Preparing quantum states by measurement-feedback control with Bayesian optimization

Abstract

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

Graphical abstract

Keywords

state preparation / feedback control / Bayesian optimization

References

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Conflicts of interest

There are no conflicts to declare.

Acknowledgements

Y.W. is supported by the National Program on Key Basic Research Project of China (Grant No. 2021YFA1400900) and the National Natural Science Foundation of China (Grant No. 12174236). P.Z. is partly supported by the Walter Burke Institute for Theoretical Physics at Caltech. J.Y. is supported by the National Natural Science Foundation of China (Grant No. 11904190) and Guangdong Basic and Applied Basic Research Foundation (Grant No. 2022B1515120021).

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