2 research outputs found
Learning-based Calibration of Flux Crosstalk in Transmon Qubit Arrays
Superconducting quantum processors comprising flux-tunable data and coupler
qubits are a promising platform for quantum computation. However, magnetic flux
crosstalk between the flux-control lines and the constituent qubits impedes
precision control of qubit frequencies, presenting a challenge to scaling this
platform. In order to implement high-fidelity digital and analog quantum
operations, one must characterize the flux crosstalk and compensate for it. In
this work, we introduce a learning-based calibration protocol and demonstrate
its experimental performance by calibrating an array of 16 flux-tunable
transmon qubits. To demonstrate the extensibility of our protocol, we simulate
the crosstalk matrix learning procedure for larger arrays of transmon qubits.
We observe an empirically linear scaling with system size, while maintaining a
median qubit frequency error below kHz