The quantum Internet’s significance in information technology is increasingly recognized for its potential to enable secure and private communication. The quantum Internet facilitates the transmission of quantum information over extensive distances, thus unlocking novel possibilities in various applications. In this study we explore the quantum communication capacity within general quantum entangled networks. Leveraging von Neumann entropy, we show the capability of state transfer and information concentration across four-party networks, thereby characterizing the bipartite quantum communication capacity with the support of local operations and classical communications from other parties. We introduce unidirectional and bidirectional models, employing chain or star transferring network configurations, and extend these frameworks to encompass general quantum networks. Furthermore, we provide illustrative examples to underscore the implications of our findings. This research yields the initial nontrivial outcome concerning quantum communication capacity across general quantum networks, shedding light on the potential of quantum networks for advanced communication protocols.

Most of the previous neural machine translations (NMT) rely on parallel corpus. Integrating explicitly prior syntactic structure information can improve the neural machine translation. In this article, we propose a Syntax Induced Self-Attention (SISA) which explores the influence of dependence relation between words through the attention mechanism and fine-tunes the attention allocation of the sentence through the obtained dependency weight. We present a new model, Double Syntax Induced Self-Attention (DSISA), which fuses the features extracted by SISA and a compact convolution neural network (CNN). SISA can alleviate long dependency in sentence, while CNN captures the limited context based on neighbors. DSISA utilizes two different neural networks to extract different features for richer semantic representation and replaces the first layer of Transformer encoder. DSISA not only makes use of the global feature of tokens in sentences but also the local feature formed with adjacent tokens. Finally, we perform simulation experiments that verify the performance of the new model on standard corpora.