AI-Driven Reinforcement Learning Framework for Multi-Parameter Calibration of Superconducting QPUs on the QC-Test Space

Achieving high-fidelity operations in superconducting quantum processors requires efficient calibration of an increasingly large parameter space as system scale grows. On the QC-Test Space, we develop an AI-assisted calibration framework that integrates reinforcement learning (RL) with a DGX-Q accelerated control environment through a tightly coupled OPX1000–GH200 stack via OPNIC. The RL agent autonomously tunes both single-qubit (X, SX) and two-qubit (CZ) gates with more than two control parameters, iterating through action–reward cycles to maximize state fidelity derived from Pauli-based observables. By exploring multiple RL architectures and reward functions, we analyze convergence behavior, learning stability, and optimization efficiency compared to a traditional calibration-tree baseline. This hybrid quantum–classical workflow enables continuous retuning, dynamic error compensation, and data-driven adaptation as device conditions evolve. The collected calibration data further enhance our digital twin model, guiding hardware design and suggesting improved control strategies and sequences. Our approach establishes a new, scalable and intelligent paradigm for automated superconducting QPU calibration and performance optimization.