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.
