3,523 research outputs found
Recursive quantum repeater networks
Internet-scale quantum repeater networks will be heterogeneous in physical
technology, repeater functionality, and management. The classical control
necessary to use the network will therefore face similar issues as Internet
data transmission. Many scalability and management problems that arose during
the development of the Internet might have been solved in a more uniform
fashion, improving flexibility and reducing redundant engineering effort.
Quantum repeater network development is currently at the stage where we risk
similar duplication when separate systems are combined. We propose a unifying
framework that can be used with all existing repeater designs. We introduce the
notion of a Quantum Recursive Network Architecture, developed from the emerging
classical concept of 'recursive networks', extending recursive mechanisms from
a focus on data forwarding to a more general distributed computing request
framework. Recursion abstracts independent transit networks as single relay
nodes, unifies software layering, and virtualizes the addresses of resources to
improve information hiding and resource management. Our architecture is useful
for building arbitrary distributed states, including fundamental distributed
states such as Bell pairs and GHZ, W, and cluster states.Comment: 14 page
The 1st International Electronic Conference on Algorithms
This book presents 22 of the accepted presentations at the 1st International Electronic Conference on Algorithms which was held completely online from September 27 to October 10, 2021. It contains 16 proceeding papers as well as 6 extended abstracts. The works presented in the book cover a wide range of fields dealing with the development of algorithms. Many of contributions are related to machine learning, in particular deep learning. Another main focus among the contributions is on problems dealing with graphs and networks, e.g., in connection with evacuation planning problems
Machine learning-based routing and wavelength assignment in software-defined optical networks
Recently, machine learning (ML) has attracted the attention of both researchers and practitioners to address several issues in the optical networking field. This trend has been mainly driven by the huge amount of available data (i.e., signal quality indicators, network alarms, etc.) and to the large number of optimization parameters which feature current optical networks (such as, modulation format, lightpath routes, transport wavelength, etc.). In this paper, we leverage the techniques from the ML discipline to efficiently accomplish the routing and wavelength assignment (RWA) for an input traffic matrix in an optical WDM network. Numerical results show that near-optimal RWA can be obtained with our approach, while reducing computational time up to 93% in comparison to a traditional optimization approach based on integer linear programming. Moreover, to further demonstrate the effectiveness of our approach, we deployed the ML classifier into an ONOS-based software defined optical network laboratory testbed, where we evaluate the performance of the overall RWA process in terms of computational time.The authors would like to acknowl-edge the support of the project TEXEO (TEC2016-80339-R), funded by Spanish MINECO and the EU-H2020 Metrohaul project (grant no. 761727)
Optimization Methods in Modern Transportation Systems
One of the greatest challenges in the public transportation network is the optimization of the passengers waiting time, where it is necessary to find a compromise between the satisfaction of the passengers and the requirements of the transport companies. This paper presents a detailed review of the available literature dealing with the problem of passenger transport in order to optimize the passenger waiting time at the station and to meet the requirements of companies (maximize profits or minimize cost). After a detailed discussion, the paper clarifies the most important objectives in solving a timetabling problem: the requirements and satisfaction of passengers, passenger waiting time and capacity of vehicles. At the end, the appropriate algorithms for solving the set of optimization models are presented
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