High-dimensional multi-fidelity Bayesian optimization for quantum control

High-dimensional multi-fidelity Bayesian optimization for quantum control

Blog Article

We present the first multi-fidelity Bayesian optimization (BO) approach for solving gruvi golden lager inverse problems in the quantum control of prototypical quantum systems.Our approach automatically constructs time-dependent control fields that enable transitions between initial and desired final quantum states.Most importantly, our BO approach gives impressive performance in constructing time-dependent control fields, even for cases that are difficult to converge with existing gradient-based approaches.

We provide detailed descriptions of our machine learning methods as well as performance metrics for a variety of machine learning algorithms.Taken together, our here results demonstrate that BO is a promising approach to efficiently and autonomously design control fields in general quantum dynamical systems.