Constraints on the cosmic ray diffusion coefficient in the W28 region from gamma-ray observations

GeV and TeV gamma rays have been detected from the supernova remnant W28 and its surroundings. Such emission correlates quite well with the position of dense and massive molecular clouds and thus it is often interpreted as the result of hadronic cosmic ray interactions in the dense gas. Constraints on the cosmic ray diffusion coefficient in the region can be obtained, under the assumption that the cosmic rays responsible for the gamma ray emission have been accelerated in the past at the supernova remnant shock, and subsequently escaped in the surrounding medium. In this scenario, gamma ray observations can be explained only if the diffusion coefficient in the region surrounding the supernova remnant is significantly suppressed with respect to the average galactic one.Comment: To appear in the proceedings of "Journ\'ees de la SF2A 2010" Marseille 21-24 June 2010, 4 pages, 4 figure

Quality Assurance Indicators of Long-Term Care in European Countries

This study reports on the quality indicators that were collected by the ANCIEN project partners in each country considered in Work Package 5 (Quality in Long-Term Care). The main contribution of this report is a classification of the quality assurance indicators in different European countries according to three dimensions: organisation type (indicators applied to formal institutional care \u2013 FIC, formal home-based care \u2013 FHBC, formal home nursing care - FHNC, and informal home care - IHC); quality dimensions (indicators about effectiveness, safety, patient value responsiveness, or coordination) and system dimensions (input, process, or outcome indicators). The countries that provided quality indicators, which are used at a national level or are recommended to be used at a local level by a national authority, are: Estonia, Finland, France, Germany, Hungary, Italy, Latvia, the Netherlands, Spain, Sweden and the United Kingdom. In total, we collected 390 quality indicators. Each quality indicator has been assigned to one or more options in each dimension

Deep Strong Coupling Regime of the Jaynes-Cummings model

We study the quantum dynamics of a two-level system interacting with a quantized harmonic oscillator in the deep strong coupling regime (DSC) of the Jaynes-Cummings model, that is, when the coupling strength g is comparable or larger than the oscillator frequency w (g/w > 1). In this case, the rotating-wave approximation cannot be applied or treated perturbatively in general. We propose an intuitive and predictive physical frame to describe the DSC regime where photon number wavepackets bounce back and forth along parity chains of the Hilbert space, while producing collapse and revivals of the initial population. We exemplify our physical frame with numerical and analytical considerations in the qubit population, photon statistics, and Wigner phase space.Comment: Published version, note change of title: DSC regime of the JC mode

Zeno physics in ultrastrong circuit QED

We study the Zeno and anti-Zeno effects in a superconducting qubit interacting strongly and ultrastrongly with a microwave resonator. Using a model of a frequently measured two-level system interacting with a quantized mode, we show different behaviors and total control of the Zeno times depending on whether the rotating-wave approximation can be applied in the Jaynes-Cummings model, or not. We exemplify showing the strong dependence of our results with the properties of the initial field states and suggest applications for quantum tomography.Comment: 5 pages, 3 figure

Digital Quantum Simulation of the Holstein Model in Trapped Ions

We propose the implementation of the Holstein model by means of digital methods in a linear chain of trapped ions. We show how the simulation fidelity scales with the generation of phononic excitations. We propose a decomposition and a stepwise trapped-ion implementation of the Holstein Hamiltonian. Via numerical simulations, we study how the protocol is affected by realistic gates. Finally, we show how measurements of the size of the simulated polaron can be performed.Comment: 5 pages + supplemental material, 3+3 figures. Accepted in Physical Review Letter

Measuring Entanglement in a Photonic Embedding Quantum Simulator

Measuring entanglement is a demanding task that usually requires full tomography of a quantum system, involving a number of observables that grows exponentially with the number of parties. Recently, it was suggested that adding a single ancillary qubit would allow for the efficient measurement of concurrence, and indeed any entanglement monotone associated to antilinear operations. Here, we report on the experimental implementation of such a device---an embedding quantum simulator---in photonics, encoding the entangling dynamics of a bipartite system into a tripartite one. We show that bipartite concurrence can be efficiently extracted from the measurement of merely two observables, instead of fifteen, without full tomographic information.Comment: Updated versio

A systematic comparison of supervised classifiers

Pattern recognition techniques have been employed in a myriad of industrial, medical, commercial and academic applications. To tackle such a diversity of data, many techniques have been devised. However, despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, the consideration of as many as possible techniques presents itself as an fundamental practice in applications aiming at high accuracy. Typical works comparing methods either emphasize the performance of a given algorithm in validation tests or systematically compare various algorithms, assuming that the practical use of these methods is done by experts. In many occasions, however, researchers have to deal with their practical classification tasks without an in-depth knowledge about the underlying mechanisms behind parameters. Actually, the adequate choice of classifiers and parameters alike in such practical circumstances constitutes a long-standing problem and is the subject of the current paper. We carried out a study on the performance of nine well-known classifiers implemented by the Weka framework and compared the dependence of the accuracy with their configuration parameter configurations. The analysis of performance with default parameters revealed that the k-nearest neighbors method exceeds by a large margin the other methods when high dimensional datasets are considered. When other configuration of parameters were allowed, we found that it is possible to improve the quality of SVM in more than 20% even if parameters are set randomly. Taken together, the investigation conducted in this paper suggests that, apart from the SVM implementation, Weka's default configuration of parameters provides an performance close the one achieved with the optimal configuration