Automated and parallelized tune-up of a 17-qubit QPU with a scalable software framework

As quantum processing units (QPUs) grow in scale and complexity, tuning them to their optimal operating points to sustain high-fidelity operation becomes a more and more challenging task. Tackling the various quantum engineering difficulties associated with it requires software frameworks suitable for automation, providing both high performance and transparent user access on multiple levels of abstraction.

Here we show how LabOne Q, our open-source control software framework, addresses these challenges in the automated tune-up of a 17-qubit QPU located at the Walther-Meißner-Institute. The framework enables parallelization of tune-up experiments and covers data analysis, data storage, and parameter updating. The user is provided with Python interfaces at the workflow-, gate-, and pulse-level, covering all abstraction levels needed in QPU development. We also show performance improvements achieved in the core software component implemented in Rust, reducing overall compilation time by typically more than one order of magnitude. Combining open multi-level access and a sustainable performance, LabOne Q provides a scalable architecture for the next major milestones on the path towards fault-tolerant quantum computing.