Abstract

Quantum network is a promising platform for many ground-breaking applications that lie beyond the capability of its classical counterparts. Efficient entanglement generation on quantum networks with relatively limited resources such as quantum memories is essential to fully realize the network’s capabilities, the solution to which calls for delicate network design and is currently at the primitive stage. In this study we propose an effective routing scheme to enable automatic responses for multiple requests of entanglement generation between source-terminal stations on a quantum lattice network with finite edge capacities. Multiple connection paths are exploited for each connection request while entanglement fidelity is ensured for each path by performing entanglement purification. The routing scheme is highly modularized with a flexible nature, embedding quantum operations within the algorithmic workflow, whose performance is evaluated from multiple perspectives. In particular, three algorithms are proposed and compared for the scheduling of capacity allocation on the edges of quantum network. Embodying the ideas of proportional share and progressive filling that have been well-studied in classical routing problems, we design another scheduling algorithm, the propagatory update method, which in certain aspects overrides the two algorithms based on classical heuristics in scheduling performances. The general solution scheme paves the road for effective design of efficient routing and flow control protocols on applicational quantum networks.

Details

Title
Effective routing design for remote entanglement generation on quantum networks
Author
Li Changhao 1   VIAFID ORCID Logo  ; Li, Tianyi 2   VIAFID ORCID Logo  ; Yi-Xiang, Liu 1   VIAFID ORCID Logo  ; Cappellaro Paola 1   VIAFID ORCID Logo 

 Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) 
 System Dynamics Group, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) 
Publication year
2021
Publication date
2021
Publisher
Nature Publishing Group
e-ISSN
20566387
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2480996330
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.