Protecting dissipative quantum state preparation via dynamical decoupling

We show that dissipative quantum state preparation processes can be protected against qubit dephasing by interlacing the state preparation control with dynamical decoupling (DD) control consisting of a sequence of short $\pi$-pulses. The inhomogeneous broadening can be suppressed to second order of the pulse interval, and the protection efficiency is nearly independent of the pulse sequence but determined by the average interval between pulses. The DD protection is numerically tested and found to be efficient against inhomogeneous dephasing on two exemplary dissipative state preparation schemes that use collective pumping to realize many-body singlets and linear cluster states respectively. Numerical simulation also shows that the state preparation can be efficiently protected by $\pi$-pulses with completely random arrival time. Our results make possible the application of these state preparation schemes in inhomogeneously broadened systems. DD protection of state preparation against dynamical noises is also discussed using the example of Gaussian noise with a semiclasscial description.Comment: 9 pages, 8 figure

The coastal-inland income gap in China from 1991 to 1999: the role of geography and policy

We investigate the enlarging coastal-inland income gap in China during the 1990s, using GMM estimation of a Solow growth model. Disaggregating capital investment by source: public, foreign and private: helps to disentangle the effect of policy from those of geography. The impact of public investment on growth is insignificant in our panel data for 29 provinces; that of foreign investment is significant; private investment is most influential. We also use the distance by railway of each province’s capital city to its nearest port city as a proxy for transportation costs, and find significant differences across regions. Distance has negative effects on economic development but its marginal impact effects become less as distance increases. The coastal-inland gap will grow in the foreseeable future, if inland areas are not able to benefit from an increase in private investment and infrastructure improvements (to reduce transport costs).

Learning a Mixture of Deep Networks for Single Image Super-Resolution

Single image super-resolution (SR) is an ill-posed problem which aims to recover high-resolution (HR) images from their low-resolution (LR) observations. The crux of this problem lies in learning the complex mapping between low-resolution patches and the corresponding high-resolution patches. Prior arts have used either a mixture of simple regression models or a single non-linear neural network for this propose. This paper proposes the method of learning a mixture of SR inference modules in a unified framework to tackle this problem. Specifically, a number of SR inference modules specialized in different image local patterns are first independently applied on the LR image to obtain various HR estimates, and the resultant HR estimates are adaptively aggregated to form the final HR image. By selecting neural networks as the SR inference module, the whole procedure can be incorporated into a unified network and be optimized jointly. Extensive experiments are conducted to investigate the relation between restoration performance and different network architectures. Compared with other current image SR approaches, our proposed method achieves state-of-the-arts restoration results on a wide range of images consistently while allowing more flexible design choices. The source codes are available in http://www.ifp.illinois.edu/~dingliu2/accv2016