GBC: Graph-based task orchestrator for research and development 

Fast and autonomous control of quantum computers requires carefully designed software frameworks, and the ability to dynamically respond to parameters deviations from design and drift in quantum processing unit (QPU). We present a powerful framework called Graph-Based Calibration (GBC), designed to help experimentalists manage complex experimental workflows. GBC encodes a series of calibration tasks as a Directed Acyclic Graph (DAG), where each node represents a task and each directed edge defines the flow of information. In the event of a task failure, GBC can trigger fallback nodes—alternative sequence of tasks capable of generating corrected parameters, by exploiting information from the preceding nodes. GBC also supports parallel execution, allowing for parallelisation of classical computation. We demonstrate that users can easily build, modify, sweep and visualise these graphs during daily research activities. In our production system, GBC enables autonomous recalibration for the quantum computer, maintaining high fidelities over time.