Quantitative Rules for Parallel Calibration and Drift-Resilient Maintenance of Large-Scale Superconducting Quantum Processors
The authors developed quantitative rules to automate calibration for large superconducting quantum processors by addressing crosstalk from parallel operations and temporal drift of control parameters. They found optimal parallel densities for single-qubit and two-qubit gates, and established a priority system to refresh control parameters based on their impact on gate errors. This allows for automated maintenance that significantly reduces qubit errors.