Abstract
Variational quantum algorithms (VQAs) are widely applied in the noisy intermediate-scale quantum era and are expected to demonstrate quantum advantage. However, training VQAs faces difficulties, one of which is the so-called barren plateaus (BP) phenomenon, where gradients of the cost function vanish exponentially with the number of qubits. In this paper, based on the basic idea of transfer learning, where knowledge of pre-solved tasks could be further used in a different but related work with the training efficiency improved, we report a parameter initialization method to mitigate BP. In the method, the quantum neural network, as well as the optimal parameters for the task with a small size, are transferred to tasks with larger sizes. Numerical simulations show that this method outperforms random initializations and could mitigate BP as well. This work provides a reference for mitigating BP, and therefore, VQAs could be applied to more practical problems.
Abstract (translated)
URL
https://arxiv.org/abs/2112.10952