Towards an experimentally feasible controlled-phase gate on two blockaded Rydberg atoms

We investigate the implementation of a controlled-Z gate on a pair of Rydberg atoms in spatially separated dipole traps where the joint excitation of both atoms into the Rydberg level is strongly suppressed (the Rydberg blockade). We follow the adiabatic gate scheme of Jaksch et al. [1], where the pair of atoms are coherently excited using lasers, and apply it to the experimental setup outlined in Ga\"etan et al. [2]. We apply optimisation to the experimental parameters to improve gate fidelity, and consider the impact of several experimental constraints on the gate success.Comment: 10 pages, 14 figure

Flux tubes and their interaction in U(1) lattice gauge theory

We investigate singly and doubly charged flux tubes in U(1) lattice gauge theory. By simulating the dually transformed path integral we are able to consider large flux tube lengths, low temperatures, and multiply charged systems without loss of numerical precision. We simulate flux tubes between static sources as well as periodically closed flux tubes, calculating flux tube profiles, the total field energy and the free energy. Our main results are that the string tension in both three and four dimensions scales proportionally to the charge -- which is in contrast to previous lattice results -- and that in four-dimensional U(1) there is an attractive interaction between flux tubes for beta approaching the phase transition.Comment: 19 pages, latex2e with tex- and eps-figures; complete postscript file also available at ftp://is1.kph.tuwien.ac.at/pub/zach/np97.ps.g

Automatic identification of chemical moieties

In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular. Message-passing neural networks (MPNNs) solve this task by constructing atomic representations, from which the properties of interest are predicted. Here, we introduce a method to automatically identify chemical moieties (molecular building blocks) from such representations, enabling a variety of applications beyond property prediction, which otherwise rely on expert knowledge. The required representation can either be provided by a pretrained MPNN, or be learned from scratch using only structural information. Beyond the data-driven design of molecular fingerprints, the versatility of our approach is demonstrated by enabling the selection of representative entries in chemical databases, the automatic construction of coarse-grained force fields, as well as the identification of reaction coordinates

Towards many colors in FISH on 3D-preserved interphase nuclei

The article reviews the existing methods of multicolor FISH on nuclear targets, first of all, interphase chromosomes. FISH proper and image acquisition are considered as two related components of a single process. We discuss (1) M-FISH (combinatorial labeling + deconvolution + widefield microscopy); (2) multicolor labeling + SIM (structured illumination microscopy); (3) the standard approach to multicolor FISH + CLSM (confocal laser scanning microscopy; one fluorochrome - one color channel); (4) combinatorial labeling + CLSM; (5) non-combinatorial labeling + CLSM + linear unmixing. Two related issues, deconvolution of images acquired with CLSM and correction of data for chromatic Z-shift, are also discussed. All methods are illustrated with practical examples. Finally, several rules of thumb helping to choose an optimal labeling + microscopy combination for the planned experiment are suggested. Copyright (c) 2006 S. Karger AG, Basel

Quantum-Chemical Insights from Interpretable Atomistic Neural Networks

With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers. Here, we describe interpretation techniques for atomistic neural networks on the example of Behler–Parrinello networks as well as the end-to-end model SchNet. Both models obtain predictions of chemical properties by aggregating atom-wise contributions. These latent variables can serve as local explanations of a prediction and are obtained during training without additional cost. Due to their correspondence to well-known chemical concepts such as atomic energies and partial charges, these atom-wise explanations enable insights not only about the model but more importantly about the underlying quantum-chemical regularities. We generalize from atomistic explanations to 3d space, thus obtaining spatially resolved visualizations which further improve interpretability. Finally, we analyze learned embeddings of chemical elements that exhibit a partial ordering that resembles the order of the periodic table. As the examined neural networks show excellent agreement with chemical knowledge, the presented techniques open up new venues for data-driven research in chemistry, physics and materials science.EC/H2020/792572/EU/Machine Learning for Catalytic Carbon Dioxide Activation/MachineCatBMBF, 01IS14013A, Verbundprojekt: BBDC - Berliner Kompetenzzentrum für Big DataBMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and DataEC/H2020/725291/EU/Beyond Static Molecules: Modeling Quantum Fluctuations in Complex Molecular Environments/BeStM

Some Cautionary Remarks on Abelian Projection and Abelian Dominance

Some critical remarks are presented, concerning the abelian projection theory of quark confinement.Comment: Talk presented at LATTICE96(topology) plenary session, uses psfig and espcrc2 package

Thermodynamics of ideal quantum gas with fractional statistics in D dimensions

We present exact and explicit results for the thermodynamic properties (isochores, isotherms, isobars, response functions, velocity of sound) of a quantum gas in dimensions D>=1 and with fractional exclusion statistics 0<=g<=1 connecting bosons (g=0) and fermions (g=1). In D=1 the results are equivalent to those of the Calogero-Sutherland model. Emphasis is given to the crossover between boson-like and fermion-like features, caused by aspects of the statistical interaction that mimic long-range attraction and short-range repulsion. The full isochoric heat capacity and the leading low-T term of the isobaric expansivity in D=2 are independent of g. The onset of Bose-Einstein condensation along the isobar occurs at a nonzero transition temperature in all dimensions. The T-dependence of the velocity of sound is in simple relation to isochores and isobars. The effects of soft container walls are accounted for rigorously for the case of a pure power-law potential.Comment: 15 pages, 31 figure

The influence of matrix viscosity on MWCNT dispersion and electrical properties in different thermoplastic nanocomposites

Composites of MWCNTs having each three different levels of matrix viscosity with five different polymers (polyamide 12, polybutylene terephthalate, polycarbonate, polyetheretherketone and low density polyethylene) were melt mixed to identify the general influence of matrix viscosity on the electrical properties and the state of MWCNT dispersion. Huge differences in the electrical percolation thresholds were found using the same polymer matrix with different viscosity grades. The lowest percolation thresholds were always found in the composites based on the low viscosity matrix. The state of primary MWCNT agglomerate dispersion increased with increasing matrix viscosity due to the higher input of mixing energy. TEM investigations showed nanoagglomerated structures in the low viscosity samples which are obviously needed to achieve low resistivity values. The effect of nanotube shortening was quantified using two different viscosity grades of polycarbonate. Due to the higher mixing energy input the nanotube shortening was more pronounced in the high viscosity matrix which partially explains the higher percolation threshold. © 2011 Elsevier Ltd. All rights reserved