Mitigating Barren Plateaus with Transfer-learning-inspired Parameter Initializations

TL;DR

This paper introduces a transfer-learning-inspired parameter initialization method for variational quantum algorithms that effectively mitigates barren plateaus, enhancing training efficiency and enabling practical quantum applications.

Contribution

The paper proposes a novel initialization technique based on transfer learning to reduce barren plateaus in VQAs, improving training efficiency for larger quantum systems.

Findings

Mitigates barren plateau phenomenon in VQAs

Enhances training efficiency for larger quantum tasks

Numerical simulations validate effectiveness

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 cost functions vanish exponentially with the number of qubits. In this paper, inspired by transfer learning, where knowledge of pre-solved tasks could be further used in a different but related work with training efficiency improved, we report a parameter initialization method to mitigate BP. In the method, a small-sized task is solved with a VQA. Then the ansatz and its optimum parameters are transferred to tasks with larger sizes. Numerical simulations show that this method could mitigate BP and improve training efficiency. A brief discussion on how this method can work well is also provided. This work provides a reference for mitigating BP, and therefore, VQAs could be applied to more practical problems.