  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 optimized through gradient-based
approaches. However, 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
exponential gradient vanishing, 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. Besides, we propose a new taxonomy to categorize most existing
mitigation strategies. At last, we provide insightful discussion for future
directions of BPs.
