Conditional phase gate and quantum state transfer via off-resonant quantum Zeno dynamics

We propose a scheme to realize the conditional phase gate (CPG) and quantum state transfer (QST) between two qubits (acted by nitrogen-vacancy (NV) centers) based on off-resonant quantum Zeno dynamics. We also consider the entanglement dynamics of two qubits in this system. Since no cavity photons or excited levels of the NV center is populated during the whole process, the scheme is immune to the decay of cavity and spontaneous emission of the NV center. The strictly numerical simulation shows that the fidelities of QST and CPG are high even in the presence of realistic imperfections.Comment: 18 pages, 8 figures, Optics Communications 201

Arbitrary Control of Entanglement between two nitrogen-vacancy center ensembles coupling to superconducting circuit qubit

We propose an effective scheme for realizing a Jaynes-Cummings (J-C) model with the collective nitrogen-vacancy center ensembles (NVE) bosonic modes in a hybrid system. Specifically, the controllable transmon qubit can alternatively interact with one of the two NVEs, which results in the production of $N$ particle entangled states. Arbitrary $N$ particle entangled states, NOON states, N-dimensional entangled states and entangled coherent states are demonstrated. Realistic imperfections and decoherence effects are analyzed via numerical simulation. Since no cavity photons or excited levels of the NV center are populated during the whole process, our scheme is insensitive to cavity decay and spontaneous emission of the NVE. The idea provides a scalable way to realize NVEs-circuit cavity quantum information processing with current technology.Comment: 13 pages, 7 figure

Structure fusion based on graph convolutional networks for semi-supervised classification

Suffering from the multi-view data diversity and complexity for semi-supervised classification, most of existing graph convolutional networks focus on the networks architecture construction or the salient graph structure preservation, and ignore the the complete graph structure for semi-supervised classification contribution. To mine the more complete distribution structure from multi-view data with the consideration of the specificity and the commonality, we propose structure fusion based on graph convolutional networks (SF-GCN) for improving the performance of semi-supervised classification. SF-GCN can not only retain the special characteristic of each view data by spectral embedding, but also capture the common style of multi-view data by distance metric between multi-graph structures. Suppose the linear relationship between multi-graph structures, we can construct the optimization function of structure fusion model by balancing the specificity loss and the commonality loss. By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as adjacent matrix to input graph convolutional networks for semi-supervised classification. Experiments demonstrate that the performance of SF-GCN outperforms that of the state of the arts on three challenging datasets, which are Cora,Citeseer and Pubmed in citation networks

The Rise in House Prices in China: Bubbles or Fundamentals?

The dramatic rise of house prices in many cities of China has brought huge attention from both the governmental and academic circles. There is a huge debate on whether the increasing house prices are driven by market fundamentals or just by speculation. Like Levin and Wright (1997a, 1997b), we decompose house prices in China into fundamental and non-fundamental components. We also consider potential nonlinear feedback from the historical growth rate of house prices on the current house prices and propose a semiparametric approach to estimate the speculative components in the model. We demonstrate that the non-fundamental part contributes a relatively small proportion of the rise of house prices in China.