Deterministic entanglement distribution on series-parallel quantum networks

Xiangyi Meng, Yulong Cui, Jianxi Gao, Shlomo Havlin, Andrei E. Ruckenstein

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The performance of distributing entanglement between two distant nodes in a large-scale quantum network (QN) of partially entangled bipartite pure states is generally benchmarked against the classical entanglement percolation (CEP) scheme. Improvements beyond CEP were only achieved by nonscalable strategies for restricted QN topologies. This paper explores and amplifies a new and more effective mapping of a QN, referred to as concurrence percolation theory (ConPT), that suggests using deterministic rather than probabilistic protocols for scalably improving on CEP across arbitrary QN topology. More precisely, we implement ConPT via a deterministic entanglement transmission (DET) scheme that is fully analogous to resistor network analysis, with the corresponding series and parallel rules represented by deterministic entanglement swapping and concentration protocols, respectively. The main contribution of this paper is to establish a powerful mathematical framework, which is applicable to arbitrary d-dimensional information carriers (qudits), that provides different natural optimality metrics in terms of generalized k-concurrences (a family of fundamental entanglement measures) for different QN topologies. In particular, we conclude that the introduced DET scheme (a) is optimal over the well-known nested repeater protocol for distilling entanglement from partially entangled qubits and (b) leads to higher success probabilities of obtaining a maximally entangled state than using CEP. The implementation of the DET scheme is experimentally feasible as tested on IBM's quantum computation platform.

Original languageEnglish
Article number013225
Number of pages16
JournalPhysical Review Research
Volume5
Issue number1
DOIs
StatePublished - 31 Mar 2023

Bibliographical note

Publisher Copyright:
© 2023 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Funding

X.M. was supported by the NetSeed: Seedling Research Award of the Network Science Institute at Northeastern University. J.G. acknowledges the support of National Science Foundation under Grant No. 2047488. A.E.R. was partially supported by a grant from the Innovation Institue of the Massachusetts Technology Collaborative.

FundersFunder number
Innovation Institue of the Massachusetts Technology Collaborative
Network Science Institute at Northeastern University
National Science Foundation2047488

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