Enhancing Variational Quantum Circuit Training: An Improved Neural Network Approach for Barren Plateau Mitigation

TL;DR

This paper presents an improved neural network method to mitigate barren plateaus in variational quantum circuit training, enhancing convergence speed and understanding of the loss landscape.

Contribution

The authors refine the neural network architecture for better barren plateau mitigation and extend its applicability to more general quantum input and circuit structures.

Findings

Improved convergence speed in VQC training.

Enhanced understanding of loss landscape smoothness.

Reduction in VQC expressibility improves trainability.

Abstract

Combining classical optimization with parameterized quantum circuit evaluation, variational quantum algorithms (VQAs) are among the most promising algorithms in near-term quantum computing. Similar to neural networks (NNs), VQAs iteratively update circuit parameters to optimize a cost function. However, the training of variational quantum circuits (VQCs) is susceptible to a phenomenon known as barren plateaus (BPs). Various methods have been proposed to mitigate this issue, such as using neural networks to generate VQC parameters. In this paper, we improve the NN-based BP mitigation approach by refining the neural network architecture and extend its applicability to a more generalized scenario that includes random quantum inputs and VQC structures. We evaluate the effectiveness of this approach by comparing the convergence speed before and after it is utilized. Furthermore, we give an explanation for the effectiveness of this method by utilizing a loss landscape visualization technique and the expressibility metric of VQC. The smoothness of the loss landscape offers an intuitive insight into the method's utility, while the reduction in expressibility accounts for the enhanced trainability. Our research highlights the universal applicability of the NN-based BP mitigation approach, underscoring its potential to drive progress in the development of VQAs across diverse domains.