Learning quantum operator by quantum adiabatic computation

In this article, we introduce the quantum adiabatic computation to the research field of quantum operator learning. Compared with existing conventional optimization approaches, the adiabatic algorithm ensures to reach the global optimal solution, and thus avoids the local minimum problem. The performance of the experiments on two tasks indicates the feasibility and potentiality of this novel method. We firmly believe that the quantum adiabatic computation can be applied to other tasks of machine learning.