Simple unconventional geometric scenario of one-way quantum computation with superconducting qubits inside a cavity

We propose a simple unconventional geometric scenario to achieve a kind of nontrivial multi-qubit operations with superconducting charge qubits placed in a microwave cavity. The proposed quantum operations are insensitive not only to the thermal state of cavity mode but also to certain random operation errors, and thus may lead to high-fidelity quantum information processing. Executing the designated quantum operations, a class of highly entangled cluster states may be generated efficiently in the present scalable solid-state system, enabling one to achieve one-way quantum computation.Comment: Accepted version with minor amendments. To appear in Phys. Rev.

Universal holonomic quantum gates in decoherence-free subspace on superconducting circuits

To implement a set of universal quantum logic gates based on non-Abelian geometric phases, it is a conventional wisdom that quantum systems beyond two levels are required, which is extremely difficult to fulfil for superconducting qubits, appearing to be a main reason why only single qubit gates was implemented in a recent experiment [A. A. Abdumalikov Jr \emph{et al}., Nature 496, 482 (2013)]. Here we propose to realize non-adiabatic holonomic quantum computation in decoherence-free subspace on circuit QED, where one can use only the two levels in transmon qubits, a usual interaction, and a minimal resource for the decoherence-free subspace encoding. In particular, our scheme not only overcomes the difficulties encountered in previous studies, but also can still achieve considerably large effective coupling strength, such that high fidelity quantum gates can be achieved. Therefore, the present scheme makes it very promising way to realize robust holonomic quantum computation with superconducting circuits.Comment: V4: published version; V1: submitted on April

Nitrate sources and dynamics in a salinized river and estuary : a δ15N-NO₃⁻ and δ18O-NO₃⁻ isotope approach

To trace NO3- sources and assess NO3- dynamics in salinized rivers and estuaries, three rivers (Haihe River: HH River, Chaobaixin River: CB River and Jiyun River: JY River) and two estuaries (HH Estuary and CJ Estuary) along the Bohai Bay (China) have been selected to determine dissolved inorganic nitrogen (DIN: NH4+, NO2- and NO3-. Upstream of the HH River, NO3- was removed 30.9 +/- 22.1% by denitrification, resulting from effects of the floodgate: limiting water exchange with downstream and prolonging water residence time to remove NO3-. Downstream of the HH River NO3- was removed 2.5 +/- 13.3% by NO3- turnover processes. Conversely, NO3- was increased 36.6 +/- 25.2% by external N source addition in the CB River and 34.6 +/- 35.1% by instream nitrification in the JY River. The HH and CY Estuaries behaved mostly conservatively excluding the sewage input in the CJ Estuary. Hydrodynamics in estuaries has been changed by the ongoing reclamation projects, aggravating the loss of the attenuation function of NO3- in the estuary

Effects of tidally enhanced stellar wind on the horizontal branch morphology of globular clusters

Metallicity is the first parameter to influence the horizontal branch (HB) morphology of globular clusters (GCs). It has been found, however, that some other parameters may also play an important role in affecting the morphology. While the nature of these important parameters remains unclear, they are believed to be likely correlated with wind mass-loss of red giants, since this mass loss determines their subsequent locations on the HB. Unfortunately, the mass loss during the red giant stages of the stellar evolution is poorly understood at present. The stellar winds of red giants may be tidally enhanced by companion stars if they are in binary systems. We investigate evolutionary consequences of red giants in binaries by including tidally enhanced stellar winds, and examine the effects on the HB morphology of GCs. We find that red, blue, and extreme horizontal branch stars are all produced under the effects of tidally enhanced stellar wind without any additional assumptions on the mass-loss dispersion. Furthermore, the horizontal branch morphology is found to be insensitive to the tidal enhancement parameter, Bw. We compare our theoretical results with the observed horizontal branch morphology of globular cluster NGC 2808, and find that the basic morphology of the horizontal branch can be well reproduced. The number of blue horizontal branch stars in our calculations, however, is lower than that of NGC 2808.Comment: 7 pages, 4 figures, 2 tables, accepted for publication in Astronomy & Astrophysic

The influences of the galactic cosmic ray on the atmospheric ozone

The relationship between the yearly variations of cosmic ray intensity and ozone in the atmosphere, and the ozone disturbance initiated by the Forbush decrease of 1965-1976 is analyzed. The data on cosmic ray intensity were selected from the records of the super neutron monitor at Deep River station and the ionization chamber at Beijing station. Ozone data were taken from Resolute (Canada), Bismark (N. Dakota, USA), Kagoshima (Japan), and Kodaikanal (India). The statistical results show that ozone is prominently modulated and disturbed by the 11 year variation and the Forbush decrease in the galactic cosmic ray

Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning

Cardiovascular disease accounts for 1 in every 4 deaths in United States. Accurate estimation of structural and functional cardiac parameters is crucial for both diagnosis and disease management. In this work, we develop an ensemble learning framework for more accurate and robust left ventricle (LV) quantification. The framework combines two 1st-level modules: direct estimation module and a segmentation module. The direct estimation module utilizes Convolutional Neural Network (CNN) to achieve end-to-end quantification. The CNN is trained by taking 2D cardiac images as input and cardiac parameters as output. The segmentation module utilizes a U-Net architecture for obtaining pixel-wise prediction of the epicardium and endocardium of LV from the background. The binary U-Net output is then analyzed by a separate CNN for estimating the cardiac parameters. We then employ linear regression between the 1st-level predictor and ground truth to learn a 2nd-level predictor that ensembles the results from 1st-level modules for the final estimation. Preliminary results by testing the proposed framework on the LVQuan18 dataset show superior performance of the ensemble learning model over the two base modules.Comment: Jiasha Liu, Xiang Li and Hui Ren contribute equally to this wor