Automating full-device characterization and robust gate calibration in the era of QPU proliferation

The rapid advancement in quantum processor design and fabrication has driven the rise of quantum processing units (QPUs) globally, increasing the need for scalable and robust characterization and calibration of high-fidelity quantum gates. Meeting this challenge requires autonomous methods that can adaptively optimize performance across different devices.

This talk presents the latest progress in demonstrating such autonomous capabilities with transmon-based superconducting QPUs. We showcase a hybrid approach that integrates physics-based models with model-free techniques to achieve hands-off calibration reliably. By incorporating advanced error-handling protocols, we minimize manual intervention and enable system-wide optimization. Our results highlight improvements in the resilience and adaptability of the calibration process, laying the groundwork for extending these methods beyond transmon-based platforms. We will also discuss the outlook for applying this framework to other quantum architectures, aiming to standardize scalable quantum operation across diverse QPU technologies.