Optimizing quantum error correction experiments with flux-tunable transmonsPart 2: higher accuracy error decoding

ORAL

Abstract

In this second part, we present results from running small surface code error correction experiments on a 17-qubit flux-tunable transmon, with fixed-frequency couplers, quantum computer. We compare the performance of different decoding algorithms, including a neural-network-based one. We further demonstrate improvement in the logical fidelity by making use of additional analogue information obtained during readout, widely called soft information. Our results lead to a greater understanding of the performance of the superconducting device in surface code error correction contexts.

*Research funded by IARPA (U.S. A.R.O. Grant No. W911NF-16-1-0071), and the European Flagship (OpenSuperQPlus).

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Presenters

  • Ophelia Crawford

    • Riverlane, Cambridge, UK
    • Riverlane

Authors

  • Ophelia Crawford

    • Riverlane, Cambridge, UK
    • Riverlane

  • Joonas Majaniemi

    • Riverlane

  • Marc Serra-Peralta

    • Delft University of Technology

  • Jorge F Marques

    • QuTech and Kavli Institute of Nanoscience, Delft University of Technology
    • Delft University of Technology
    • QuTech and Kavli Institute of Nanoscience, Delft University of Technology, The Netherlands

  • Hany Ali

    • Delft University of Technology
    • QuTech and Kavli Institute of Nanoscience, Delft University of Technology, The Netherlands

  • David Byfield

    • Riverlane

  • Boris M Varbanov

    • Delft University of Technology

  • Leonardo DiCarlo

    • QuTech and Kavli Institute of Nanoscience, Delft University of Technology
    • Delft University of Technology

  • Barbara M Terhal

    • Delft University of Technology

  • Earl T Campbell

    • Riverlane