Neural network decoder for topological color codes with circuit level noise

A quantum computer needs the assistance of a classical algorithm to detect and identify errors that affect encoded quantum information. At this interface of classical and quantum computing the technique of machine learning has appeared as a way to tailor such an algorithm to the specific error processes of an experiment --- without the need for a priori knowledge of the error model. Here, we apply this technique to topological color codes. We demonstrate that a recurrent neural network with long short-term memory cells can be trained to reduce the error rate $\epsilon_{\rm L}$ of the encoded logical qubit to values much below the error rate $\epsilon_{\rm phys}$ of the physical qubits --- fitting the expected power law scaling $\epsilon_{\rm L} \propto \epsilon_{\rm phys}^{(d+1)/2}$, with $d$ the code distance. The neural network incorporates the information from "flag qubits" to avoid reduction in the effective code distance caused by the circuit. As a test, we apply the neural network decoder to a density-matrix based simulation of a superconducting quantum computer, demonstrating that the logical qubit has a longer life-time than the constituting physical qubits with near-term experimental parameters.Comment: 10 pages, 9 figures; V2: updated text and figure

Clone wars:asexual reproduction dominates in the invasive range of Tubastraea spp. (Anthozoa: Scleractinia) in the South-Atlantic Ocean

Although the invasive azooxanthellate corals Tubastraea coccinea and T. tagusensis are spreading quickly and outcompeting native species in the Atlantic Ocean, there is little information regarding the genetic structure and path of introduction for these species. Here we present the first data on genetic diversity and clonal structure from these two species using a new set of microsatellite markers. High proportions of clones were observed, indicating that asexual reproduction has a major role in the local population dynamics and, therefore, represents one of the main reasons for the invasion success. Although no significant population structure was found, results suggest the occurrence of multiple invasions for T. coccinea and also that both species are being transported along the coast by vectors such as oil platforms and monobouys, spreading these invasive species. In addition to the description of novel microsatellite markers, this study sheds new light into the invasive process of Tubastraea.Coordenacao de Aperfeicoamento de Pessoal de Nivel SuperiorFundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de JaneiroConselho Nacional de Desenvolvimento Cientifico e TecnologicoFundacao de Amparo a Pesquisa do Estado de Sao PauloNSF-OA (National Science Foundation)Univ Fed Rio de Janeiro, Dept Zool, Rio De Janeiro, BrazilUniv Hawaii Manoa, Hawaii Inst Marine Biol, Sch Ocean & Earth Sci & Technol, Kaneohe, HI USACoral Sol Res Technol Dev & Innovat Network, Rio De Janeiro, BrazilUniv Fed Rio de Janeiro, Inst Microbiol Paulo Goes, Rio De Janeiro, BrazilUniv Estado Rio de Janeiro, Dept Ecol, Rio De Janeiro, BrazilUniv Fed Sao Paulo, Dept Ciencias Mar, Santos, BrazilUniv Sao Paulo, Ctr Biol Marinha, Sao Sebastiao, BrazilUniv Fed Sao Paulo, Dept Ciencias Mar, Santos, BrazilCAPES: 1137/2010FAPERJ: E26/010.003031/2014FAPERJ: E26/201.286/2014CNPq: 305330/2010-1FAPESP: 2014/01332-0Web of Scienc

Facing Erosion Identification in Railway Lines Using Pixel-wise Deep-based Approaches

Soil erosion is considered one of the most expensive natural hazards with a high impact on several infrastructure assets. Among them, railway lines are one of the most likely constructions for the appearance of erosion and, consequently, one of the most troublesome due to the maintenance costs, risks of derailments, and so on. Therefore, it is fundamental to identify and monitor erosion in railway lines to prevent major consequences. Currently, erosion identification is manually performed by humans using huge image sets, a time-consuming and slow task. Hence, automatic machine learning methods appear as an appealing alternative. A crucial step for automatic erosion identification is to create a good feature representation. Towards such objective, deep learning can learn data-driven features and classifiers. In this paper, we propose a novel deep learning-based framework capable of performing erosion identification in railway lines. Six techniques were evaluated and the best one, Dynamic Dilated ConvNet, was integrated into this framework that was then encapsulated into a new ArcGIS plugin to facilitate its use by non-programmer users. To analyze such techniques, we also propose a new dataset, composed of almost 2,000 high-resolution images

Department of Pathogenic Fungi, Division of Fungal Resources and Development

<p>Average relative abundance of bacterial phyla composition in root apex samples from teeth with post-treatment apical periodontitis.</p

Characteristics of tropical–extratropical cloud bands over tropical and subtropical South America simulated by BAM-1.2 and HadGEM3-GC3.1

Tropical–extratropical cloud bands are common in South America (SAm), contributing significantly to the total rainy season precipitation. Thus, it is fundamental that climate and weather forecast models correctly represent them and their associated dynamic aspects. Adopting an event-based framework, we evaluate the performance of two global models in simulating the observed cloud bands over SAm: the Brazilian Global Atmospheric Model version 1.2 (BAM-1.2) and the Hadley Centre Global Environment Model in the Global Coupled configuration 3.1 (HadGEM3-GC3.1). Both models reproduce the main characteristics of cloud bands and the dynamical aspects leading to their development and persistence. Nonetheless, the biases in precipitation during simulated cloud bands contribute more than 50% of the bias in total precipitation in some regions. BAM-1.2 simulates fewer but more persistent cloud bands than observed; HadGEM3-GC3.1 simulates weaker cloud band activity during early summer and more persistent events after January than observed. In all models, the biases in cloud band events arise from the interaction between biases in the basic state and the synoptic-scale regional circulation. In the basic state, stronger upper level westerlies over the midlatitude South Pacific support the propagation of longer and slower Rossby waves towards subtropical SAm, increasing the duration of the cloud band events. This bias interacts with negative biases in the upper level westerlies over subtropical SAm, increasing the wind shear, hindering the propagation of synoptic-scale Rossby waves into lower latitudes, and resulting in biases in the cloud band location, intensity, and seasonality. The application in this study of an event-based framework robust to differences in model resolution and complexity enables the identification of small but critical biases in circulation. These biases are linked to synoptic-scale rainfall system biases and help to explain the season total rainfall model biases

Orthogonality Catastrophe in Parametric Random Matrices

We study the orthogonality catastrophe due to a parametric change of the single-particle (mean field) Hamiltonian of an ergodic system. The Hamiltonian is modeled by a suitable random matrix ensemble. We show that the overlap between the original and the parametrically modified many-body ground states, $S$, taken as Slater determinants, decreases like $n^{-k x^2}$, where $n$ is the number of electrons in the systems, $k$ is a numerical constant of the order of one, and $x$ is the deformation measured in units of the typical distance between anticrossings. We show that the statistical fluctuations of $S$ are largely due to properties of the levels near the Fermi energy.Comment: 12 pages, 8 figure

Coulomb blockade conductance peak fluctuations in quantum dots and the independent particle model

We study the combined effect of finite temperature, underlying classical dynamics, and deformations on the statistical properties of Coulomb blockade conductance peaks in quantum dots. These effects are considered in the context of the single-particle plus constant-interaction theory of the Coulomb blockade. We present numerical studies of two chaotic models, representative of different mean-field potentials: a parametric random Hamiltonian and the smooth stadium. In addition, we study conductance fluctuations for different integrable confining potentials. For temperatures smaller than the mean level spacing, our results indicate that the peak height distribution is nearly always in good agreement with the available experimental data, irrespective of the confining potential (integrable or chaotic). We find that the peak bunching effect seen in the experiments is reproduced in the theoretical models under certain special conditions. Although the independent particle model fails, in general, to explain quantitatively the short-range part of the peak height correlations observed experimentally, we argue that it allows for an understanding of the long-range part.Comment: RevTex 3.1, 34 pages (including 13 EPS and PS figures), submitted to Phys. Rev.

Data security and trading framework for smart grids in neighborhood area networks

Due to the drastic increase of electricity prosumers, i.e., energy consumers that are also producers, smart grids have become a key solution for electricity infrastructure. In smart grids, one of the most crucial requirements is the privacy of the final users. The vast majority of the literature addresses the privacy issue by providing ways of hiding user’s electricity consumption. However, open issues in the literature related to the privacy of the electricity producers still remain. In this paper, we propose a framework that preserves the secrecy of prosumers’ identities and provides protection against the traffic analysis attack in a competitive market for energy trade in a Neighborhood Area Network (NAN). In addition, the amount of bidders and of successful bids are hidden from malicious attackers by our framework. Due to the need for small data throughput for the bidders, the communication links of our framework are based on a proprietary communication system. Still, in terms of data security, we adopt the Advanced Encryption Standard (AES) 128bit with Exclusive-OR (XOR) keys due to their reduced computational complexity, allowing fast processing. Our framework outperforms the state-of-the-art solutions in terms of privacy protection and trading flexibility in a prosumer-to-prosumer design