Learning-based Calibration of Flux Crosstalk in Transmon Qubit Arrays

Abstract

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 $300$ kHz.

20 pages, 16 figures; replaced with published version

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Cited by in corpus (7)

• Probing entanglement across the energy spectrum of a hard-core Bose-Hubbard lattice
• Implementing a synthetic magnetic vector potential in a 2D superconducting qubit array
• Signal crosstalk in a flip-chip quantum processor
• Tantalum airbridges for scalable superconducting quantum processors
• Flux-Trapping Fluxonium Qubit
• Simultaneous sweet-spot locking of gradiometric fluxonium qubits
• Attention to Quantum Complexity