Partial Teleportation of Entanglement in the Noisy Environment

Partial teleportation of entanglement is to teleport one particle of an entangled pair through a quantum channel. This is conceptually equivalent to quantum swapping. We consider the partial teleportation of entanglement in the noisy environment, employing the Werner-state representation of the noisy channel for the simplicity of calculation. To have the insight of the many-body teleportation, we introduce the measure of correlation information and study the transfer of the correlation information and entanglement. We find that the fidelity gets smaller as the initial-state is entangled more for a given entanglement of the quantum channel. The entangled channel transfers at least some of the entanglement to the final state.Comment: 8 pages, 2 figure

Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets

This paper introduces new algorithms and data structures for quick counting for machine learning datasets. We focus on the counting task of constructing contingency tables, but our approach is also applicable to counting the number of records in a dataset that match conjunctive queries. Subject to certain assumptions, the costs of these operations can be shown to be independent of the number of records in the dataset and loglinear in the number of non-zero entries in the contingency table. We provide a very sparse data structure, the ADtree, to minimize memory use. We provide analytical worst-case bounds for this structure for several models of data distribution. We empirically demonstrate that tractably-sized data structures can be produced for large real-world datasets by (a) using a sparse tree structure that never allocates memory for counts of zero, (b) never allocating memory for counts that can be deduced from other counts, and (c) not bothering to expand the tree fully near its leaves. We show how the ADtree can be used to accelerate Bayes net structure finding algorithms, rule learning algorithms, and feature selection algorithms, and we provide a number of empirical results comparing ADtree methods against traditional direct counting approaches. We also discuss the possible uses of ADtrees in other machine learning methods, and discuss the merits of ADtrees in comparison with alternative representations such as kd-trees, R-trees and Frequent Sets.Comment: See http://www.jair.org/ for any accompanying file

Iterated smoothed bootstrap confidence intervals for population quantiles

This paper investigates the effects of smoothed bootstrap iterations on coverage probabilities of smoothed bootstrap and bootstrap-t confidence intervals for population quantiles, and establishes the optimal kernel bandwidths at various stages of the smoothing procedures. The conventional smoothed bootstrap and bootstrap-t methods have been known to yield one-sided coverage errors of orders O(n^{-1/2}) and o(n^{-2/3}), respectively, for intervals based on the sample quantile of a random sample of size n. We sharpen the latter result to O(n^{-5/6}) with proper choices of bandwidths at the bootstrapping and Studentization steps. We show further that calibration of the nominal coverage level by means of the iterated bootstrap succeeds in reducing the coverage error of the smoothed bootstrap percentile interval to the order O(n^{-2/3}) and that of the smoothed bootstrap-t interval to O(n^{-58/57}), provided that bandwidths are selected of appropriate orders. Simulation results confirm our asymptotic findings, suggesting that the iterated smoothed bootstrap-t method yields the most accurate coverage. On the other hand, the iterated smoothed bootstrap percentile method interval has the advantage of being shorter and more stable than the bootstrap-t intervals.Comment: Published at http://dx.doi.org/10.1214/009053604000000878 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Temperature- and magnetic-field-dependent resistivity of MgB2 sintered at high temperature and high pressure condition

We report the temperature- and magnetic-field-dependent resistivity of MgB2 sintered at high temperature and high pressure condition. The superconducting transition width for the resistivity measurement was about 0.4 K, and the low-field magnetization showed a sharp superconducting transition with a transition width of about 1 K. The resistivity in the normal state roughly followed T^2 behavior with smaller residual resistivity ratio (RRR) of 3 over broad temperature region above 100 K rather than reported T^3 behavior with larger RRR value of ~ 20 in the samples made at lower pressures. Also, the resistivity did not change appreciably with the applied magnetic field, which was different from previous report. These differences were discussed with the microscopic and structural change due to the high-pressure sintering.Comment: 2 pages, 3 figures. Accepted by Physica