Preparing Quantum States by Measurement-feedback Control with Bayesian Optimization

TL;DR

This paper introduces a framework combining measurement-feedback control and Bayesian optimization to efficiently prepare ground states of many-body quantum systems, demonstrated on the Bose-Hubbard model.

Contribution

It presents a novel integration of machine learning with quantum control to optimize state preparation processes.

Findings

Bayesian optimization effectively determines measurement and feedback operators.

The method drives the system to low energy states with high probability.

Demonstrated on the Bose-Hubbard model with promising results.

Abstract

Preparation of quantum states is of vital importance for performing quantum computations and quantum simulations. In this work, we propose a general framework for preparing ground states of many-body systems by combining the measurement-feedback control process (MFCP) and the machine learning method. Using the Bayesian optimization (BO) strategy, the efficiency of determining the measurement and feedback operators in the MFCP is demonstrated. Taking the one dimensional Bose-Hubbard model as an example, we show that BO can generate optimal parameters, although constrained by the operator basis, which can drive the system to the low energy state with high probability in typical quantum trajectories.