Learning-based Calibration of Flux Crosstalk in Transmon Qubit Arrays

ORAL

Abstract

Superconducting quantum processors comprising flux-tunable data and coupler qubits are a promising platform for analog quantum simulation and digital quantum computation. One challenge to scaling this platform is the magnetic flux crosstalk between flux-control lines and qubits, which impedes precision control of qubit frequencies. To implement high-fidelity quantum operations as processor sizes increase, we need an extensible approach to measure flux crosstalk and compensate for it. We demonstrate the experimental performance of a learning-based approach to DC-flux and fast-flux crosstalk calibration on an array of 16 flux-tunable transmon qubits. The overall calibration time for this approach empirically scales linearly with system size, while achieving a median qubit frequency error below 300 kHz.

* This work is supported in part by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Quantum System Accelerator (QSA); in part by the National Science Foundation under grants PHY-1720311 and 1839197; and by the U.S. Department of Energy under Air Force Contract No. FA8702-15-D-0001. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government.

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Publication: Barrett, C. N., Karamlou, A. H., et al. (2023). Learning-based calibration of flux crosstalk in transmon qubit arrays. Physical Review Applied, 20(2), 024070.

Presenters

  • Cora N Barrett

    Wellesley College, Massachusetts Institute of Technology

Authors

  • Cora N Barrett

    Wellesley College, Massachusetts Institute of Technology

  • Amir H Karamlou

    Massachusetts Institute of Technology MI

  • Sarah Muschinske

    Massachusetts Institute of Technology MI, Massachusetts Institute of Technology

  • Ilan T Rosen

    Massachusetts Institute of Technology

  • Jochen Braumuller

    Massachusetts Institute of Technology

  • Rabindra Das

    Massachusetts Institute of Technology MIT, MIT Lincoln Laboratory

  • David K Kim

    MIT Lincoln Lab, MIT Lincoln Laboratory

  • Bethany M Niedzielski

    MIT Lincoln Lab, MIT Lincoln Laboratory

  • Megan Schuldt

    MIT Lincoln Laboratory

  • Kyle Serniak

    MIT Lincoln Laboratory & MIT RLE, MIT Lincoln Laboratory, MIT Lincoln Laboratory, MIT RLE

  • Mollie E Schwartz

    MIT Lincoln Laboratory

  • Jonilyn L Yoder

    MIT Lincoln Lab, MIT Lincoln Laboratory

  • Terry P Orlando

    Massachusetts Institute of Technology MIT, Massachusetts Institute of Technology

  • Simon Gustavsson

    Massachusetts Institute of Technology MIT, Massachusetts Institute of Technology

  • Jeffrey A Grover

    Massachusetts Institute of Technology, Massachusetts Institute of Technology (MIT), Massachusetts Institute of Technology MIT

  • William D Oliver

    Massachusetts Institute of Technology MI, Massachusetts Institute of Technology, Massachusetts Institute of Technology (MIT), Massachusetts Institute of Technology MIT