coverage films. [9][10][11][12][13] We recently described a single-mode, electroactive planar IOW (the EA-IOW) having a step refractive index profile. It was fabricated by sputtering a Corning 7059 glass layer (400 nm) over soda lime glass or quartz, followed by a 200-nm layer of SiO 2

Single-cell analysis: visualizing pharmaceutical and metabolite uptake in cells with label-free 3D mass spectrometry imaging

Detecting metabolites and parent compound within a cell type is now a priority for pharmaceutical development. In this context, three-dimensional secondary ion mass spectrometry (SIMS) imaging was used to investigate the cellular uptake of the antiarrhythmic agent amiodarone, a phospholipidosis-inducing pharmaceutical compound. The high lateral resolution and 3D imaging capabilities of SIMS combined with the multiplex capabilities of ToF mass spectrometric detection allows for the visualization of pharmaceutical compound and metabolites in single cells. The intact, unlabeled drug compound was successfully detected at therapeutic dosages in macrophages (cell line: NR8383). Chemical information from endogenous biomolecules was used to correlate drug distributions with morphological features. From this spatial analysis, amiodarone was detected throughout the cell with the majority of the compound found in the membrane and subsurface regions and absent in the nuclear regions. Similar results were obtained when the macrophages were doped with amiodarone metabolite, desethylamiodarone. The FWHM lateral resolution measured across an intracellular interface in a high lateral resolution ion images was approximately 550 nm. Overall, this approach provides the basis for studying cellular uptake of pharmaceutical compounds and their metabolites on the single cell level

What is behind a summary-evaluation decision?

Research in psychology has reported that, among the variety of possibilities for assessment methodologies, summary evaluation offers a particularly adequate context for inferring text comprehension and topic understanding. However, grades obtained in this methodology are hard to quantify objectively. Therefore, we carried out an empirical study to analyze the decisions underlying human summary-grading behavior. The task consisted of expert evaluation of summaries produced in critically relevant contexts of summarization development, and the resulting data were modeled by means of Bayesian networks using an application called Elvira, which allows for graphically observing the predictive power (if any) of the resultant variables. Thus, in this article, we analyzed summary-evaluation decision making in a computational framewor