Scalable solid-state quantum computation in decoherence-free subspaces with trapped ions

We propose a decoherence-free subspaces (DFS) scheme to realize scalable quantum computation with trapped ions. The spin-dependent Coulomb interaction is exploited, and the universal set of unconventional geometric quantum gates is achieved in encoded subspaces that are immune from decoherence by collective dephasing. The scalability of the scheme for the ion array system is demonstrated, either by an adiabatic way of switching on and off the interactions, or by a fast gate scheme with comprehensive DFS encoding and noise decoupling techniques.Comment: 4 pages, 1 figur

Reaction Mechanism Reduction for Ozone-Enhanced CH4/Air Combustion by a Combination of Directed Relation Graph with Error Propagation, Sensitivity Analysis and Quasi-Steady State Assumption

In this study, an 18-steps, 22-species reduced global mechanism for ozone-enhanced CH4/air combustion processes was derived by coupling GRI-Mech 3.0 and a sub-mechanism for ozone decomposition. Three methods, namely, direct relation graphics with error propagation, (DRGRP), sensitivity analysis (SA), and quasi-steady-state assumption (QSSA), were used to downsize the detailed mechanism to the global mechanism. The verification of the accuracy of the skeletal mechanism in predicting the laminar flame speeds and distribution of the critical components showed that that the major species and the laminar flame speeds are well predicted by the skeletal mechanism. However, the pollutant NO was predicated inaccurately due to the precursors for generating NO were removed as redundant components. The laminar flame speeds calculated by the global mechanism fit the experimental data well. The comparisons of simulated results between the detailed mechanism and global mechanism were investigated and showed that the global mechanism could accurately predict the major and intermediate species and significantly reduced the time cost by 72%Peer reviewe

Dictionary Representation of Deep Features for Occlusion-Robust Face Recognition

Deep learning has achieved exciting results in face recognition; however, the accuracy is still unsatisfying for occluded faces. To improve the robustness for occluded faces, this paper proposes a novel deep dictionary representation-based classification scheme, where a convolutional neural network is employed as the feature extractor and followed by a dictionary to linearly code the extracted deep features. The dictionary is composed by a gallery part consisting of the deep features of the training samples and an auxiliary part consisting of the mapping vectors acquired from the subjects either inside or outside the training set and associated with the occlusion patterns of the testing face samples. A squared Euclidean norm is used to regularize the coding coefficients. The proposed scheme is computationally efficient and is robust to large contiguous occlusion. In addition, the proposed scheme is generic for both the occluded and non-occluded face images and works with a single training sample per subject. The extensive experimental evaluations demonstrate the superior performance of the proposed approach over other state-of-the-art algorithms