P3: ACE-QC Fully Classical and Efficient Autonomous Calibration of Quantum Computers

Members: Prof. Dr. Mariami Gachechiladze (TU Darmstadt), Prof. Dr. Tommaso Calarco (FZ Juelich), Khanh Uyen Giang (TU Darmstadt), Dr. Markus Heinrich (FZ Juelich), Prof. Dr. Felix Motzoi (FZ Juelich, University of Cologne), Jan Nöller (TU Darmstadt)

This project aims to develop a complete framework for fully classical and sample-efficient autonomous calibration of quantum computers. It combines a new certification protocol, Quantum System Quizzing (QSQ), with advanced quantum optimal control methods to enable reliable, scalable, and automated tuning of quantum hardware. QSQ provides sound, SPAM-robust, and black-box certification of quantum operations under minimal assumptions, drastically reducing measurement requirements compared to existing tools. Integrated into optimal control workflows such as the dCRAB algorithm, QSQ will deliver precise, sound figures of merit for gate calibration, enabling faster and more reliable optimization across different hardware platforms. The project also leverages machine learning, including AlphaZero-based reinforcement learning, for proactive calibration and experimental validation on superconducting and trapped-ion systems, paving the way for a new generation of self-calibrating quantum devices.