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Tomography of star-shaped quantum networks with Pauli channels

ORAL

Abstract

Quantum networks are systems formed by the interconnection of quantum processors with quantum channels that allow for the exchange of quantum information. Since noise and decoherence are intrinsic in quantum information systems, understanding their impacts on quantum communication is critical to guide the development of efficient networking protocols. For instance, quantum error correction processes can make use of error descriptions, which are manifestations of noise, to improve decoder efficiency. In this setting, Quantum Network Tomography is a field of network management that targets the estimation of parameters characterizing errors in network channels, under the assumption that most of the network nodes cannot perform quantum measurements for estimation. To solve this problem, selected nodes in the network, referred to as monitors, must use the network to exchange quantum states among themselves, perform measurements on such states and use results to infer channel parameters. In this talk, I will cover initial developments in the network tomography of quantum star networks, formed by a single quantum router interconnecting multiple end-nodes, with Pauli channels, assuming that leaves are monitors and that the router is not, thus being forbidden to perform measurements for estimation . Stars are of interest since it is likely that initial quantum network implementations will be star-shaped and methods for stars serve as basis for tomography on more complex topologies.

Publication: "Quantum Network Tomography with Multi-party State Distribution." 2022 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2022

Presenters

  • Matheus Guedes de Andrade

    University of Massachusetts Amherst

Authors

  • Matheus Guedes de Andrade

    University of Massachusetts Amherst

  • Jaime A Diaz

    Northern Arizona University

  • Jake Navas

    Northern Arizona University

  • Saikat Guha

    University of Arizona

  • Inès Montaño

    Northern Arizona U.

  • Brian J Smith

    University of Oregon

  • Michael G Raymer

    University of Oregon

  • Don Towsley

    University of Massachusetts Amherst