Model predictive control for robust quantum state preparation

A critical engineering challenge in quantum computing is the accurate control of quantum dynamics. To design classical control fields sufficient for high-fidelity quantum processes, model-based numerical methods for quantum optimal control are essential.  These open-loop control strategies are known to be limited by systematic modeling errors and noise. Closed-loop strategies provide critical performance enhancements by bringing experimental data to task. Model predictive control (MPC) is a model-based feedback strategy complimentary to existing model-free feedback strategies used for quantum optimal control. MPC naturally accommodates experimental constraints and is robust in the presence of systematic modeling errors and noise. We show how MPC can be used to generate practical optimal control sequences in representative examples of quantum state preparation and, by extension, quantum gate synthesis. Our examples showcase why MPC makes a welcome addition to the quantum engineer's toolbox.