Preparing quantum states by measurement-feedback control with Bayesian optimization

The preparation of quantum states is crucial for enabling computations and simulations. In this work, we present a general framework preparing ground many-body systems by combining the measurement-feedback control process (MFCP) with machine learning techniques. Specifically, employ Bayesian optimization (BO) to enhance efficiency determining measurement feedback operators within MFCP. As an illustration, study state one-dimensional Bose–Hubbard model. Through BO, are able identify optimal parameters that can effectively drive system towards low-energy high probability across various trajectories. Our results open up new directions further exploration development advanced strategies