Entangling single atoms over 33 km telecom fibre

Heralded entanglement between distant quantum memories is the key resource for quantum networks. Based on quantum repeater protocols, these networks will facilitate efficient large-scale quantum communication and distributed quantum computing. However, despite vast efforts, long-distance fibre based network links have not been realized yet. Here we present results demonstrating heralded entanglement between two independent, remote single-atom quantum memories generated over fibre links with a total length up to 33 km. To overcome the attenuation losses in the long optical fibres of photons initially emitted by the Rubidium quantum memories, we employ polarization-preserving quantum frequency conversion to the low loss telecom band. The presented work represents a milestone towards the realization of efficient quantum network links.

Layer-Assisted Neural Topic Modeling over Document Networks

Neural topic modeling provides a flexible, efficient, and powerful way to extract topic representations from text documents. Unfortunately, most existing models cannot handle the text data with network links, such as web pages with hyperlinks and scientific papers with citations. To resolve this kind of data, we develop a novel neural topic model , namely Layer-Assisted Neural Topic Model (LANTM), which can be interpreted from the perspective of variational auto-encoders. Our major motivation is to enhance the topic representation encoding by not only using text contents, but also the assisted network links. Specifically, LANTM encodes the texts and network links to the topic representations by an augmented network with graph convolutional modules, and decodes them by maximizing the likelihood of the generative process. The neural variational inference is adopted for efficient inference. Experimental results validate that LANTM significantly outperforms the existing models on topic quality, text classification and link prediction..

Load Balancing Oriented Predictive Routing Algorithm for Data Center Networks

A data center undertakes increasing background services of various applications, and the data flows transmitted between the nodes in data center networks (DCNs) are consequently increased. At the same time, the traffic of each link in a DCN changes dynamically over time. Flow scheduling algorithms can improve the distribution of data flows among the network links so as to improve the balance of link loads in a DCN. However, most current load balancing works achieve flow scheduling decisions to the current links on the basis of past link flow conditions. This situation impedes the existing link scheduling methods from implementing optimal decisions for scheduling data flows among the network links in a DCN. This paper proposes a predictive link load balance routing algorithm for a DCN based on residual networks (ResNet), i.e., the link load balance route (LLBR) algorithm. The LLBR algorithm predicts the occupancy of the network links in the next duty cycle, according to the ResNet architecture, and then the optimal traffic route is selected according to the predictive network environment. The LLBR algorithm, round-robin scheduling (RRS), and weighted round-robin scheduling (WRRS) are used in the same experimental environment. Experimental results show that compared with the WRRS and RRS, the LLBR algorithm can reduce the transmission time by approximately 50%, reduce the packet loss rate from 0.05% to 0.02%, and improve the bandwidth utilization by 30%.

Improved Model of Load Balancing in the Infocommunication Network

The paper proposes an improved mathematical model of load balancing in the infocommunication network (ICN), corresponding to the Traffic Engineering (TE) concept principles. The model mathematically formalizes the case of ICN construction when each access network is switched simultaneously to not one but to several border routers to increase fault tolerance. Therefore, it is proposed to improve the load balancing level in the ICN according to the TE criterion by ensuring the distribution of traffic at the access level between several border routers that create a default virtual gateway. The proposed mathematical model is based on the conditions of implementation of single or multipath routing; load balancing at the access level; flow conservation at the access level and the network itself; overload prevention of communication links, which act as conditions for load balancing in ICN. Within the proposed model, the load balancing task in ICN is formulated as an optimization problem of mixed-integer linear programming. The results of the study confirmed the effectiveness of the proposed solution. Ensuring coordinated load balancing at both access and core network levels, in general, has increased network performance by 25.45% compared to a solution based on multipath routing, but without access level balancing, and 2.76 times compared to the model in which load balancing in the ICN was absent. Within the available load for each of the compared models, the use of the proposed solution allowed to reduce the upper bound of the network links utilization by an average from 20% to 60%. Lowering the upper bound of the network links utilization positively affects the quantitative values of the main Quality of Service indicators – the average end-to-end delay, jitter, and packet loss probability.

Non-Conservative Distributed Control over Real-Time Network Links

No data is associated with this manuscript.