Data for: "Space-borne quantum memories for global quantum communication"

  • Jasminder Sidhu (Creator)
  • Mustafa Gündoğan (Creator)
  • Luca Mazzarella (California Institute of Technology) (Creator)

Dataset

Description

Global scale quantum communication links will form the backbone of the quantum internet. However, exponential loss in optical fibres precludes any realistic application beyond few hundred kilometres. Quantum repeaters (QR) and space-based systems offer to overcome this limitation. However, the most ambitious land based QR architecture is limited to around 4000 km beyond which meaningful secret generation becomes impossible. On the other hand, majority of the satellite-based quantum communication architectures proposed so far are either limited to the line-of-sight distance of the orbit or they rely on satellites as trusted nodes. Here, we analyse the use of quantum memory (QM)-equipped satellites for quantum communication focussing on global range repeaters and Measurement-Device-Independent (MDI) QKD. We show that a network consisting of satellites equipped with QMs could offer orders of magnitude faster entanglement distribution rates across the whole globe than the existing hybrid, space-ground protocols. We further analyse how entanglement distribution performance depends on memory characteristics, determine benchmarks to assess performance of different tasks, and propose various architectures for light-matter interfaces. Our work provides a practical roadmap to realise unconditionally secure quantum communications over global distances with current technologies.

Video content - Access to this content requires a subscription. You must be a premium user to view this content.

This site includes records provided by Elsevier's Data Monitor product. University of Strathclyde does not control or guarantee the accuracy, relevance, or completeness of the information contained in such records and accepts no responsibility or liability for such information.
Date made available1 Jun 2023
Date of data production2020

Cite this