A modular approach to quantum control offers efficiency gains

Researchers at the University of Stuttgart have developed a modular framework using model predictive control (MPC) to address the computational complexity of quantum optimal control (QOC). The work introduces multiple MPC schemes, ranging from simple approaches to those with stability guarantees, to both improve the efficiency of pre-programmed quantum control and enhance robustness through feedback. This framework relies on repeatedly solving smaller QOC problems, a strategy stemming from MPC’s success in classical control systems. The team demonstrates these benefits with numerical results, benchmarking their methods against existing approaches to tackle the non-convex optimization inherent in quantum control. The modular framework achieves exponential convergence to a target quantum state, a result demonstrated through numerical simulations. This convergence, implicitly solving the original quantum optimal control (QOC) problem, distinguishes the approach from many existing methods that struggle with consistently reaching desired outcomes. Researchers Eya Guizani and Julian Berberich, of the University of Stuttgart, developed the system to address computational complexity inherent in QOC, a challenge amplified by the exponentially scaling dimensions of quantum systems. Model predictive control (MPC) forms the foundation of this…