Entangling a series of trapped ions by moving cavity bus

Entangling multiple qubits is one of the central tasks for quantum information processings. Here, we propose an approach to entangle a number of cold ions (individually trapped in a string of microtraps) by a moved cavity. The cavity is pushed to include the ions one by one with an uniform velocity, and thus the information stored in former ions could be transferred to the latter ones by such a moving cavity bus. Since the positions of the trapped ions are precisely located, the strengths and durations of the ion-cavity interactions can be exactly controlled. As a consequence, by properly setting the relevant parameters typical multi-ion entangled states, e.g., $W$ state for 10 ions, could be deterministically generated. The feasibility of the proposal is also discussed.Comment: 8 pages, 2 figures, 1 tabl

Automatic Recognition of Knowledge Characteristics of Scientific and Technological Literature from the Perspective of Text Structure

This paper independently explores the chapter structure of scientific and technological literature in the field of shipbuilding in the natural sciences and the field of library and information in the social sciences. The chapter structure model of previous studies, namely \u27background, purpose, method, result, conclusion, demonstration,\u27 is quoted as the verification object of the document chapter structure in the field of exploration. In order to verify the rationality of the structure, this paper uses the deep learning models TextCNN, DPCNN, TextRCNN, and BiLSTM-Attention as experimental tools, and designs 5-fold cross-validation experiment and normal experiment, and finally verifies the rationality of the model structure, and It is concluded that the BiLSTM-Attention model can better identify the chapter structure in this field

Model-based reinforcement learning: A survey

Reinforcement learning is an important branch of machine learning and artificial intelligence. Compared with traditional reinforcement learning, model-based reinforcement learning obtains the action of the next state by the model that has been learned, and then optimizes the policy, which greatly improves data efficiency. Based on the present status of research on model-based reinforcement learning at home and abroad, this paper comprehensively reviews the key techniques of model-based reinforcement learning, summarizes the characteristics, advantages and defects of each technology, and analyzes the application of model-based reinforcement learning in games, robotics and brain science

The ρ\rho-meson longitudinal leading-twist distribution amplitude

In the present paper, we suggest a convenient model for the vector $\rho$-meson longitudinal leading-twist distribution amplitude $\phi_{2;\rho}^\|$, whose distribution is controlled by a single parameter $B^\|_{2;\rho}$. By choosing proper chiral current in the correlator, we obtain new light-cone sum rules (LCSR) for the $B\to\rho$ TFFs $A_1$, $A_2$ and $V$, in which the $\delta^1$-order $\phi_{2;\rho}^\|$ provides dominant contributions. Then we make a detailed discussion on the $\phi_{2;\rho}^\|$ properties via those $B\to\rho$ TFFs. A proper choice of $B^\|_{2;\rho}$ can make all the TFFs agree with the lattice QCD predictions. A prediction of $|V_{\rm ub}|$ has also been presented by using the extrapolated TFFs, which indicates that a larger $B^{\|}_{2;\rho}$ leads to a larger $|V_{\rm ub}|$. To compare with the BABAR data on $|V_{\rm ub}|$, the longitudinal leading-twist DA $\phi_{2;\rho}^\|$ prefers a doubly-humped behavior.Comment: 7 pages, 3 figures. Discussions improved and references updated. To be published in Phys.Lett.

Regulation of CCL5 Expression in Smooth Muscle Cells Following Arterial Injury

Chemokines play a crucial role in inflammation and in the pathophysiology of atherosclerosis by recruiting inflammatory immune cells to the endothelium. Chemokine CCL5 has been shown to be involved in atherosclerosis progression. However, little is known about how CCL5 is regulated in vascular smooth muscle cells. In this study we report that CCL5 mRNA expression was induced and peaked in aorta at day 7 and then declined after balloon artery injury, whereas IP-10 and MCP-1 mRNA expression were induced and peaked at day 3 and then rapidly declined