Effective field model of roughness in magnetic nano-structures

An effective field model is introduced here within the micromagnetics formulation, to study roughness in magnetic structures, by considering sub-exchange length roughness levels as a perturbation on a smooth structure. This allows the roughness contribution to be separated, which is found to give rise to an effective configurational anisotropy for both edge and surface roughness, and accurately model its effects with fine control over the roughness depth without the explicit need to refine the computational cell size to accommodate the roughness profile. The model is validated by comparisons with directly roughened structures for a series of magnetization switching and domain wall velocity simulations and found to be in excellent agreement for roughness levels up to the exchange length. The model is further applied to vortex domain wall velocity simulations with surface roughness, which is shown to significantly modify domain wall movement and result in dynamic pinning and stochastic creep effects

Optimal control of a qubit coupled to a non-Markovian environment

A central challenge for implementing quantum computing in the solid state is decoupling the qubits from the intrinsic noise of the material. We investigate the implementation of quantum gates for a paradigmatic, non-Markovian model: A single qubit coupled to a two-level system that is exposed to a heat bath. We systematically search for optimal pulses using a generalization of the novel open systems Gradient Ascent Pulse Engineering (GRAPE) algorithm. We show and explain that next to the known optimal bias point of this model, there are optimal shapes which refocus unwanted terms in the Hamiltonian. We study the limitations of controls set by the decoherence properties. This can lead to a significant improvement of quantum operations in hostile environments.Comment: 5 pages, 3 figures, improved pulse shape

Polymerized Hemin as An Electrocatalytic Platform for Peroxynitrite\u27s Oxidation and Detection

Peroxynitrite (ONOO−) constitutes a major cytotoxic agent, implicated in a host of pathophysiological conditions, thereby stimulating a tremendous interest in evaluating its role as an oxidant in vivo. Some of the detection methods for peroxynitrite include oxidation of fluorescent probes, EPR spectroscopy, chemiluminescence, immunohistochemistry, and probe nitration; however, these are more difficult to apply for real-time quantification due to their inherent complexity. The electrochemical detection of peroxynitrite is a simpler and more convenient technique, but the best of our knowledge there are only few papers to date studying its electrochemical signature, or reporting amperometric microsensors for peroxynitrite. Recently, we have reported the use of layered composite films of poly(3,4-ethylenedioxythiophene) (PEDOT) and hemin (iron protoporphyrin IX) as a platform for amperometric measurement of peroxynitrite. The main goal herein is to investigate the intrinsic catalytic role of hemin electropolymerized thin films on carbon electrodes in oxidative detection of peroxynitrite. The electrocatalytic oxidation of peroxynitrite is characterized by cyclic voltammetry. The catalytic current increased as a function of peroxynitrite\u27s concentration, with a peak potential shifting positively with peroxynitrite\u27s concentration. The catalytic efficiency decreased as the scan rate increased, and the peak potential of the catalytic oxidation was found to depend on pH. We show that optimized hemin-functionalized carbon electrodes can be used as simple platforms for peroxinitrite detection and quantification. We report dose–response amperometry as an electroanalytical determination of this analyte on hemin films and we contrast the intrinsic hemin catalytic role with its performance in the case of the PEDOT–hemin as a composite matrix. Finally, we include some work extending the use of simple hemin films for peroxynitrite determination on carbon microfiber electrodes in a flow system

Quantum Nondemolition-Like, Fast Measurement Scheme for a Superconducting Qubit

We present a measurement protocol for a flux qubit coupled to a dc-Superconducting QUantum Interference Device (SQUID), representative of any two-state system with a controllable coupling to an harmonic oscillator quadrature, which consists of two steps. First, the qubit state is imprinted onto the SQUID via a very short and strong interaction. We show that at the end of this step the qubit dephases completely, although the perturbation of the measured qubit observable during this step is weak. In the second step, information about the qubit is extracted by measuring the SQUID. This step can have arbitrarily long duration, since it no longer induces qubit errors

Automated data pre-processing via meta-learning

The final publication is available at link.springer.comA data mining algorithm may perform differently on datasets with different characteristics, e.g., it might perform better on a dataset with continuous attributes rather than with categorical attributes, or the other way around. As a matter of fact, a dataset usually needs to be pre-processed. Taking into account all the possible pre-processing operators, there exists a staggeringly large number of alternatives and nonexperienced users become overwhelmed. We show that this problem can be addressed by an automated approach, leveraging ideas from metalearning. Specifically, we consider a wide range of data pre-processing techniques and a set of data mining algorithms. For each data mining algorithm and selected dataset, we are able to predict the transformations that improve the result of the algorithm on the respective dataset. Our approach will help non-expert users to more effectively identify the transformations appropriate to their applications, and hence to achieve improved results.Peer ReviewedPostprint (published version

Nanoscale Architectures for Smart Bio-Interfaces: Advances and Challenges

Volcanology & seismolog