Investigating and Mitigating Barren Plateaus in Variational Quantum Circuits: A Survey

TL;DR

This survey reviews recent research on barren plateaus in variational quantum circuits, categorizing mitigation strategies and discussing future research directions to address the scalability challenges in quantum machine learning.

Contribution

The paper provides a comprehensive taxonomy of barren plateau mitigation strategies and compares recent surveys, offering insights into future research directions.

Findings

Most mitigation strategies fall into five categories.

Gradient variance diminishes exponentially with qubits.

Future directions include novel mitigation techniques.

Abstract

In recent years, variational quantum circuits (VQCs) have been widely explored to advance quantum circuits against classic models on various domains, such as quantum chemistry and quantum machine learning. Similar to classic machine-learning models, VQCs can be trained through various optimization approaches, such as gradient-based or gradient-free methods. However, when employing gradient-based methods, the gradient variance of VQCs may dramatically vanish as the number of qubits or layers increases. This issue, a.k.a. Barren Plateaus (BPs), seriously hinders the scaling of VQCs on large datasets. To mitigate the barren plateaus, extensive efforts have been devoted to tackling this issue through diverse strategies. In this survey, we conduct a systematic literature review of recent works from both investigation and mitigation perspectives. Furthermore, we propose a new taxonomy to categorize most existing mitigation strategies into five groups and introduce them in detail. Also, we compare the concurrent survey papers about BPs. Finally, we provide insightful discussion on future directions for BPs.