🤖 AI Summary
Variational quantum circuits (VQCs) suffer from barren plateaus (BPs)—exponential decay of gradient variance with qubit count or circuit depth—rendering gradient-based optimization ineffective in large-scale training. This work systematically analyzes the origins of BPs and proposes the first unified classification framework covering five mitigation strategies: parameterization design, layer-structure constraints, loss-function construction, initialization optimization, and gradient preprocessing. Leveraging random unitary matrix theory, gradient sensitivity analysis, and optimization theory, we conduct a cross-method comparative study. Relative to existing surveys, our work explicitly identifies critical gaps—including hardware-aware mitigation and hybrid non-gradient optimization—and constructs an interpretable, scalable knowledge graph. The framework provides both theoretical foundations and practical engineering guidelines for robust training of large-scale VQCs.
📝 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.